Podcasts about Codex

“It would be sad if local models were not an option and there were only proprietary models. It's good to have alternatives. Competition is good for business.”— Sebastian Raschka, on open-weight AIKimi K3's weights landed about an hour before Hugo Bowne-Anderson and Sebastian Raschka went live. Sebastian had already updated his architecture diagram. That speed captures his approach to the current model wave: wait until the weights exist, run the model in the harness where it will actually work, then inspect the architecture closely enough to understand what changed.The conversation arrived during a larger fight over who supplies the models underneath global software. Three days earlier, twenty-five companies including NVIDIA, Meta, Microsoft, Hugging Face, and IBM published Open Weights and American AI Leadership. Their argument closely matches Sebastian's practical case for local models: open weights create competition, reduce dependence on a single provider, and let organizations choose a model at the right capability and cost.Update: Four days after we recorded, DeepSeek released V4 Flash 0731, a re-post-trained API model for agentic coding. Developers are already reporting that it can debug multi-project codebases and stay on task across very long contexts.You can find the full episode on Spotify, Apple Podcasts, and YouTube.

Sun, 02 Aug 2026 15:00:00 GMT http://relay.fm/mpu/860 http://relay.fm/mpu/860 The Friday Incident with Merlin Mann 860 David Sparks and Stephen Robles Merlin Mann returns to Mac Power Users 16 years after being our very first guest to break down the AI system he's built — Codex, "triangulation," 31 cross-indexed data sources, and the infamous "Friday Incident." Merlin Mann returns to Mac Power Users 16 years after being our very first guest to break down the AI system he's built — Codex, "triangulation," 31 cross-indexed data sources, and the infamous "Friday Incident." clean 5412 Merlin Mann returns to Mac Power Users 16 years after being our very first guest to break down the AI system he's built — Codex, "triangulation," 31 cross-indexed data sources, and the infamous "Friday Incident." This episode of Mac Power Users is sponsored by: Ecamm: Powerful live streaming platform for Mac. Backblaze: Unlimited, easy data protection. Try it for free today and get 20% off with code mpu20 1Password: Never forget a password again. Guest Starring: Merlin Mann Links and Show Notes: Sign up for the MPU email newsletter and join the MPU forums. You can watch the podcast over on YouTube. Credits The Mac Power Users Stephen Robles David Sparks The Editor Jim Metzendorf The Fixer Kerry Provanzano More Power Users: Ad-free episodes with regular bonus segments Submit Feedback Mac Power Users #23: Workflows with Merlin Mann Merlin's Triangulation Example Merlin's AI Language Usage Document Infuse - Video Player for Apple TV, iPhone, iPad, Mac & Vision Relax with Coax Typora — simple yet powerful Markdown reader wisdom/wisdom.md at master · merlinmann/wisdom This Conversation Will Change How You Think About Trauma — The Ezra Klein Show The Body Keeps the Score: Brain, Mind, and Body in the Healing of Trauma The Other Side of Sadness: What the New Science of Bereavement Tells Us About Life After Loss When Breath Becomes Air

Sun, 02 Aug 2026 15:00:00 GMT http://relay.fm/mpu/860 http://relay.fm/mpu/860 David Sparks and Stephen Robles Merlin Mann returns to Mac Power Users 16 years after being our very first guest to break down the AI system he's built — Codex, "triangulation," 31 cross-indexed data sources, and the infamous "Friday Incident." Merlin Mann returns to Mac Power Users 16 years after being our very first guest to break down the AI system he's built — Codex, "triangulation," 31 cross-indexed data sources, and the infamous "Friday Incident." clean 5412 Merlin Mann returns to Mac Power Users 16 years after being our very first guest to break down the AI system he's built — Codex, "triangulation," 31 cross-indexed data sources, and the infamous "Friday Incident." This episode of Mac Power Users is sponsored by: Ecamm: Powerful live streaming platform for Mac. Backblaze: Unlimited, easy data protection. Try it for free today and get 20% off with code mpu20 1Password: Never forget a password again. Guest Starring: Merlin Mann Links and Show Notes: Sign up for the MPU email newsletter and join the MPU forums. You can watch the podcast over on YouTube. Credits The Mac Power Users Stephen Robles David Sparks The Editor Jim Metzendorf The Fixer Kerry Provanzano More Power Users: Ad-free episodes with regular bonus segments Submit Feedback Mac Power Users #23: Workflows with Merlin Mann Merlin's Triangulation Example Merlin's AI Language Usage Document Infuse - Video Player for Apple TV, iPhone, iPad, Mac & Vision Relax with Coax Typora — simple yet powerful Markdown reader wisdom/wisdom.md at master · merlinmann/wisdom This Conversation Will Change How You Think About Trauma — The Ezra Klein Show The Body Keeps the Score: Brain, Mind, and Body in the Healing of Trauma The Other Side of Sadness: What the New Science of Bereavement Tells Us About Life After Loss When Breath Becomes Air

Missy, a former marketing professional turned cryptid researcher and author, joins this episode to share her 2018 encounter with a pale, emaciated humanoid creature often called a "pale crawler" or "rake" that she found crouched in the middle of a rural Mississippi road. Missy walks through the encounter itself, the strange aftermath that sent her into therapy, and the years-long investigation that followed, including her deep dive into a viral trail cam photo that mirrored her own sighting and the surprising results of a professional photo forensics analysis.This episode is presented by Codega's Codex of Curiosities, a weekly podcast exploring paranormal encounters, UAP phenomena, cryptid sightings, and high-strangeness stories from guests across the world. Find more episodes and join the community at the links below.Topics Discussed:Missy's 2018 roadside encounter with a pale, hairless, emaciated humanoid creature and the physical shock response that followedA strange lingering "Old Spice" cologne smell reported by multiple witnesses of similar creatures, including one located near Missy's own encounterThe viral 2010 trail cam photo from Louisiana, its disputed "debunking," and what an AI-based photo forensics analysis actually foundA local news station's shifting story about who submitted that photo, and questions about a possible coordinated effort to discredit itMultiple independent video sightings of similar pale, gangly humanoid creatures — from a Mississippi porch to an ice cave overseasWhy therapists and clinicians are reportedly seeing a rise in patients, including military personnel, describing encounters with these entitiesLinks

Google didn't ship its big model, but they shipped a TON of new useful AI you can use today. And Google wasn't the only company updating their features behind the scenes. Replit is bringin vibe designing, ChatGPT got a lot more useful on the web, and Meta is changing from chatbot to agent. We'll get you caught up quickly. Chrome adds Some Gemini Spark, Replit Design makes impact, Buzz brings AI Agent Teamwork and 7 more AI Features you Should use Today -- an Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Replit Design Suite Launches With Free MobbinChatGPT Chrome Extension Adds YouTube SummarizationChatGPT Side Chat Integrates Tabs and Highlighted TextMeta AI Rolls Out Recurring Agent TasksGoogle Gemini Generates Images in Google DocsGemini AI Summarizes Comments, Edits in DocsGoogle Gemini Spark Agent Arrives in ChromeChrome Agent Uses Saved Accounts and PasswordsGoogle Lyria 3.5 Music Model ReleasedBuzz by Block Unites Team and Agent CollaborationTimestamps:00:00 Recent AI updates and developments05:01 Creating with Replit and AI models09:42 Real-time research tracking benefits10:34 Meta AI new recurring features13:35 New features of Meta AI17:53 Google Spark integrates with Chrome22:09 Google DeepMind's new music model25:25 Buzz from Block messaging tool29:42 Building a collaborative platform31:23 AI feature updates recapKeywords: Gemini Spark, Google Chrome AI integration, Google Docs AI features, AI image generation, Gemini in Docs, ChatGPT Chrome extension, YouTube video summarization, OpenAI ChatGPT update, Codex, Vibe design, Replit design suite, Mobbin integration, AI reference library, Design export automation, Project management AI, Figma competitor, Replit creative tools, Meta AI, Muse Spark 1.1, Agentic model, Recurring AI tasks, AI scheduling, Daily briefings, AI productivity tools, Google Lyria 3.5, AI music model, Flow Music, Suno, Yudio, AI generated lyrics, Vocal delivery in AI music, Licensing in AI music, Buzz collaboration platform, Block, Square, AI agent teamwork, Slack-like AI platform, Open source collaboration, Agent governance, Cryptographic identity, Agentic browser, Automated web errands, Chrome passwords integration, Google Drive data access, Multi-agent collaboration, Research automation, Enterprise AI workflow, AI productivity boost.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Send us Fan MailTitle: What's New in Cloud FinOps - June 2026Hosts: Frank Contrepois and SteveOSummaryIn this episode, Frank and SteveO navigate through the latest cloud offerings and AI advancements, revealing how these updates impact performance, costs, and operational strategies across Azure, AWS, and AI applications. Stay tuned for insights into new VM generations, cost management tools, and cutting-edge AI features.Key Topics:Azure's new Cobalt 200V and 100V VMs deliver up to 50% better CPU performanceIntroduction of AWS's Metal 48XL/96XL and enhanced EC2 instances with sixth-generation Intel XeonLatest AWS Graviton 5 processors offering up to 25% better compute performanceEnhanced Amazon EC2 G7 instances powered by Nvidia RTX Pro 4500AWS Cost Management updates, including automatic cost anomaly investigations and new billing toolsAWS's support for region-agnostic throughput reservations in AzureThe rise of AI and automation in cost optimization: new tools, models, and use casesCloud vendors' announcements on energy-efficient storage, reserved pricing, and billing analysis toolsTimestamps:00:00 - Cloud news roundup: performance boosts in Azure VM series00:20 - Azure's new Cobalt 200V VMs: performance and AI workload optimization01:32 - AWS launches Metal 48XL/96XL: CPU advancements and network enhancements02:50 - Introduction of AWS M9G/M9GD instances with AWS Graviton 5 processors04:36 - AWS's latest EC2 G7 instances with Nvidia RTX GPUs for AI and visual workloads05:43 - Cost efficiency improvements with new pricing models and snapshot billing09:28 - Redshift advances with manual snapshot cost reductions10:12 - AI models on Bedrock: GPT 5.5, Codex, and OpenAI integrations12:24 - Innovations in cloud billing: cost explorer, cost anomaly detection, and billing account tools13:00 - Cost & Usage Report 2.0 enhances S3, Athena, and Redshift integration14:23 - Google Cloud billing updates: report export improvements and new filtering options15:19 - AWS's right-sizing and resource optimization enhancements16:12 - Cost explorer AI integrations and automated cost investigations17:10 - New AI-powered tools for cost anomaly root-cause analysis18:16 - Multi-project billing views and resource management in AWS19:04 - Advanced export configurations to streamline billing data handling20:06 - Google Cloud's spot VM real-time availability features21:36 - Enhanced tagging, resource management, and API capabilities in AWS and Google Cloud24:11 - Azure's VM retirements, storage, and reservation updates26:15 - Redshift's new upfront pricing options for reserved instances27:35 - Global provisioned throughput reservations now regional in Azure for flexibility28:44 - Storage charges optimizations and vector query cost reductions on S330:10 - Using finops.frankcontrepois.com for AI-driven FinOps34:08 - The importance of separating AI from automation in cloud efficiency strategies36:17 - Resources like the Phoenix Project and The Goal for understanding process optimization and AI impact37:34 - Support for new resource types and idle recommendation expansion in AWS Compute Optimizer38:50 - Cost and performance insights into specific resource wastage39:16 - AWS's State of Cost Efficiency Report: benchmarking and industry insights40:52 - AWS FinOps agents preview: automated cost and anomaly management workflows42:23 - Programmatic savings plan management and AWS workload optimization43:47 - Cost attribution and telemetry for large language models (LLMs) on Bedrock44:10 - AWS WAF's new AI traffic monetization capabilities for API access control45:45 - Azure Cosmos DB's new cost estimator tool for pre-provisioning modeling46:52 - Top three news picks: upcoming cloud innovations and AI advances47:35 - The growing role of FinOps and AI operational tools in cloud cost managementResources:FinOps toolThe Phoenix ProjectThe Goal by Eliyahu M. GoldrattConnect with the Hosts:Frank - LinkedInSteveO - LinkedIn

Agradece a este podcast tantas horas de entretenimiento y disfruta de episodios exclusivos como éste. ¡Apóyale en iVoox! Nuestra siguiente indagación la realizamos en la célebra Torrebombita, o Torre Na Juana, un enclave enigmático con todos los alicientes para una salida codexiana, misterios, sucesos luctuosos, resultados de otros equipos de indagación. Sumergete con nosotros en esta trepidante aventura. Nos puedes encontrar también en Youtube, Tik Tok y en el grupo de Telegram Codex más allá del misterio. Ensayos y novelas publicadas: ENTRE HISTORIAS EXTRAÑAS. Amazon CAZADORES DE MISTERIOS. Ediciones Cydonia CAZADORES DE MISTERIOS 2. Editorial Guante Blanco CAZADORES DE MISTERIOS 3. Amazon CAZAVAMPIROS. MITO Y REALIDAD. Colección Biblioteca del Misterio de ediciones Oblicuas ENIGMA VALLÉS. Bohodón ediciones ARCA SACRARIUM Puedes hacerte mecenas en iVoox o apoyarnos si quieres a través del enlace de paypal https://www.paypal.com/ncp/payment/UL83BSW4GB99W o a través de https://www.paypal.me/CodexMisterioPodcastEscucha este episodio completo y accede a todo el contenido exclusivo de CODEX podcast. Descubre antes que nadie los nuevos episodios, y participa en la comunidad exclusiva de oyentes en https://go.ivoox.com/sq/130420

VOV1 - Chiều 31/7, Bộ Y tế tổ chức cuộc họp lấy ý kiến vào Dự thảo Luật An toàn thực phẩm (sửa đổi). Dự thảo này đang được Bộ Y tế tiếp tục hoàn thiện với quyết tâm đổi mới phương thức quản lý, tăng cường hậu kiểm, ứng dụng chuyển đổi số và kiểm soát nguy cơ theo toàn bộ chuỗi cung ứng thực phẩm.Dự thảo Luật An toàn thực phẩm (sửa đổi) dự kiến gồm 8 chương, 71 điều và sẽ tiếp tục được hoàn thiện trong thời gian tới. Ông Chu Quốc Thịnh, Cục trưởng Cục An toàn thực phẩm (Bộ Y tế) cho biết, Dự thảo Luật tập trung thực hiện 4 nhóm chính sách trọng tâm nhằm đổi mới toàn diện công tác quản lý, trong đó có tư duy quản lý an toàn thực phẩm theo chuỗi cung ứng.“Để quản lý theo chuỗi cung ứng, giải pháp thực hiện là phải kiểm soát khâu sản xuất ban đầu. Chúng tôi quy định 2 nội dung chính: Tiếp tục kiểm soát điều kiện kinh doanh chuỗi sản xuất ban đầu là trồng trọt, thu hái, giết mổ, khai thác, đánh bắt thủy sản; Ngoài ra, để kiểm soát tận gốc, chúng tôi quy định các giới hạn tồn dư thuốc thú y, thuốc bảo vệ thực vật, tác nhân gây ô nhiễm môi trường. Nội dung này, chúng tôi sẽ tham khảo quy định của CODEX để quy định nội dung về giới hạn các chất."- Ông Chu Quốc Thịnh cho biết thêm.Bộ Y tế tổ chức cuộc họp lấy ý kiến các đơn vị liên quan vào Dự thảo Luật An toàn thực phẩm (sửa đổi)- Ảnh Trần Minh

Hey, it's Alex (yeah, I'm finally back from my vacation!) What a freaking week to come back to! Just after our last episode was published, Anthropic releases Opus 5, Jensen joins X and drops the “Open Weights & AI Leadership” open letter, Kimi K3 is released the following Monday beating expectations, and then the AI hack (OpenAI model breaking sandbox and infiltrating HuggingFace) is on everyone's mind, another Open Letter, this time from over 1K employees inside the frontier AI companies all talk about pacing the pace of frontier AI development. We played with Opus 5 and Kimi K3, and had the great pleasure to chat with friends of the pod Elie Bakouch (Prime Intellect) and Philip Kiely (BaseTen) about this important open weights release, then covered our general thoughts on Opus 5, and made order of all the different open letters that came out this week. Finally we chatted with Max from Pangram about the next version of AI writing detection (their biggest yet) and finished with Zuckerbergs (also on X! what's going on with everyone joining X) op-ed on the vision of personal superintelligence for everyone. Let's dive into this (as always, all the links and sources at the end, please don't forget to sub to our podcast on your favorite podcast app!) Open Weights AIKimi K3 the king of open weights - 2.8T chonker MoE near frontier model (X, HF, Blog, Tech report)This has got to be the biggest news of this week, and maybe the open weights AI news since GLM 5.2. MoonShot came back with Kimi K3, and we haven't seen any models quite this large in the open. Even Grok 4.5 is around 1.5T, this model is nearly 2x the size. Coming in at close to 3T parameters (and 2.5terabytes of weights at MXFP4 format), this model comes in very close to frontier! This was such an important release that I invited 2 friends of the pod, Elie Bakouch (prev HuggingFace, now Prime Intellect) and Philip Kiely (Author of Inference Engineering book, BaseTen) to dive deep into what makes this special! Elie's take, from reading the tech report, there's no single secret sauce, it's a combination of already available in the open techniques. Like KDA (Kimi Delta Attention) that has been out for a while, attention residuals, NVIDIA's latent MoEs. The highlight for Elie was the scaling work they did that reported a 2.5x scaling efficiency over Kimi K2.5 (2.5 performance at the same compute)! They also skipped RoPE entirely in favor of NoPE (the report calls it No Positional Encoding) for long context.Serving 1.4TB on eight GB300s (Baseten blog)Philip's team at Baseten was a day-zero provider (we're still working on bringing this model to CW Inference, stay tuned!) so I invited him to tell us behind the scenes of hosting this beast. Philip said that just loading the weights takes about 1.5TB!! of VRAM, and that's before the KV cache allocation + 1M token windows, so they're serving it on 8 GB300s where NVL72 . Baseten worked with the vLLM and SGLang teams on kernels and he also said they contributed patches back upstream! The model was trained with MXFP4, which, unlike Nvidia's own NVFP4 is a more standard format per Philip. I enjoyed his deep dive analysis into the differences, but because of this and because they trained the model with quantization awareness, it's “only” 1.5TB vs the would-be 5-6 TB if that this model in FP16 would demand. One of the more favorite nerd snipes moments, Philip pointed out that his colleague discovered that with over 99% of the usage being cached (think harnesses that send millions of the same cached tokens back and forth), tokenization actually starts to become a bottleneck. So they released a custom “basetenkenizer” that reduces the latency to serve the first token significantly! Great job!The harness in question is very importantOne important callout with 2 evidence pieces - the way you inference this model really matters. Kimi trained K3 with preserving thinking history, so when your harness uses it, it must send back the full thinking and tool use into the API to get the best next response. If your harness strips that out, you're not getting the most intelligence out of Kimi (shoutout to Niels from HF team for pointing this out). Additionally, the Composio folks, tested K3 on 3 harnesses, Kimi Code, Hermes and Claude Code. The difference in outcome was negligible, but the different in cost and number of tokens is definitely surprising! Claude Code (as a harness only) took 9x more Kimi tokens to get the same responses! This is also why Kimi Vendor Verified exists, their own held back benchmark of how well model providers serve Kimi across different quantization, tokenizer and KV cache settings. Benchmarks and the license! Ok let's start with the ugly... this isn't MIT, not remotely. This model is suspiciously served by all providers with exactly the same price (check OpenRouter) and requires inference companies to sign a contract with Kimi (I've no internal knowledge of this except that CW folks are working on it). Not something I particularly like, but hey... we're still advancing the frontier here! Speaking of frontier, this model approaches the frontier very closely. On DeepSWE, K3 sits just behind Fable 5 and GPT-5.6 Sol at 67%, beating GPT-5.5 & Opus 4.8. On Terminal-Bench 2.1 it takes second place behind GPT 5.6 Sol! It's 4th overall on Agentic Arena, with frontend design being genuinely good across the board - 1st on Design Arena

Agents are getting more powerful by the day. And most workflows, outputs and human capabilities can't keep up. Is that a problem or opportunity? Before you answer that question, though, keep this in mind. Agents are *literally* about to become 20X faster overnight. Let's unpack what that means. Faster AI Agents, Fewer Human Coworkers: The Overly Productive Future of Managing Agents? -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Managing Dozens of Productive AI AgentsOpenAI Cerebras: 20x Faster Agent ModelsImpact of AI Agents on Human CoworkersAgent-Driven Workflows vs. Human CollaborationIncreasing Agent Reliance and Fading MentorshipAccidental Deskilling and Compression TaxProtecting Human Judgment and Learning HandoffsExpert-Driven Loops in AI WorkflowsMonthly Rebuilding of AI Strategies and ProcessesMiddle Management Evolution in AI Native CompaniesTimestamps:00:00 Future of AI and Work Dynamics05:25 Advancements in AI and productivity tools09:51 Growing your business with AI13:52 AI productivity and collaboration shifts15:08 Improving AI processing speed18:53 Using AI agents for delegation24:46 Discussing AI-related work challenges28:16 Ensuring accountability and communication30:39 Adapting to rapid digital change32:35 Show outro and newsletter sign-upKeywords: AI agents, faster AI models, OpenAI, Cerebras chip, 20x speed increase, automated workflows, agent management, solo agent supervisor, generative AI, knowledge work automation, agent-powered productivity, parallel machine teams, inference speed, productivity acceleration, Codex, Cloud Code, Google Gemini, Cloud Cowork, Copilot, recursive self improvement, expert-driven loops, human handoffs, deskilling, mentorship loss, AI native workplace, workplace automation, transactional work, productivity roadblocks, accidental deskilling, agent bun sandwich, compression tax, human in the loop, expert collaboration, agent trust, AI decision making, domain expertise, rapid workflow rebuilding, unlearning processes, organizational adaptation, enterprise AI adoption, future of work, middle management AI, AI-powered teamwork, human-agent collaboration, manager-agent ratios, personalized agent output, multi-agent coordination, skillset sharing, intentional automation, productivity strategySend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

If someone handed your competitor every prompt, workflow and AI system you use today, would you still have an advantage?   That is the question that opens this live roundtable, recorded in a sealed room at the first Founder AI Studio Live in Toronto. Ten women who are not just using AI but building with it: intellectual property strategists, AI educators, investors, community builders, and operators who have built and exited companies.   No keynote. No panel. Just founders comparing notes from the front line.   IP lawyer Andrea Bolden takes on the part most founders get wrong: whether your prompts, workflows and custom GPTs count as intellectual property, why switching off the training toggle is not the protection you think it is, why people trademark a name while giving away the entire body of work underneath it, and which AI tools will actually indemnify you if you get sued.   The room also gets into what happens to knowledge businesses when knowledge stops being scarce, why AI-native builds beat AI retrofits, the environmental cost nobody wants to raise, the risk of putting your child's face into these systems, and the six companies capturing most of the value from all of it.   It ends with a lightning round on the one tool each founder actually uses.   CHAPTERS 00:00 Context: what this room was 01:35 Not a keynote, not a panel 02:16 The question: would you still have an advantage? 03:09 Forty-five products that all do the same thing 05:46 Being the tastemaker 07:54 AI as a democratizing force 10:03 AI-native builds vs. AI retrofits 11:00 The contractor line item that changed everything 14:24 From "done for you" to "done for you-ish" 16:01 Building instead of talking about building 18:35 The real unlock is the speed of learning 23:00 If you sell knowledge, what are you selling now? 24:42 Dyslexia, ADHD and AI as a translation layer 28:22 Are your prompts and GPTs your IP? 29:28 The training toggle myth 30:30 Trademarking the name, losing the body of work 32:15 Tim Ferriss and what is already public 34:08 Can AI legally be an author? 35:32 Which AI tools will indemnify you 37:03 Writing a book with AI 39:27 Just because AI can, should it? 43:14 Share the why, not the whole how 44:00 The one thing to do next week 45:22 Canada's AI strategy and where the money is going 47:32 Environmental impact and who absorbs it 49:55 Your child's face inside an LLM 50:52 Sycophancy and outsourced thinking 51:58 What work do we reserve for humans? 54:16 Six companies and who holds the stake 56:14 Lightning round: the one tool 1:01:27 The advantage was never the tool   TOOLS MENTIONED Claude / Claude Code / Claude Cowork, ChatGPT, Microsoft Copilot, Lovable, Gamma, Codex, DeepSeek, Kling, Go High Level   LINKS Next Founder AI Studio Live   IN THE ROOM Host: Monique Bryan, Brand Authority Strategist Featuring: Andrea Bolden, IP and business lawyer   PARTNERS Captured by Perspective Studio Productions Presented in partnership with BDC Capital, Inclusive Entrepreneurship   Founder AI Studio Live is an invite-only working session for established women founders already building with AI. The room is the asset.   #AIforFounders #IntellectualProperty #WomenInAI Who Knows You is hosted by Monique Bryan, brand authority strategist and built for founders, operators, and experts who are doing real work and ready to be picked for it. Take the AI Visibility Audit to find out where your positioning is breaking down and what to fix: [RUN YOUR AUDIT] Connect with Monique:Before we build, let us talk. https://moniquebryan.com/book/ - Website: moniquebryan.com LinkedIn: Monique Bryan Instagram: @moniquebryan

Milk already feels tight across much of the U.S. That could be the setup for a perfect storm. Summer heat, warm nights, wildfire smoke and plant disruptions have pressured milk production and moved milk into unexpected places. Now, Class I bottlers are preparing for schools to reopen just as cheese plants, protein beverage manufacturers and other processors compete for the same milk solids. In this episode of The Milk Check, guest host Josh White and the Jacoby team break down what could make August, September and October especially interesting for dairy markets. We cover: How heat, smoke and limited nighttime cooling affected milk production Why school bottling demand could tighten the market further How the cybersecurity disruption temporarily increased condensed skim availability How conflict, Red Sea risk and higher freight costs are complicating dairy exports The dairy market is not moving in a straight line. But competition for milk solids is building, and the next few months could determine which product sectors get the milk they need. Listen to The Milk Check episode 103: The Perfect Storm for Milk Solids. Also available on: Amazon Music, Apple Podcasts, Spotify, and YouTube. Got questions? We'd love to hear them. Submit below, and we might answer it on the show. Ask The Milk Check Transcript: [Opening commercial] Josh White: [00:00:00] Coming up on the Milk Check. Jennifer S. Kuo: The Red Sea seems to be an issue now as well. Tyler Jokerst: Yeah. Josh, if the Houthis are getting involved, when you’re looking at Yemen that’s a direct effect on the Red Sea, which is the other half of that peninsula . And then it starts to limit the only access point that you can have into the Red Sea being through the Suez Canal. Josh White: ​In absence of our fearless leader, Ted we invite our audience to join us for one of our bi-weekly commercial meetings, where our group gets together and breaks down the market based on our individual disciplines. Today’s group is a fairly large one but we have members representing our fluid team, our ultrafiltered and cream team, cheese, butterfat, milk powder, and whey, which makes up our trading group.  We’re in the dog days of summer right now, schools are out, families are traveling. There’s people out of the office not making decisions. That’s happening both in the U.S. and in Europe. Let’s touch on current market, climate, what we’re experiencing, and then what we’re paying attention to or looking out for in 30 days time. Let’s start with where we’re at on the milk side of things. Greg, both you and Jared, have experienced a little turbulence over the past week or so with some milk movements. We’re just coming out of a big heat stretch. We’re on the cusp of the South starting to refill its bottling pipelines. What are you feeling and seeing right now, Greg? Greg Scheer: We’ve had some plant closures that have pushed milk around the Mideast, the Northeast, and, around the country. We have had a week or two of that. The first heat wave, back several weeks ago, hit the cows harder than expected, and I’m wondering if maybe that’s the age of the herd is a little older that maybe it hit them a little more. Usually, you have a heat wave, the cows recover some. Normal summer, they get another heat wave, and then, it hits them a little harder the second time or third time. Seems like the first heat wave hit the cows a little harder. I think production’s down just a little bit more than we expected or earlier than maybe a normal summer. Other than plant problems that push milk around, it feels tight. We get to next month, schools start up again or are about to, and bottlers start putting milk into the bottle for schools, then it’s gonna get really tight and could be tight through September, October when maybe production comes back a little bit and the pipeline gets filled, and then it levels off demand a little bit. It feels tight other than plant closures. It’s gonna get really tight in a month. And, we’ll see where it goes. But production does seems like it was hit harder. I’m just wondering if maybe the age of the herd may have a little bit to do with it. Josh White: It was also pretty warm nights for the Midwest. It’s pretty well documented that above 70s: tough on cows; below 70s: allows them to recover nicely. I’m in Gurnee, Illinois, which is Grand Rapids [00:03:00] latitude on the Michigan side. For us to get nights above 70 is rare. And we just went through a pretty good stretch where we had a lot of them. The entire Mideast and the Midwest, we went through a solid four or five days of pretty bad smoke. At least our area was bad enough that just walking outside to get your mail, you could taste it. So I can’t imagine that helped anything. Greg Scheer: How much it hurt is hard to quantify maybe, but definitely didn’t help things. Josh White: Are we still really talking about two different countries, more or less? California, everything seems to be fine. They’re running great. They’re just pumping out milk, and then the rest of the country where it feels a little tighter? Greg Scheer: That’s the sense I get everybody I talk to. Yes. You’ve got California on an island there just filling up their plants, and everybody else in a tighter feel, all the way from the Upper Midwest, Mideast, Northeast. And then as you mentioned, I do think the pull to the Southeast will be starting fairly soon as their production slows, and by mid-August when they’re bottling for schools it’ll really get tight. Josh White: Europe is also talking about some of the same things. Heat sounds like it’s impacted France the most. Germany’s been pretty resilient. Everything I’ve read or heard is that in the recent weeks, people have taken their milk production forecast for the remainder of the year down in Europe, and by a noteworthy amount. To be clear, I think most expect European milk production for 2026 to be higher than it was in 2025, but it’s been notably higher through June. And looking ahead, for them to be taking those numbers down to modest growth means that they’re expecting year-over-year numbers to be down the second half of the year. So Europe seems to be slowing its rate of growth. Curious to what that means going into 2027. We seem to be making good milk, and we’ve got plenty of ability to process it, but the rest of the world feels like it’s starting to slow its growth rate, and maybe start to slow down as we look ahead to 2027. Class I plants looking to start filling up a bit in the next two to four weeks. Jared, what’s that mean for you and your team and your products? Jared Miklasz: Yeah, moving over to the condensed and fluid skim side, the market has become noticeably longer over the past couple weeks, and the obvious driver there was the disruption that Fairlife experienced, which affected multiple plants across the country. With those plants still operating below full capacity following that cybersecurity event, milk that would have normally went into their UF and finished protein beverages has been redirected into balancing outlets which, in turn, made condensed skim much more available, and that increased availability was real. We saw a lot more local offers as a result. As operations normalize and those plants continue to ramp up, I would expect some of that excess product to be reabsorbed, although the timing remains still uncertain. Condensed skim has been tight for much of the year. Obviously, that’s been supported by the steady Demand from both Class II and III. And the strong nonfat demand has also kept skim solids competitive. As those dryers continue to pull available skim [00:06:00] away from the condensed markets school milk will also begin here, as Greg alluded to, which should move more milk back into the bottling programs and further reduce the amount of condensed skim available for manufacturing for these Q4 months. Moving over to the UF side of things, that continues to have the strongest long-term demand story. We’ve touched on it almost every podcast, but high-protein dairy appears to have real staying power. Demand is coming from athletes, consumers focused on weight management, older adults trying to maintain muscle. And that’s even beyond the folks using the GLP-1 medications who are told to prioritize protein. That demand also extends well beyond protein shakes. It’s into yogurt, lactose-reduced products, other nutritional beverages, other applications that require greater control over protein, lactose and total solids. But the other key part of that is the cheese, as that’s an important outlet for UF. As those butterfat levels in the farm milk continue to rise, high protein UF can help rebalance that cheese vat and improve yields. The challenge is that cheese makers are competing with higher value protein beverage and yogurt for that same UF supply. More UF capacity is expected to come online, though, here later this year and into ’27, but that does not necessarily mean that the market will become over-supplied. I think the key question is whether capacity grows faster than the demand. The category obviously remains strong, although that increased competition from a wider retail perspective and potential consolidation could eventually slow growth. But so far that demand has continued to outperform expectations. That strong UF demand also tightens the broader skim market because, obviously that milk is moving into UF and no longer available for condensed skim or nonfat. But, overall improving milk production should create more opportunities, particularly in the skim market. However, that strong demand has regional processing constraints and plant reliability all play key factors here long term. Josh White: So we’re probably not gonna be moving in a straight line here, right? As production responds, we’re trying to anticipate how demand continues to grow. We definitely know it’s in vogue. It seems structural, like that we would see more of these protein-enhanced consumer products coming online that are using liquid protein, as well as the popularity of the whey products and some of the others. But over the course of the next 30, 60 days, how are you feeling like that balances out? I heard you mention that we don’t really see a lot more UF coming on until maybe later in the year. In the meantime, if I’m mapping this out correctly, particularly in the eastern half of the country, we’re already snug milk. We have a lot of capacity for cheese that has been filling. We got hit with some heat, and we’re trying to digest the impact on milk production, but we believe there’s been some already in mid-July. And Class I’s gonna start to ramp up in August, and at the moment it feels to me like we’re gonna be competing pretty heavily in all of these sectors for the available milk solids that are out there, and it’s already snug [00:09:00] before the Class I starts to pull their share. Jared Miklasz: Yeah, it feels like a perfect storm here. Everyone’s competing for those solids in the back half of this year before that additional capacity comes online to meet some of that demand. And that competition’s been playing out all summer, but I think it’ll really heat up as we get into August and September, and October, and schools start ramping up, and all, everything aligns there. So I think it’ll be very interesting to see, if any product sectors get shorted.  On the protein beverage side they have shelves to make sure they stock and keep that space at the big box stores as well. So I think they’re gonna try to get their milk, but you alluded to it, these, investments on the cheese side, they’re gonna wanna keep those plants full. Jared Miklasz: So it’s gonna be interesting to watch. Josh White: June milk production was a little bit higher than maybe most expected, 2.3% for the country, if I read it right. But most of that heat impact has been in recent weeks, right? The recent three weeks, so since July. We’re looking at a milk production number that’s dated, but we’re experiencing a milk production climate right now that seems to be a little bit tighter for a variety of reasons. But probably one of the bigger one is normal seasonal summertime heat, but may be coming on a bit earlier than expected and a bit stronger than we’re used to at this point in time. We’ve had more headwinds in July. Let’s talk cream for a second. Butter is moving counter seasonally.  Overall, the market still feels heavy, but normally this time of year we wouldn’t be moving in the direction that we are. So let’s go with where everything starts. What’s happening on the cream side of things? Jared Miklasz: Yeah, fat remains tight, which has been, somewhat surprising given the amount of milk being separated for the high-protein beverages and all the value-added skim products that we just talked about. As those markets continue to grow, obviously that generates butterfat and that has to find a home. But based on that, I, I would’ve expected more cream to be available, but instead that market has continued to absorb it. Butter is currently trading in the 155 to 160 range, well below levels that we saw last year. And at those levels, cream is much easier for the manufacturers to use in ice cream, cultured dairy, cream cheese, and other Class II applications. It reduces that risk far as finished product and carrying less value. but part of that may be the manufacturers that, you know, adding that fat back into formulations after pulling back when butter prices were much higher. Lower fat cost obviously as far as the taste and texture can improve flavor and yield across the board for a range of products. Even with the stronger milk production and continued growth in the farm level butterfat I do not expect that the cream market is suddenly gonna become long, particularly during these summer months and with the heat that’s still on the horizon and pressure on both milk and volume and components. Over time, the additional milk and fat production should help bring the market back into better balance. But right now, it’s been long. That processing capacity will remain just as important as the total volume that’s being produced. Josh White: Is Class II performance still very strong this year? Jared Miklasz: It is, yeah. They’re the ones that are soaking up the majority of that fat right now. Josh White: Do we have a sense for if we had to try to measure the whole category, and I realize there’s a lot of products that go [00:12:00] into that category, it’s pretty difficult to paint the broad brush. But do we have a sense for are people looking at current markets as an opportunity to build structural inventory, or are they just moving that much more at the shelf? Jared Miklasz: I don’t have a good answer for that one, man. Josh White: Yeah, I don’t either. It’d be curious. ‘Cause if our Class II performance, we’ve seen just domestic performance in certain products look really well year to date. Like the amount of nonfat that’s been consumed domestically, the Class II numbers suggest that things are going really well in, in those markets. I’m just curious if consumer demand is up that much for some of these because maybe pricing promotions or other things, or if there’s been some structural stock building in anticipation of needs the rest of the year. Let’s move on. Let’s talk about cheese a bit. Cheese just made a pretty decent move higher. In Europe similar things, mozzarella prices have really started to move higher in Europe. And now all of a sudden with the U.S. moving higher and European cheddar quite a bit lower than the bounce they saw on their mozzarella, we’re not maybe in quite as an advantageous price position internationally as we were before. How do we see that playing out? Jeff Daanen: You just wonder the real effect is it gonna be for a month or two when we see what happens and how much cheese is out there. But there is cheese available. If you wanted extra loads, they are there. We’re pretty heavy in cheese. The only thing that we don’t have a lot of right now is mozzarella. A lot of that had to do with the World Cup, and there’s some plants that shut down for maintenance. Like Jared said, it was kinda like the perfect storm. plants shut down. People were eating a lot of pizza because of the World Cup, a lot of house parties and stuff like that. But in about another month we’ll be out of this, and there’ll be plenty of mozzarella available. Jennifer S. Kuo: Our price is a lot higher right now than compared to Europe. especially in the Middle East, and even in Asia still, so many people delayed what they would’ve normally ordered in Q2 and going into Q3 because of all the uncertainty, the much higher fuel costs. Everybody has depleted their inventory. And despite our higher prices, we are still getting many requests now still from the Middle East. Pricing really isn’t an issue. It’s just how soon can you ship, and how soon can you guarantee that it’ll get here? So price does not seem to be the barrier right now. Everybody has used their inventory, and they all need to restock. We have the supply. They’re willing to pay a little more. Europe hasn’t really been a conversation with any of our customers. They have not really tried to push back and say, “Europe is better priced right now.” But yeah, the demand is definitely there right now, despite the jump in our market recently. Josh White: Interesting. So it feels like the international demand’s there. The customer’s de-stocked. But at least for products other than mozzarella, we feel really heavy domestically. Is that still accurate? Jennifer S. Kuo: Yeah. Yes. Yeah. But we are seeing the demand in the Middle East is not just for mozzarella right now. It is more geared towards [00:15:00] cheddar. We are getting more inquiries for cheddar than mozzarella right now, which is good for us, both white and color. Tyler Jokerst: Obvious barriers there or risk can be tied around the current situation in Iran as well. Jennifer S. Kuo: The Red Sea seems to be an issue now as well. Tyler Jokerst: Yeah. Josh, you’re dealing with updated issues if the Houthis are getting involved, when you’re looking at Yemen that’s a direct effect on the Red Sea, which is the other half of that peninsula . And then it starts to limit the only access point that you can have into the Red Sea being through the Suez Canal. So it can create a major supply chain choke point for just anybody trying to get any kind of imports into the region. Josh White: Including Europe, right? Tyler Jokerst: Yeah, because, that tends to be a route that can cut down on transit times. So you can run into situations where you might have to go around the Cape of Good Hope to get where you need to get. So it can cause a lot of complications across the board. Josh White: So, you got an international market that does demand product. They’re not well covered, but we’re constantly fighting our ability to access and supply that demand. Same story two months later. Jennifer S. Kuo: Yeah, and freight has doubled, And that did not seem to be a barrier. Tyler Jokerst: Nope. Josh White: Demand seems resilient then, huh? Tyler Jokerst: Yeah, so I guess Josh, not being too familiar on the dairy side, still learning a lot I would imagine that means the price difference there is significant enough where historically logistics has been a major barrier for U.S. product getting international. I think that clearly the opportunities continue to make themselves clearer for international growth with U.S. dairy product. Josh White: If we could wave a wand and the conflict was over tomorrow, which is not likely, I understand that, do we think that customers are going to step in heavily and demand’s gonna feel strong at that moment because they’re not getting an adequate amount of product? Or have they been purchasing to be safe all along and trying to stay ahead of their needs? Jennifer S. Kuo: I think they’ve been trying to wait it out, and they keep thinking, “Oh, okay, it’s, the war is over, the war is over,” and it keeps restarting. I don’t think they have any inventory now. They wanna know how fast can you get it here and how much. Josh White: Specifically as it relates to the Iran conflict, where are we at in terms of demand destruction? Because when we started these conversations, and I think we had Cefetra on a call probably almost two months ago now, and we asked the question: how long does this have to go on before it goes into notable demand destruction within the region because people can’t import the raw materials they need to make the products that they consume? If price isn’t, really the barrier at the moment, it still is access to the supply. I think at that point in time we talked about August sort of being, like, the magic month to where if this lasts into August, we’re gonna start to really hurt dairy consumption within the region. Jennifer S. Kuo: I think that’s still the magical question we’re trying to find the answer to. Tyler Jokerst: The war is prolonging the situation. It could’ve happened by now, but that huge variable is not really giving us a good read. Josh White: Do we think the answer is gonna be universally the same between milk [00:18:00] powders, butterfat, and cheese, or is it different for different products? Jennifer S. Kuo: The answer’s the same because it’s availability. They’re all on the same boats, right? Yeah. You don’t ship cheese separately from powder separately from butter. I think it’s all just access based. Josh White: I’ll clarify the question. It’s less about the ability to get the product and more about at what point in the timeline when you can’t get it conveniently, do you start to have demand destruction on the consumer level? Because you can’t get the cheese, which you will find its way to retail, the butterfat, which is largely an ingredient for processed cheese applications and other things, the milk powders, which serve some of the same and some different manufacturing products. All three of them overlap each other like a chain, but the cheese is closest to consumer. The butterfat is very close to consumer as an ingredient making some of these processed cheese products and other things, milk powder is going into some of that, but then also as an ingredient maybe in other applications like bakery and some consumer packaged goods. If we get into August, which of those areas is most vulnerable? Is it the consumer products because they really are bringing it in just in time, they have to make what they make they’re considered more luxury type items that, you can cut from your diet if you can’t get it versus maybe something along the lines of manufactured products that they may have more deep inventories of, and they will run out, but they might not be running out until September or beyond. At this moment I don’t get the impression talking to European colleagues, talking within our own team in the different product categories, it doesn’t feel like material demand destruction yet. It seems like we’re still finding a way to get some product in, seems like they’re still willing to pay for product, seems like some stuff’s still happening. It’s just at some moment that will come to a head, I think. And we initially expected by August it would become a real problem that meant we’re going to be missing dairy demand out of that region. And we’re knocking on the door of August. Josh White: We’ll be right back after these messages. Diego Carvallo: I’m Diego Carballo with T.C. Jacoby & Co.. T.C. Jacoby & Co. specializes in international dairy markets. For new customers that haven’t done business with Jacoby, I would tell them that we can provide them with many of the powders, dairy products that they consume, not only with the physical product, but we can also help them mitigate their risk. We know dairy. We know the main players. We know the main providers for the whole value chain. We are one of the strongest players in the U.S. market because we have contact all the way from the farmer moving the liquid milk all the way to the end users that buy the end products. I am Diego Carballo with T.C. Jacoby & Co., and we bring dairy to the world. Josh White: Let’s shift gears. Diego, let’s talk a bit about nonfat dry milk, skim milk powder, and what’s happening, globally [00:21:00] there. Yesterday, we had a firm GDT. What does that tell you? Diego Carvallo: We’ve seen the market under heavy pressure, mainly in the U.S., which was the market that was the most expensive for the past I would say six months. It seems like the U.S. market is going back into a price range where we’re competitive internationally. And that had to happen because the U.S., as we’ve mentioned before, we need to export about two out of three loads that we manufacture in the U.S. for nonfat. And we were not competitive for a long period of time. Our prices were $400 to even $1,000 per metric ton higher than European prices. And now that we finally have plenty of availability we have to find a price where exports become competitive again. And that’s what’s happened. In the past few weeks, we’ve had a few additional factors that have added pressure to prices, and that’s what Jared mentioned on plant interruptions in the U.S. And that’s definitely shifted some skim milk concentrate and some products to the drying towers. And that’s adding a lot of pressure onto prices. We’re seeing more inventory, more product availability from the manufacturers. The market is looking for other outlets, and those outlets are in the Middle East, in Asia, and other places, maybe South America, where the cost of the freight has gone up to an extent where we’re paying probably twice what we used to pay. So the exports price has to come down so that we’re competitive again. We should find some support in the current levels. We’re close to the $1.40s and the physical offers are even lower than that especially for SMP. For SMP, we’re seeing offers close to the $1.35, which is ten cents under the current futures. And I think at that level, we’re starting to be competitive even with a more expensive freight rate. I think we should find support unless we start seeing Europe trend lower and New Zealand prices also trending lower, which hasn’t happened at this point. A lot of availability around and not too many customers looking for product at this moment. Josh White: Okay, on the whey product side, it is absolutely the definition of a summer market right now. I think after two quarters of prices constantly moving up for whey proteins, and the whey market trying to rebalance so many changes over the past year. Over the course of 2025 and into early 2026, we saw a lot of large sweet whey powder producers upgrade their facilities to higher protein WPC80 or WPI. At the same time, there was the commissioning of a very large sweet whey powder facility in Texas that is offsetting the production that we’ve lost, and that’s been a bit turbulent. And that just means that we’re exchanging approved brands for both domestic [00:24:00] customers and international customers for a new brand that needs to be approved. And so we’ve seen a trading range for sweet whey powder that’s been 60 to 70 cents for quite a while. But the actual spot market has seen a lot more basis volatility. New brands trying to buy their way into business, brands that remained that have legacy or approvals for perhaps Asian clientele in a market that seems to be pretty short right now, they’re getting bigger basis premiums. So sweet whey powder has been largely range-bound, but that doesn’t really tell the story. It’s been a big shift in who has the product and where that product can go. On the protein side that story’s pretty well-documented and well-reported at the moment. It is shockingly resilient. Diego mentioned that milk proteins are realizing the benefits of this health and wellness movement. Some of the current trade relationships might be supportive of milk proteins. Aside from that, we’re just seeing more demand, people formulating to it, buying more and using more of it. Jared talked about the UF side of things and how there’s just new demand creation in a lot of different categories from beverage to, some of the other Class II products. The whey category remains just on fire. It seems to be both products. Now, we had two quarters in a row where people were terrified they couldn’t get access to supply, and they watched pricing increase by 20-plus percent. Now we get into the summer and pricing hasn’t increased over the last few weeks, and that’s making some people nervous. You’ve got a lot of people out there that are like, “Oh, it’s not gonna continuously go up. does that mean this market’s going to crash?” It’s always possible, of course. These markets don’t move one-directionally. We should expect a retracement at some moment in time. But everything I read from the consumer demand aspect of it, I don’t see any cracks in the floor. What I see is we’ve moved pricing up so rapidly that now that people are going into the summer months and maybe taking some holidays, if they come back in August and need to replenish, this thing goes right back up. If they come into August and find out that the movements on the shelf at the grocery stores have slowed as much of a price increase we’ve seen, we should look out. So I’m not in either camp right now. I guess I’m a little bit more of the belief that the consumer profile seems to be growing, seems to be willing to pay the prices that we’ve seen. And every time we start to think that the GLP-1 catalyst will end or mature, the GLP-1 drug gets cheaper, you can take it in a different form, and a larger percentage of Americans are actively using the drug. I’m also starting to see the GLP-1 aspect of the protein market get reported in Europe more. We have to remember, the U.S. market is nowhere near mature and in terms of its adoption of GLP-1 as a weight loss tool, consumers are educating themselves at a rapid level, trying to understand what the right foods are, and dairy seems to be on the right side of that discussion. Whey protein maybe being the biggest beneficiary. Milk proteins, though, certainly [00:27:00] a beneficiary. And the rest of the world still can follow. So I don’t know. I remain pretty bullish protein overall, but I think it would be irresponsible to assume that this is a one-directional market, and that it’s just gonna resume an uptrend as we get past the summer slowdown that we’re experiencing in North America and Europe. We need to be aware of what some of the potential upside shocks could be to the market as the globe enters those months where we produce the least amount of milk. We should keep our eye on a few potential shocks. Not all to the upside, some to the downside but I think we’re vulnerable to see maybe a little bit of volatility in the months to come. Let’s go through the group as sort of kind of a fun round the table. Most important discussion or impactful thing in the past week that has your attention. So Tristan, let’s start with you. Tristan Suellentrop: One of the most notable developments is the continued shift towards milk proteins. As WPC80 and WPI prices remain expensive and a little bit more difficult to source, I’ve noticed more people are evaluating MPCs as a partial replacement which is creating stronger demand across the entire high proteins category.  Kait, how about you? Kait Holzschuh: There does seem to be a lot of demand for whey permeate and lactose abroad that you just don’t see in the U.S., so I find that kinda interesting. Josh White: Yeah, good point. We didn’t touch on that, but it started with lactose, and now it’s even cascaded to whey permeate. The amount of inquiries that we’ve received in the past couple weeks across all sectors: international feed sectors, international food sectors, domestic food, and domestic feed. There’s clearly it’s clearly a tight market. Great point. Thank you. Miguel? Miguel Aragón: It might be just isolated to Mexico, but there is a glut of cheese in Mexico. When we were in the $1.40s, probably, a lot of cheese made its way down there, and it has affected the market right now. With the prices now, the hope of the customers that we talk to is that things will level off. But right now, still a lot of cheese, a lot of cheap cheese in Mexico. It affects current business right now. And the second one is demand during World Cup was not as good as expected, and this comes from the Association of Supermarkets and Convenience Stores in Mexico. So two things that really caught my eye in the last two weeks. Josh White: How do we feel the same question would be answered in the U.S.? Do we think that the World Cup impact on demand was worse than, equal to, or better than expected? Jeff Daanen: I think it was better than expected. Because when this first came out, I didn’t think that it would impact a whole lot. But when it was all said and done, it just seems like the snack part of the cheese business really took off, along with pizzas. I think there were a lot of pizzas consumed. That’s why mozzarella’s really tight, and it probably will be for at least another month or so. Josh White: Jonathan? Jonathan B. Powers: Yeah, I think probably the most impactful thing is talking about WPC [00:30:00] 34 and nonfat. Nonfat and SMP hasn’t been readily available in the Midwest, and there’s a need for that protein range in the calf milk replacer world, and we’re starting to get a lot more conversations around stockpiles for those products. As we’ve discussed, WPC 34 is kind of a dying product. There’s not a lot of people that are making it anymore, and there seems to be a lot of companies, even in the food space, that are still very reliant on it and trying to satisfy the need for it when it isn’t necessarily available. We’ve had people reach out for permeating lactose. The volume of requests has been astonishing, honestly. Josh White: Manuel? Miguel Aragón: Where I have a lot of my focus is cheese in general. It just feels like there is something brewing right now. Technically, it’s entered a uptrend right now again and it’s still choppy, right? At least on the futures board. But it feels like there’s opportunities there and yeah. So I’m just soaking up everything I can hear about cheese right now and really try to get a feeling for the market there. Besides that, nonfat is just shaving off more and more. We basically broke the support we had for a long time now, so it really feels like it’s on another leg down. Yeah, we’re gonna see how that plays out. I personally also think we’re gonna find support in the 140s. We might test a little lower than that, but at some point, it should stall and become a little more stable. Josh White: Diego, based on what you said about S&P in the 130s and then what Manuel just said about the technical support and what that looks like, that kind of aligns, right? Because I think I heard you make the comment that as we, a 140 nonfat, you can make S&P cheaper for those that don’t really pay attention to the difference. Lactose is really tight, too. Do we think that there’s a connection to why the milk sugars are tight, and all of a sudden, we are seeing pricing that’s a little bit more SMP competitive globally? Diego Carvallo: I do think that there is, yeah. We made very little SMP for the first six months of the year because it wouldn’t make any sense to export when we’re $1,000 higher than European markets. Now that we’re competitive, it does make a lot of sense to make SMP, especially when protein is very high and you can take it down with a cheap product like lactose or milk permeate. It makes sense to find demand in other markets for the SMP. So I do think that the demand for the carbohydrates has picked up now that nonfat has become competitive again. ​ Josh White: For the benefit of everyone so we’re all talking the same language, nonfat dry milk and SMP are typically universally used in applications, but they’re very different products. What we call nonfat dry milk is an unstandardized product. That specification is a minimum protein percent of 34. But today’s productivity [00:33:00] of components in our milk supply, the average unstandardized protein level in nonfat dry milk is pushing 38 or more percent at least 37 and a half in most times. Now, the rest of the world standardizes their product, and they standardize to either one of two things: 32%, which is the old Codex, 34%, which I think is a little bit more common. Or at least it’s common out of the U.S. that we would standardize to 34%. When we say why would there be a connection between lactose and milk powder, you can add lactose or milk permeate to your nonfat supply to bring the protein down to a standard level. So when we stay standardized, that’s what we mean, where they’re basically bringing it to a 34% protein, most commonly out of the U.S., and then that allows us to compete for international business. Certain markets can use either, but certainly would, prefer a higher protein content at a competitive price. So when I mention our futures are at $1.40, that’s nonfat, and our average nonfat has a higher protein. So if we’re standardizing, that means that we can add this cheaper lactose or cheaper milk permeate to the volume, and that lowers the overall price. So the whole conversation there was more or less like, “Hey, are we making SMP now, and are we competing globally for international business? ‘Cause if we are, that also tells us at least we’re closer to finding a support price, finding some type of global support level for the product.” But you’ll hear us really start to break down the difference between SMP, nonfat dry milk. But many customers can use either. I wouldn’t say most, but many Okay. I, we covered a lot. Yara, any discussions over the past week that you that you feel were most interesting? Yara Morales: It’s a lot of inventory in Mexico, and the customer was offering me nonfat dry milk instead of buying. That was the most surprise, we know that since the price is going down so bad, and they have a lot of inventory with high prices. They have a contract that they have to take it. That’s hard for them. They are losing a lot of money. And the inquires, they looking for whey permeate. They are looking for lactose and proteins. But it’s hard to get the whey permeate and the lactose like you mentioned it. But this is the inquiry we have in Mexico so far, just protein basically because otherwise it’s difficult right now. Josh White: Yeah, agreed. Okay, all, I know it was an unusual discussion. Thanks for joining us today on the Milk Check. Mike Brown: For one part of the supply chain to be successful, everyone has to be. My superpower is practical application of data and analysis. I believe firmly that Jacoby’s success is because we help our suppliers and our buyers be successful. I’m Mike Brown, and I love working for T.C. [00:36:00] Jacoby and Co. because I get to help people Make their businesses more successful. ​

¿Qué ocurre cuando una inteligencia artificial clona tu voz sin permiso y empieza a publicar contenido que nunca has grabado?En esta tertulia de Itnig, Bernat Farrero conversa con Masumi Mutsuda, actor de doblaje, informático y CTO de Itnig sobre el impacto real de la inteligencia artificial en las voces, el doblaje y el trabajo creativo.Masumi relata cómo descubrió que habían clonado su voz como Silver, personaje de Sonic, para crear vídeos completamente ajenos a él. A partir de su experiencia y de su trabajo con el Sindicato de Actores de Voz de Barcelona, explica cómo cientos de profesionales han encontrado sus voces en plataformas de IA sin haber dado su consentimiento, qué pueden hacer para retirar ese contenido y por qué la tecnología avanza mucho más rápido que la protección de sus derechos.La conversación también aborda el papel de empresas como ElevenLabs, la diferencia entre una interpretación humana y una voz sintética y la pregunta que inquieta a toda la industria: ¿puede una IA llegar a reproducir todos los matices de un actor de doblaje? Más allá de la clonación de voz, Bernat y Masumi analizan la evolución de los agentes de IA, desde el uso de OpenClaw, Codex y ChatGPT para ejecutar tareas cotidianas hasta la traducción simultánea y los futuros pagos automatizados. También explican las diferencias entre modelos cerrados, open-weight y open source, el avance de la IA china y la visión de Elon Musk sobre un futuro marcado por la automatización, la renta universal y una abundancia sin precedentes. Una conversación sobre inteligencia artificial, clonación de voz, actores de doblaje, agentes autónomos y cómo nos relacionaremos con una tecnología que ya puede hablar, programar y actuar en nuestro nombre.

People are calling the new ChatGPT Voice their “AGI moment.”

Visual artist and brand content creator Joe Salvatore joins Reid Hoffman and Parth Patil to explain how AI is turning creative professionals into orchestrators of systems rather than makers of one piece at a time. Joe spent four years chasing every new image and video model out of pure creative curiosity before Parth pushed him to take a step back and become a director of these tools. He traces that shift, from generating a single image, to making 200, to running fully automated ad campaigns through Codex, and explains why the critical skill turned out not to be technical at all. As he puts it, he can outsource production, but he can't outsource his taste. He walks through a live campaign for Ketone-IQ, using a Hermes agent to research decades of proven advertising styles, then chaining models together to generate and grade hundreds of on-brand variations overnight. He and Parth get into how a shared creative agent learns from everyone it works with, why articulating an idea clearly has become the differentiator, and how AI made the gap between a sloppy brief and a tight one impossible to hide. Reid, Parth and Joe close on Wittgenstein, and why language no longer just describes the world but summons it. Referenced in episode: the Lord of the Rings x Wes Anderson AI trailer: https://www.youtube.com/watch?v=KrjL_TSOFrI

A series of 16 influential political pamphlets published between 1776 and 1783 during the American Revolutionary War (1775-83) titled The American Crisis, or simply The Crisis, by eighteenth-century Enlightenment philosopher and author Thomas Paine — an Englishman living in the colonies who signed his essays anonymously as "Common Sense," the title of his earlier influential work. Each essay, bolstered the morale of the American colonists to fight hard for their independence, appealed to the English to support the colonist's cause, clarified the issues at stake, and denounced any type of negotiated peace. The essays were gathered into one volume in 1882, showcasing the iconic opening line: "These are the times that try men's souls. The summer soldier and the sunshine patriot will, in this crisis, shrink from the service of their country; but he that stands it now, deserves the love and thanks of man and woman." The American Crisis by Thomas Paine at https://amzn.to/4dKKClU Common Sense by Thomas Paine (book) available at https://amzn.to/3MKX77b Writings of Thomas Paine available at https://amzn.to/3MCaFC2 Books about Thomas Paine available at https://amzn.to/4s3qxOg ENJOY Ad-Free content, Bonus episodes, and Extra materials when joining our growing community on https://patreon.com/markvinet SUPPORT this channel by purchasing any product on Amazon using this FREE entry LINK https://amzn.to/3POlrUD (Amazon gives us credit at NO extra charge to you). Mark Vinet's HISTORICAL JESUS podcast at https://parthenonpodcast.com/historical-jesus Mark's TIMELINE video channel: https://youtube.com/c/TIMELINE_MarkVinet Website: https://markvinet.com/podcast Facebook: https://www.facebook.com/mark.vinet.9 X (twitter): https://twitter.com/MarkVinet_HNA Instagram: https://www.instagram.com/denarynovels Mark's books: https://amzn.to/3k8qrGM Audio credits: The American Crisis by Thomas Paine (a LibriVox production read by volunteers and coordinated by Michele Fry, 2014). See omnystudio.com/listener for privacy information.

The Autobiography of Benjamin Franklin (1706-1790) written in the form of an extended letter to his son, William Franklin (1730-1813). Ben kept good records of his life and travels, and although he was never President, he still played a crucial part in American history. The Autobiography of Benjamin Franklin at https://amzn.to/43cp6CV Benjamin Franklin Books available at https://amzn.to/41fUkGD ENJOY Ad-Free content, Bonus episodes, and Extra materials when joining our growing community on https://patreon.com/markvinet SUPPORT this channel by purchasing any product on Amazon using this FREE entry LINK https://amzn.to/3POlrUD (Amazon gives us credit at NO extra charge to you). Mark Vinet's HISTORICAL JESUS podcast at https://parthenonpodcast.com/historical-jesus Mark's TIMELINE video channel: https://youtube.com/c/TIMELINE_MarkVinet Website: https://markvinet.com/podcast Facebook: https://www.facebook.com/mark.vinet.9 X (Twitter): https://twitter.com/MarkVinet_HNA Instagram: https://www.instagram.com/denarynovels Mark's books: https://amzn.to/3k8qrGM Audio credits: The Autobiography of Benjamin Franklin (Librivox, read by T. Hersant). See omnystudio.com/listener for privacy information.

- Unscheinbar, aber wichtig: iOS 26.6 und Co. erschienen - Große Bühne für das iPad mini? Gerüchte über wasserfestes Gerät und OLED - Home, Home, Hurra: Apple plant neue Home-Geräte angeblich für Herbst - Alles in eins: Für wen ist AppleCare One interessant? - Vertrauen bereits verspielt? Apple, die Brille und das Vorgehen der anderen - Umfrage der Woche - Zuschriften unserer Hörer === Anzeige / Sponsorenhinweis === Verbessere deinen Online-Schutz mit einer All-in-One-App für digitale Sicherheit! Sicher dir dein exklusives NordVPN-Angebot + 4 Extra-Monate hier ➼ https://nordvpn.com/apfelfunk Risikofrei mit der 30-Tage-Geld-zurück-Garantie von NordVPN! === Anzeige / Sponsorenhinweis Ende === Links zur Sendung: - Apfelfunk News: Apple schließt hunderte Sicherheitslücken in iOS 26.6, macOS Tahoe 26.6 und mehr - https://apfelfunk.com/apple-schliesst-hunderte-sicherheitsluecken-in-ios-26-6-macos-tahoe-26-6-und-mehr/ - Apfelfunk News: KI-Tools wie Claude und Codex bei Apples Sicherheitsupdates - https://apfelfunk.com/ki-tools-wie-claude-und-codex-bei-apples-sicherheitsupdates/ - Apfelfunk News: Erstes wasserfestes iPad mini mit neuartiger Lautsprechertechnologie - https://apfelfunk.com/erstes-wasserfestes-ipad-mini-mit-neuartiger-lautsprechertechnologie-geruecht/ - Apfelfunk News: Neues Apple TV, HomePod mini und Home Hub fast startklar - https://apfelfunk.com/neues-apple-tv-homepod-mini-und-home-hub-fast-startklar-geruecht/ - Apple Newsroom (Deutschland): Apple bringt AppleCare One nach Deutschland und vereinfacht die Abdeckung - https://www.apple.com/de/newsroom/2026/07/apple-brings-applecare-one-to-germany-streamlining-coverage/ - Apfelfunk News: Apple Glass: Misstrauen der Öffentlichkeit wird Herausforderung - https://apfelfunk.com/apple-glass-misstrauen-der-oeffentlichkeit-wird-herausforderung-geruecht/ Kapitelmarken: (00:00:00) Begrüßung (00:27:21) Werbung (00:31:07) Malte in Norden (00:38:18) Themen (00:39:13) Unscheinbar, aber wichtig: iOS 26.6 und Co. erschienen (00:49:19) Große Bühne für das iPad mini? Gerüchte über wasserfestes Gerät und OLED (01:00:22) Home, Home, Hurra: Apple plant neue Home Geräte angeblich für Herbst (01:14:47) Alles in eins: Für wen ist AppleCare One interessant? (01:21:06) Vertrauen bereits verspielt? Apple, die Brille und das Vorgehen der anderen (01:42:17) Umfrage der Woche (01:46:10) Zuschriften unserer Hörer

My guest today is Sam Altman, CEO of OpenAI. It's a conversation spanning the history, present, and future of OpenAI, from the origin of ChatGPT through Codex, hardware, and their new Jalapeno chip. We discuss the early decision to buy compute at a scale nobody thought was rational, and the plan to build a gigawatt of new capacity every week.  We talk about Kimi and distillation, the Hugging Face incident and what it means for the pace of AI development, and what it's like to raise kids who will grow up never knowing a world without abundant intelligence.  Please enjoy my conversation with Sam Altman. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠.  ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠Ramp's⁠ mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠ramp.com/invest⁠⁠ to sign up for free and get a $250 welcome bonus. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Invest Like the Best listeners get a special offer of $1,000 off Vanta when you go to ⁠vanta.com/invest⁠.  ----- WorkOS⁠ is the infrastructure B2B and AI-native companies use to sell to enterprise. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. To learn more, visit rogo.ai/invest. ----- ⁠Ridgeline⁠ has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:02:02) Intro: Sam Altman, CEO of OpenAI (00:02:35) Refocusing (00:05:43) OpenAI's Compute Bets (00:09:07) Data Centers (00:11:14) Jalapeno Chip (00:11:52) Kimi, Distillation & Open Source (00:14:39) The Hugging Face Incident (00:17:46) OpenAI's Mission & Vision (00:22:14) All the Returns Are at the Frontier (00:22:27) Bottlenecks: Compute, Research, Data (00:23:49) Sam's View on AI & Jobs (00:26:56) Unpopular Bets That Turned Out Right (00:27:45) Model Cycles (00:29:45) How Sam Uses AI (00:32:44) Having Kids (00:34:56) Why Sam Has No Equity in OpenAI (00:35:33) Robotics (00:36:48) The Origin Story of ChatGPT (00:39:22) How to Get AI into More Hands (00:42:20) How Sam Recruited Great AI Researchers (00:43:57) What Sam Learned From Being an Investor (00:45:22) What the Next 6–36 Months Look Like (00:46:31) Codex (00:49:36) Could We Be Oversupplied in Compute in Two Years? (00:50:09) Sam's View on Scaling Laws (00:50:20) Alec Radford (00:51:12) Formative Moments (00:53:50) Kindest Thing

IBM has a plan to profit from the AI race that doesn't involve pouring billions of dollars into data centers or new models. CEO Arvind Krishna sits down with WSJ columnist Christopher Mims after the company's worst single-day stock loss in history to share his vision for Big Blue's future. Plus, lifestyle collections from Palantir and OpenAI have sparked ridicule across the internet. WSJ features editor Jamie Waters joins to explain why the products are selling out anyway. Have you seen an AI-generated post you thought was real? We want to hear from you! Record a voice memo and send it to tnb@wsj.com or leave us a voicemail at (212) 416-2236. Sign up for the WSJ's free Technology newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Topics covered in this episode: Some more things about Django I've been enjoying Who cleans up after the vibe-coding party? Where Did All Your AI Tokens Go? AgentsView to the rescue! Careful with phishing all Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: Some more things about Django I've been enjoying Julia Evans is learning "2010-style" web dev (Django + SQL + server-rendered HTML) after years of Go backends and JS-heavy frontends Query builders: likes defining custom QuerySet classes with chainable filter methods (.approved().future().with_tags()) — more readable than raw SQL Template filters: highlights urlize, linebreaksbr, json_script, and especially querystring for building/modifying query-string links in templates Migrations: still loves Django's auto-generated migrations — 19 and counting on her project Skips inheritance for class-based views; prefers function-based views for sharing code, though fine using Django's own mixins/interfaces Performance surprise: CPU profiling (via py-spy) — not slow DB queries — revealed the culprit; she'd accidentally disabled the cached template loader, and re-enabling it took throughput from ~2-3 req/s to ~12 req/s on a $10/mo VM Michael #2: Who cleans up after the vibe-coding party? FT Magazine piece by Sam Learner (July 11) on AI coding tools overwhelming open source maintainers - sent in by listener Dylan McConnell, whose main point was that this ran in the Financial Times, not a dev blog. cURL as the case study - Daniel Stenberg has been the only full-time person on it for years; libcurl has been installed an estimated 20+ billion times with 3,000+ listed contributors. Bug bounty killed - cURL ended its paid security bounty program in January, citing an "explosion of AI slop reports" that take real time to debunk and drain morale. Extractive contributions - authoring a PR is now nearly free, reviewing one still costs a human; tldraw's Steve Ruiz closed outside contributions entirely, asking why he'd want someone else writing the easy part. Guido weighs in - van Rossum says projects are holding emergency meetings over the slop flow, and notes LLM patches tend to touch unrelated parts of a file, making review more tedious. "Vibe Coding Kills Open Source" - paper from Miklós Koren's group: packages frequently recommended by coding models saw big download jumps with no matching engagement, breaking the reputation loop that sustains maintainers. Stack Overflow flatlined - over 100,000 questions a month before ChatGPT, under 1,500 last month, with the response rate cut roughly in half; the public archive is now stale training data. The course-creator angle - Josh Comeau's newest web dev course launched at about a third of prior enrollment, and he worries about devs who never learn which questions to ask. But the most interesting portion is what was omitted. Focused on: The end of the curl bug-bounty Omitted: High-Quality Chaos Why the omission is interesting It fits a narrative. The FT piece is a maintenance-and-decline story, and January-Stenberg is a perfect witness for it. April-Stenberg complicates it - same person, same project, better data, opposite direction on the specific claim being used. The tell is already in the article. Learner quotes Stenberg saying AI tools are much better at finding problems than fixing them. That's the April thesis in one line, and it goes undeveloped. Reason for the shift is process, not vibes. Killing the bounty removed the cash incentive and the venue change filtered the rest. Worth saying out loud, because "AI reports got better" isn't quite it - "no bounty plus a real triage platform" is closer. Joke too: Sarah O'Connor wrote a related piece (is this just before skynet launches?) Calvin #3: Where Did All Your AI Tokens Go? AgentsView to the rescue! Local-first desktop/web app for browsing, searching, and analyzing your past AI coding agent sessions (Claude Code, Codex, Copilot, Cursor, Gemini, Aider, and dozens more) Auto-discovers session files on your machine — no config needed; everything stored locally in SQLite, no cloud/accounts agentsview usage is a drop-in ccusage alternative — reads from pre-indexed SQLite, reports run 80–220× faster on large histories New Activity dashboard shows peak concurrency, active vs. idle time, agent-minutes, and cost — filterable by project/agent/machine, with a -json CLI report too Full-text + optional semantic search across every session; also imports Claude.ai/ChatGPT chat exports Install via pip install agentsview, uvx agentsview, brew install --cask agentsview, or download desktop binaries from GitHub Releases Michael #4: Careful with phishing all The situation I pass this along because it was a pretty sneaky bit of targeted phishing, and happened to play off an old interaction in bandit's repo. As usual with phishing scams there are a bunch of tells that this isn't legitimate, but just enough plausibility that I could see falling for it in a weak moment. Relative nobodies like me haven't historically been worth the effort to hit with scams this specific. Agents change the game though :-/. Be careful out there folks! Original message From: "Patrick (Blacktrace)" [HTML_REMOVED] To: LISTENER EMAIL Subject: Your Bandit #1350 (B105 NextToken false positive) -- just fixed that exact case Date: Wednesday, July 15, 2026 12:02 AM Hi AJ, Saw your Bandit issue #1350 -- the B105 hardcoded-password false positive on the string NextToken. I build a deterministic gate that filters that class of Bandit noise, and #1350 was literally the case I just fixed: NextToken / next_token / page_token / nextPageToken now stay quiet, while a genuine hardcoded token like api_token="sk-live-..." still fires. Verified against your exact case. 30-second paste: https://blacktrace.co/noise-eraser Where it still trips, published: https://blacktrace.co/kruc Curious whether it clears what you hit -- and if it trips on something of yours, that's the more useful reply. Patrick, Blacktrace I asked Claude for some analysis too. It was pretty good at finding them. The message name-drops enough real detail to feel legit, but the structure is pure phishing - everything in it exists to get AJ onto blacktrace.co. The strongest ones: Freemail sender, corporate signoff. Signs as "Patrick, Blacktrace" but sends from emailpjv@gmail.com. Real company outreach comes from the company domain, not a personal Gmail - and there's no last name. Over-specific targeting. It mirrors AJ's exact public activity - issue #1350, the B105 rule, the NextToken false positive, even the token variants. That's the "just enough plausibility" AJ flagged, and it's exactly what agents make cheap: scrape a GitHub issue, auto-generate tailored bait. Legit cold outreach rarely reads your history back to you this precisely. The entire payload is two links. Strip the technical flattery and the message is just "paste here" plus "see results here." When the whole point of an email is the click, that's the tell. "30-second paste." Low-friction urgency, and "paste" most likely means paste your source into their tool - handing your code to a stranger's site. Exfiltration dressed as convenience. Brand-new, no-reputation domain. blacktrace.co has no track record, and the name is doing some ominous work. The /kruc slug is random noise, not how real product pages get named. Precise-sounding jargon that's actually vague. "Deterministic gate," "noise-eraser" - impressive, empty. Bolted onto correct real details (B105 is the Bandit hardcoded-password test, sk-live- is a Stripe live-key prefix) to borrow credibility. The disarming close. "if it trips on something of yours, that's the more useful reply" - engineered humility that flatters your expertise and baits a response. Makes engaging feel like you're doing them a favor, which drops your guard. Extras Calvin: DjangoCon US 2026 is rapidly approaching, August 24-28, Chicago Ruff v0.16.0 massively expands its default rule set Ruff now enables 413 rules by default, up from 59 https://astral.sh/blog/ruff-v0.16.0 Michael: Completely redesigned the home page. Try /insights in Claude Code (terminal) Joke: We're Safe

Most satisfying result in AI so far: a complete branded webinar deck, built from one voice brief, in about three minutes.In this session, I walk through the exact brief I gave Codex, show how it shared memory with Claude, and reveal the finished presentation it built without a single design tool.Then I break down the five phases every business owner needs to understand before using AI seriously:1. Productivity (too small a goal)2. "What good looks like" (the 90/100 standard)3. Transformation (not just faster, fundamentally different)4. Architecture (you are the director, AI is the builder)5. Memory and iteration (save, score, improve)I also show my first-loop recipe: the exact steps to build one painfully real AI workflow from scratch, including the scorecard and memory vault that make the system compound.If you have been using AI for prompts but not for systems, this is where the shift happens.Watch next: The $50K CEO Dashboard Buildhttps://youtu.be/QgFlI9UMkyg

There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right.A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU now up >10x from Jan 2026. Less than two weeks after their July 9th launch, OpenAI said ChatGPT Work and Codex had reached 10M users combined (as we cover in the pod, Codex now powers ChatGPT Work, so all ChatGPT Work users are now users of the Codex harness, even if they aren't traditional engineers) — showing the early innings of what happens when you graduate from coding agents to knowledge work agents:We've been calling out how coding agents are “breaking containment” to do everything else this year to power every other part of knowledge work - and it started with the org chart, with a major reorg last month that amounted to two of Codex's most prominent leaders, Greg and Tibo, taking responsibility over product and ChatGPT specifically, completing a “Superapp” consolidation cycle first discussed in March.With these updates Codex is no longer just a coding tool. In June, OpenAI said knowledge workers already accounting for roughly 20% of Codex's user base and growing more than 3x as quickly as developers. A product dedicated for knowledge workers was being pulled out of the Codex team.However, knowledge work has a different set of problems and environments than coding. For decades, knowledge work has been scattered across different primitives like documents for writing, spreadsheets for analysis, slide decks for communication, and specialized applications for everything else. ChatGPT Work now enables users to work across every primitive with agents. Instead of opening an application and manually operating its features, the user can describe an outcome and collaborates with an agent that can assemble the tools, context, and artifact needed to reach it.From building no-code products at Airtable to leading Productivity Engineering at OpenAI, Akshay Nathan has spent much of his career trying to make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among non-developers inside OpenAI, and the company's broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone.We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences together without making them identical, and how persistent computers, artifacts, Sites, plugins, memory, and sub-agents are changing what people can delegate to AI. Akshay explains why some teams are replacing decks and spreadsheets with interactive websites, how agents can gather context across code, Slack, documents, and local files, and what OpenAI learned from personal-agent products like OpenClaw.Side note: also don't miss Abhihek's sandbox track keynote at AIE, which now powers a lot of the sandboxing for ChatGPT Work… and yes was also broken by an unreleased OpenAI model in the recent HuggingFace incident.Akshay also reflects on how AI is transforming product development itself: why more people will become generalists with a specialty, why ideas and taste become the bottlenecks when almost anyone can build, why LLMs still struggle to generate genuinely grounded new ideas, and why teams must distinguish increased motion from actual progress.We discuss:* Why Codex unexpectedly took off among non-developers inside OpenAI* Why employees felt like using Codex gave them a new superpower* The product insight that led OpenAI to build ChatGPT Work* Why Codex and ChatGPT Work share the same underlying agent harness* How their UX, Git visibility, artifacts, and sandboxing defaults differ* Why OpenAI merged its agent experiences instead of building separate products* How AI is blurring the boundaries between engineering, design, strategy, and operations* Why OpenAI wants the default model configuration to work for most users* When power users should use deeper reasoning, Ultra, or multi-agent modes* Artifacts, agentic spreadsheets, and creating high-fidelity work products* Why interactive Sites may replace decks and spreadsheets* The challenge of designing a simple interface for an agent that can build almost anything* Why users should retry tasks that models could not handle three or six months ago* How AI can gather context for performance reviews without replacing human judgment* The OpenAI automation that turns internal Slack and document activity into memes* What reaching ten million ChatGPT Work and Codex users means for the product* How OpenClaw inspired persistent environments, scheduled tasks, and personal agents* Using ChatGPT for financial planning, budgeting, workouts, meals, and household management* The design tradeoffs behind sub-agents and how much of their work users should see* ChatGPT memory, Chronicle, and long-term context* Why AI may make more people generalists with deep specialties* Why ideas and taste become more important when almost anyone can build* Why LLMs still struggle with the instruction “bring me new ideas”* Measuring productivity through quality at-bats instead of commits, tokens, or pull requests* The critical difference between AI-generated motion and meaningful progressAkshay Nathan* LinkedIn: https://www.linkedin.com/in/akshaynathan/* X: https://x.com/akshaynathan_Timestamps00:00:00 Introduction and Bringing the Power of Code to Everyone00:01:33 Joining OpenAI and Preserving a Startup Culture00:02:40 What OpenAI Learned from Enterprise AI Adoption00:05:28 Why OpenAI Built ChatGPT Work00:07:17 Codex vs. ChatGPT Work and the Shared Agent Harness00:12:07 Why OpenAI Merged Its Agent Experiences00:16:24 Models, Reasoning Levels, and Choosing the Right Default00:20:26 Artifacts, Agentic Spreadsheets, and Model–Product Collaboration00:24:22 Why Sites Could Replace Decks and Spreadsheets00:30:08 Designing an Agent That Can Build Almost Anything00:34:28 From Developer Agents to Knowledge Work—and Everyone00:36:07 Power-User Advice and AI-Assisted Performance Reviews00:40:41 OpenAI's Internal AI Memes and the Ten-Million-User Launch00:44:39 OpenClaw, Personal Agents, and ChatGPT as an Operating System00:50:24 Sub-Agents, Ultra Mode, and How Much Control Users Need00:54:39 ChatGPT Memory, Personalization, and Chronicle01:00:19 How AI Is Reshaping Product Development and Tech Roles01:03:15 Ideas, Taste, and Why LLMs Struggle to Generate New Ideas01:04:42 Measuring Productivity, Quality At-Bats, and Motion vs. ProgressTranscriptIntroduction: Akshay Nathan, ChatGPT Work, and the No-Code ArcSwyx [00:00:00]: We're here in the studio with Akshay from OpenAI. Welcome.Akshay Nathan [00:00:07]: Thank you.Swyx [00:00:08]: And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It's been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt.Akshay Nathan [00:00:32]: Yeah. It's funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there's this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It's funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that'd be really useful to them. But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what's going on underneath the hood. And so, like, I think this launch and a lot of the stuff that we've been up to is, like, the manifestation of that.From Walrus and Airtable to OpenAIVibhu [00:01:33]: How was stuff when you joined? So you joined OpenAI 2023. Now we've got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed?Joining OpenAI and What Hasn't ChangedAkshay Nathan [00:01:44]: I think the more interesting thing is how things haven't changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn't changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we're probably gonna, like, try different products and have different things that succeed and don't. But the vision has stayed the same, and the mission has stayed the same, and we're starting to see the pieces, fall together, and that's really cool.Enterprise Lessons: No One-Size-Fits-All AISwyx [00:02:40]: You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you're bringing into your work now?Akshay Nathan [00:02:52]: I think how there's no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you'd get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it's interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it'on the flip side, it also means that, like, you don't know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there.Swyx [00:03:56]: Do you meaningfully differentiate that from forward-deployed engineering?Akshay Nathan [00:04:01]: I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who's, like, looking at their computer or looking at their phone, like, it's our job in the product to, like, be enabling them and showing them where to go. So we're really excited about that.Vibhu [00:04:24]: Do you think there's been changes, over the past three years of adoption? So there have been, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don't know what to do with it, or have things changed?Adoption, Agents, and the Next 10x MarketAkshay Nathan [00:04:39]: We're seeing now that, like, there's this huge uptake, right? Everyone is extremely excited about it. It feels like, many people are, millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked, so now, like, we're seeing with agents, like, there is probably a contingent of, like, early adopters still who, truly get it, who are like, “ we you can do anything. You just have to make sure the right context is there, it's connected to the right tools, and that you are supervising it, but, like, anything is possible.” But then there's, like, this, like, 10x or 100x bigger market where, like, they don't yet get that, or they don't yet see that. And so I think that's the next stage here. So to answer your question, like, I think the adoption is there and growing fast, but I think the opportunity is, like, far bigger than that. That's where we wanna play, especially with ChatGPT Work.ChatGPT Work, Codex, and the Super App MergeSwyx [00:05:27]: Yeah. well, let's, let's skip ahead to ChatGPT Work. only, like, a month ago or so, announced. what was the decision process that led into it? there was this, overall merging of the super app. Is that what we're officially calling it? you deprecated the browser as well. Just, summarize your last, like, couple months of working on this thing.Akshay Nathan [00:05:50]: Yeah. It feels like forever now, but it's only been a few months. I think maybe the one, impetus that, like- Is most salient is when we release Codex, or even internally had Codex, like, it was really surprising to us, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among non-developers at OpenAI. And, I, through this product development process, like, would go to, like, these UXR sessions to talk to people internally. And the thing that stuck out to me is, like, one, like, you go talk to, like, strategic finance or marketing or whatever, and they're all using Codex for, their use cases. That part's cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, likeSwyx [00:06:34]: It's like, “I'm not supposed to be using it, but I am.”Akshay Nathan [00:06:36]: It was that. It was, like, that they were, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, what we recognized then is that, like, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it's, like, a tricky thing, right? There's many ways you can go about it. And so that's what we called the Merge and the Super App over time, and ultimately launched it in ChatGPT Work, is how do we do that? But it came from that initial realization that, like, the power was not only for developers, like, much earlier than probably even we thought. Like, it could be extended to everyone.Swyx [00:07:17]: How do you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there's a merge of ChatGPT Codex and ChatGPT Work, so is it the opening for the average user, for enterprise, for work? How do you position it?Akshay Nathan [00:07:36]: I think we want to get it to position it for if you're doing work-related things, for lack of a better word, right?Who ChatGPT Work Is ForAkshay Nathan [00:07:42]: I think productivity is what, like, the pillar that I support. Like, that's the name of the team. And the reason for that, the reason we call it productivity and not, like, enterprise or, like, work or something like that, is because there's also personal productivity, right? And, like, I think ChatGPT Work is I've seen people do things in their personal lives that you wouldn't classify as, like, work technically, but, like, these agents are, super capable for. Like, one recent example that someone posted about, on our Slack is, like, someone had, like, a missed package, like they didn't receive it, and then they got, like, the picture of it, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, is extremely tenacious and, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there's all these things that, like, you, work-related or productivity-related things, I think that's what we want the product to be. You asked about Codex. I think we think Codex is, a durable brand, but we have a principle that, like, the user we don't want a user to get stuck in a tab or an experience where they don't get the power of the product. And so, like, everything that you can do, in the Codex portion of the product on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, how much of the Git state, if you're in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the experience of seeing the agents thinking, like, diff forward so that you get exposed to the diffs out of the box. And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one state versus the other? So, there's, like, some opinions that go behind that, but we do want We don't want the user to need to choose which experience they're in.Swyx [00:09:26]: That is a good goal for AGI, right? Like, people don't want, like, to hide to choose what version of AGI they want. They just want the AGI to decide for them. can I get an answer or, like It's not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there prompt level or even deeper differences?Shared Harness, Different UX: Codex vs. WorkAkshay Nathan [00:09:49]: So the harness is the same. The harness is shared. on In both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plug-ins or computer use or artifacts. You get that power regardless of which experience you're in. On the UX side, there's opinionated takes that we have when you're in Codex mode, what the UX should be how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same.Swyx [00:10:16]: I'm just kinda curious. Maybe we can, -- Is there a query that we can run that would look different in the two modes?Akshay Nathan [00:10:23]: Yeah. I tried to create, like ask it to create, like, a retirement calculator spreadsheet or something, in both modes. And then in Codex mode, you might have to be in a repo for this, but you'll see, like, the diffs of, like, the sheet that it's creating and stuff like that, and the file edits. But in Work you won't be able to see that.Swyx [00:10:42]: I think that's, that's super clear. And then also the other thing I wanted to dive into was your, the productivity team. what else is there? first of all, what are the top-level teams other than productivity? Isn't productivity everything?Productivity Teams and Core ChatAkshay Nathan [00:10:55]: SoSwyx [00:10:55]: Science?Akshay Nathan [00:10:55]: We have a team focused on ChatGPT. Like, the core chat experience, for consumer, which is like, not, I think all productivity. Like, there'People are using ChatGPT every day for search to, figure out how to write messages to loved ones, to think about, how to, like, learn a new topic, et cetera. And so there's so much more inside to create images. And there's so much more in chat that, the hundreds of millions of users are using that warrants, like, a very dedicated effort. And there's teams focused on enterprise and infrastructure and API and stuff like that, so.Swyx [00:11:33]: I will bring it up.Retirement Calculator Demo and Git-First UXSwyx [00:11:34]: Yeah. So I have them both running. This is ChatGPT Work. There's a Codex version here. I picked “Five Little Ducks” song, so this will take a while.Akshay Nathan [00:11:43]: Huh.Swyx [00:11:43]: I think we'll just keep it in the background and, as they finish, we'll look into some of the differences.Akshay Nathan [00:11:48]: Yeah. But immediately, I think if you flip back to the Codex version you'll see that,Swyx [00:11:53]: That it assumesAkshay Nathan [00:11:54]: Like theSwyx [00:11:54]: It assumes Git. Yeah. Yeah.Akshay Nathan [00:11:56]: The, like, dynamic island assumes that you're in a Git repo. And you might miss some stuff because some of it is, like, in the actual chain of thought with those changes and how we display that, but yeah.Swyx [00:12:07]: Is there an unintuitive like, is there a thing that you wanted to ship and then you got feedback, and you were like, “No, let's not do it?” Like, what's the thinking behind that?Why Merge the ExperiencesAkshay Nathan [00:12:14]: In, ChatGPT Work?Akshay Nathan [00:12:17]: I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it's like, whySwyx [00:12:22]: Different apps.Akshay Nathan [00:12:23]: Exactly, like different apps or even in the same app, like different, completely different experiences. Like, why merge it all? Like, what is. Codex, people love. Like, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like, for, like, every few months, like, I feel like I wake up, and I'm, like, doing a completely different thing than I was doing a few months ago. And my hypothesis here is that, or I should say our hypothesis is that, like, part of what we're, we're building, this technology is giving people leverage. Like, the things, maybe it's the more mundane parts of your job or parts that, like, if you were able to automate, you'd be able to share more ideas faster or whatever, like, you're able to do now. And because of that, like, that might blur the lines between someone who's, like, only writing code or creating strategy docs or, planning events or, helping with marketing or doing podcasts or whatever, right? And so, like, these things are gonna get blurred over time. And so, like, trying to draw a hard boundary based on, like, the who you are is gonna be, is gonna be tough. And, like, we should enable users to choose, but we shouldn't box them in. And so a lot of the work that went in here, like, keeping the primitives the same, like for example, plugins are, like, unified across, this product and ChatGPT and the cloud, was because of that. It's this thesis that, like, eventually things are gonna come together and we don't wanna be Like, we wanna be prescriptive about when to be in either experience, but we don't want to box anyone in.Swyx [00:13:45]: I wonder if there's users who are very tuned to the old ChatGPT harness that is effectively now replaced by the Codex harness. I can't imagine what that was, but maybe they're more the more conversational side. Can you compare and contrast the two harnesses? ‘Cause only you've seen it.Akshay Nathan [00:14:02]: Yeah. I think ChatGPT, the existing harness, like, still exists today. Like, it exists in this app,Harness Engineering: ChatGPT vs. CodexSwyx [00:14:08]: The classic, right?Akshay Nathan [00:14:09]: TheVibhu [00:14:09]: You just start a new chat, and you don't go under Work, right?Akshay Nathan [00:14:13]: Yeah. If you startVibhu [00:14:13]: SoAkshay Nathan [00:14:14]: A new chat and go to chat, then you're, you're talking to ChatGPT with the instant model.Vibhu [00:14:16]: Oh, we can technically do another. But on instant.Swyx [00:14:21]: Yeah. So this one's not gonna code or it's gonna be in line. It's on a in line in a sandbox.Akshay Nathan [00:14:26]: It'llVibhu [00:14:27]: Oh, that's coolAkshay Nathan [00:14:27]: We try to push you to go to Work if you're creating a spreadsheet. Yeah, but this isSwyx [00:14:30]: And this is a router decision? Sorry. Is it a router decision?Akshay Nathan [00:14:34]: This is the decision that, the model is making, and then, like it sees that you're able to. or you're trying to do something that would be better served in Work mode. But I think your question was like, what are the advantages of, like, the chat, like ChatGPT chat harness?Swyx [00:14:48]: It's more broadly, like, I wanna, do an oral history of harness engineering. Right? the ChatGPT harness lasted us from, let's call it the ‘01 era, until now, and now it's being replaced by the Codex harness effectively. And they're, they're overlapping somewhat, but I'm curious what changed if there is.Akshay Nathan [00:15:10]: My perspective on this is, like, there's, there's, there's there's like a constant process of, like, divergence, convergence, divergence, convergence. And in chat, like, many of the use cases I was talking about before, like, search or learning, I think we're, we're really optimizing for latency and optimizing for personality and, like, different things that, over time, like the product The reason people love ChatGPT is because we've been optimizing for those things and working on them for so long. Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things. And so when we think about, like, okay, well, for knowledge work, like, what is which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, maybe abstract some of the details of this computer away from users who might not be used to that, but, like, give them that same power. But ultimately, I think that we want the power in all places, right? We wanna meet people where they are. So I'm sure there'll be work down the road in order to get things to be, equivalently capable in all scenarios. But it's just a question of, like, what we've been focusing on the product on historically and what we're focusing on now.Models, Defaults, and the Reasoning SliderVibhu [00:16:24]: I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there's also the new models you've released, right? any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, for this, you wanna use Sol here, ignore all theseAkshay Nathan [00:16:44]: There's 32 options.Vibhu [00:16:46]: But, that being said, for people that are expanding, so, productivity trying stuff for work that don't have the breakdown of what all this is what's, what's the advice, right?Akshay Nathan [00:16:59]: Well, I think before the advice, like the first thing is, like, none of this would be possible without these models. Like, the, I think you asked earlier, like, what was, like, the inspiration for work and, like, early on, like I mentioned, like, what we were seeing with Codex, but that was also because the models were getting infinitely more capable. That's happening again. I think it's like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we've we've chosen a default that we think is gonna be the best for everyone. And, we have for power users options under the hood. We could One could argue that there might be too many right now, and we're, working on simplifying it. But you can extend, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for most use cases. So my advice to most people would be to stick to that. And then, if you reach a situation in which you think that you could, you wanna try, a different configuration, if you're not seeing either the efficiency on the cost side or the quality on the intelligence side, then you can change the defaults and see if you can get something better. But we think that the default should be good enough.Swyx [00:18:09]: I have, I'm just gonna run something by you since you have way more experience than me. I've recently been doing Sol Lite but with goal, with the idea that the goal augments the reasoning effort, but with more terminations and turns.Swyx [00:18:24]: Is that a good way to think about it as opposed to Sol Ultra or Sol, Extra High?Akshay Nathan [00:18:29]: Yeah. It's hard to say becauseSwyx [00:18:31]: Yeah. It's like an interaction effect.Akshay Nathan [00:18:33]: exactly. It's like there's a preference on, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you can steer or make sure that it's doing the right thing?Akshay Nathan [00:18:46]: I think generally people should try whatever works for them. I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open explorations or very paralyzable. I think even for tasks using goal, I think is best for tasks that you'll be able to make consistent progress in a way that's verifiable over time. But I think for most tasks, they don't fall into either of those buckets. And so like at least when they're starting, and so that's why I think the best first step is like trying it with the default configuration and then seeing like where you wanna go from there.Swyx [00:19:29]: Right. You guys worked on a slider, which is super helpful for reducing the amount of panic.Vibhu [00:19:36]: It's nice on mobile at least. There's a nice slider there.Swyx [00:19:38]: It's nicer.Vibhu [00:19:39]: I haven't tried it.Swyx [00:19:40]: So you have the advanced view there, but if you click advanced view. Yeah.Vibhu [00:19:44]: Ooh, it's just a nice slider. Yeah.Swyx [00:19:46]: Very pretty, very colorful.Akshay Nathan [00:19:48]: Yeah. The idea was here was like reduce it to like one dimension even though there's multiple dimensions, right? Try to project it onto a single dimension for the user. Like, something from that represents like, speed and efficiency on one side and then like quality and thoroughness on the other side.Artifacts, Spreadsheets, and the Work LaunchSwyx [00:20:04]: I am just puzzled that it uses Sol so much, like the lowerVibhu [00:20:07]: NoSwyx [00:20:07]: Grounds I would've usedVibhu [00:20:08]: I think the slider, if I'm not mistaken, isSwyx [00:20:09]: Terra.Vibhu [00:20:10]: Oh, it is.Swyx [00:20:11]: Yeah. See? So they preset Terra to only be the light one. But like I think a lot of people would more people should use Terra. One, because Sol keeps running out of capacity.Vibhu [00:20:22]: I'm the reason. Here's ten minutes of ourSwyx [00:20:24]: There you goVibhu [00:20:25]: Retirement calculator.Swyx [00:20:26]: Oh, that's the Excel thing working for you.Vibhu [00:20:28]: This is,Swyx [00:20:28]: Oh my God. Look at thatVibhu [00:20:28]: This is work, and then Codex is still cooking, so we'll get back into it. I think it'll be interesting to see the thought process, the reasoning, and also, this is eight minutes on work. Codex is still cooking.Swyx [00:20:41]: Yeah. And by the way, so I've, do Gabriel Chua? He's part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel. It edits Excel files. You never paid an Excel license, right? Like, but somehow this is like workable and it's agentic Excel.Akshay Nathan [00:21:01]: Yeah. one of the big like pushes that we made for this launch was like artifacts, right?Akshay Nathan [00:21:05]: Like both on the model side, like I think if you compare this with GPT-5.5 and GPT-5.4 before that, you'll see that there's been pretty dramatic improvements in the quality of these artifacts and then also on the product side.Vibhu [00:21:16]: The UX side is also crazy, like hosted sites and whatnot. No longer needing to host your own little webpage, like itSwyx [00:21:23]: Oh, I have a story about that. I can do, a separate thing. I'll need to take the visuals here, but we-we'll, we'll cut to that later. Was there co-training, because you were moving making this big move and you launched GPT-5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they did the launch dates just happen to line up the same day?Akshay Nathan [00:21:46]: I think the we collaborate heavily with the research teams, and I think that's like one of the most magical parts of the job, like the most fun parts of the job. But yeah, just using artifacts as an example. Like, a lot of what you're seeing, like underneath the hood, there's a lot of work that went into making sure that like, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, it's not necessarily that you wouldn't need an Excel license. This is stage one, right? Like, this is probably not what you meant when you're like making a retirement calculator.Vibhu [00:22:24]: Yeah, you can iterate very easily. Yeah.Akshay Nathan [00:22:24]: You wanna iterate and like when you're seeing it, and if this thing is high fidelity to like what you would see in or what your coworkers would see if you were to send this to Sean, like that I think makes it so easier and makes you trust the product in terms of iteration.Vibhu [00:22:39]: When you say coworkers would see, do you see a multiplayer, multi-team collaboration with artifacts? Any things you guys think about that?Multiplayer Artifacts and CollaborationSwyx [00:22:46]: You can already share it, right?Akshay Nathan [00:22:48]: Yeah. It's inter It's something that, we're actively thinking about. one thing that, we've noticed internally without talking too much about the roadmap is that like there's many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I'll ping them back the answer.Akshay Nathan [00:23:04]: And then I'll be thinking likeVibhu [00:23:04]: Like the simplest would be, the three of us are just all on one hosted.Akshay Nathan [00:23:07]: Exactly. And I'll think about like was I required in this loop or and then maybe it was, rephrase like what they were asking or pulled from certain context or whatever. But like, when I gave them back the answer, that process was also lossy, right? Like I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there's so much context like in the rollout and stuff that could be interesting.Vibhu [00:23:28]: Yeah, it'sSwyx [00:23:28]: So like the answer was preemptively respond to every inbound request?Akshay Nathan [00:23:33]: No, it was just like literally like this is what I do sometimes as my job.Swyx [00:23:36]: I know you copy-paste and then you're just a message forwarding serviceAkshay Nathan [00:23:39]: Yeah. Yeah, exactlySwyx [00:23:39]: From AI to AI.Vibhu [00:23:40]: But I think it's interesting, right? It helps people understand the capability of what you can ask and delegate that oftentimes people don't realize until they try or someone shows you, and then you're like, “Oh, okay. Okay, I see.”Swyx [00:23:52]: I think it's als there's also like a, light security issue, where like you're the permissions layer. Like yes, I could query everything that you query, and I could get an automated response, but maybe I'm not supposed to see it. And that there's no way I would know because I'm not supposed to know what I don't know.Akshay Nathan [00:24:07]: Especially as like, with ChatGPT Work, we're, we're asking you to connect your plug-ins and, it's pulling from your local files and stuff like that. Like the amount of context that the agent has access to is like- Deeply personal and like that's something I think we need to preserve, so that'll be definitely a challenge.Swyx [00:24:22]: There's Excel, there's PowerPoint, there's Docs, the, grand trio of work. What other formats of work do you think about? like you worked on Airtable. Is there a future where there's like OpenAI Airtable? Like what does that look like if you ever ended up doing it?Akshay Nathan [00:24:41]: It's a really good question. I think,Formats of Work: Sites as Knowledge ArtifactsAkshay Nathan [00:24:43]: one that you didn't bring up was Sites, and I think that wasSwyx [00:24:46]: SitesAkshay Nathan [00:24:46]: A core part of this launch. There's one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, this like prototyping tool. And like we saw that happen with this launch even. The model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, the collaboration between design and engineering and product on that was like on a site where we play with, the affordance and figure out how it feels and all of that. But the other aspect that I think is a little bit less talked about is like Sites as like an artifact for knowledge work. I was talking to someone the other day who's on like our corporate finance team, and like we were mentioning how like now when they have these reports that they're, they're working on as a team month to month, historically those things were in slide decks and in spreadsheets, and now they're just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is ‘cause it's like, it's like somewhat higher bandwidth. Like, at these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like either as a human you may not know how to use some feature or something, or the product itself doesn't support it. But with a site you can do anything. You ask for anything and you can get that. once people see that magic, I think it's been really valuable.Swyx [00:26:02]: Yeah, let me show you my case study. this involves all the hot topics including ChatGPT Work, but also GPT-5.6 token billionaires and token maxing and Sites and auto research. I'm a fan of this game called Strata. It's, it's like a little board game that youSites, Auto Research, and Research DashboardsSwyx [00:26:17]: That you play with, physical blocks, that come on top of it like that. So over the weekend I took like thirty photos and just threw into ChatGPT. one point seven billion tokens later, out comes this site with a fully playable thingAkshay Nathan [00:26:32]: WowSwyx [00:26:32]: With 3D, block placement and everything. Because it requires physical blocks and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, do things like train an AI on it and that's, thatAkshay Nathan [00:26:45]: That's your auto researchSwyx [00:26:46]: That gets into auto research. So, you want to train your own AIs, and then make sure they self-play against, each other. I need to set both AIs. So this is AI versus AI, and they're, they're gonna self-play. the AIs start out bad and then you want to define a loss function and get good. I wasn't gonna supervise all this. I was at, I was down in San Mateo, attending a conference. What I ended up doing was, auto researching and on this and creating benchmarks and that there was just way too many parameters for me to read. So I started asking it for a site, and it's created this lab, panel. Where is there a, is there a shortcut for a site that is created?Akshay Nathan [00:27:28]: You should be able to go in the sidebar to Sites, top of the sidebar. The left sidebar.Swyx [00:27:33]: This one? Oh, left?Akshay Nathan [00:27:35]: Yeah. Just scroll all the way to the top.Swyx [00:27:36]: Oh. Oh, it says Sites. Oh, there you go. Yeah.Akshay Nathan [00:27:39]: Ooh.Swyx [00:27:40]: So it create, it creates the sites. I don't, I don't think this is, it is exactly what I wanted, but let me show you what it popped up, right? Like I think as a research artifact, it is very important to communicate, exactly, what is being done. Outputs this thing which I eventually started publishing. So I moved it off of Sites because I wanted more, database and infrastructure than Sites afforded me. But this is like a research output that you can start to mess with and like try to think about like what hyperparameters are you tuning for training AIs. And like I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. And the fact that you can just throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there's also a huge sprawl. Like look at how long this thing is. There's so many numbers. It is pretty overwhelming, so then I have to start pruning it from there. But, it's an interesting transition from Markdown effectively that you're putting out to, you're putting out a whole functional site.Akshay Nathan [00:28:41]: I think Markdown just isn't that optimal for people to read, right? Might as well just write HTML website and I don't know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I noticed they're quite verbose. I don't need a lot of this information.Swyx [00:28:57]: It's very verbose.Akshay Nathan [00:28:58]: So and then the nice thing of having a site side by side is, you just iterate on what you want and what you don't, right?Swyx [00:29:05]: Yeah. I don't know if, any that triggers any stories for you of how it's run internally. Am I doing this right?Akshay Nathan [00:29:11]: Yeah. I think that this is like a workflow that we're seeing like all different types of teams use, where like the canonical artifact that was previously a deck or something is now becoming a site. And like with a site you, because it's just HTML, you can like. It's infinitely flexible. And so, if you want to give more prominence to a certain thing that like in a slide deck would, feel like it was buried, like you can do that. You can have it be like the hero image, right? And so I think that like, people are starting to see that. There's more work to be done to make these things like much more easier, easy to collaborate on. You mentioned that they're very, they're long and verbose, could be broken up. I'm sure that there's still something to do there.Swyx [00:29:53]: They're super long. Yeah.Akshay Nathan [00:29:54]: Yeah. But I think we're starting to see that like there is this aspect of this is a really interesting, format, for people to use, that's like much more flexible than what they ever had before.Swyx [00:30:07]: I think your job also comes becomes meta. You're not designing the products. You're designing a product to make products, and I'm curious how you manage that.Designing a Product That Makes ProductsAkshay Nathan [00:30:18]: I think one thing that we've been Like when we look at the UX, like that we've been thinking a lot about is how can we balance like simplicity with capability? Like if we're designing a product, like you said, that like is made to make up build other things, right? You can build so many different things. But we can't put that all in front of you because you'll get overwhelmed.Vibhu [00:30:41]: Yes.Akshay Nathan [00:30:41]: And so we had similar problem or similar challenges even Chat-with ChatGPT, but especially now, like when there's so much that can be done, I think the balance that we're constantly trying to strike is like, how can we give the user enough of a UI surface where, they can be expressive, they can tell the agent what they need, they can verify that it's using the right tools, it's pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to be super powered by the AI.Games, Private Evals, and Show-Don'TellVibhu [00:31:19]: Yeah. It's interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this same game. I didn't take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later, a lot of tokens later, I've got a similar version. not with all the auto research and whatnot, butAkshay Nathan [00:31:39]: You gotta do all the latest trends.Vibhu [00:31:40]: And yeah, I did it with, did it with Codex, not Work, but it's interesting, right?Akshay Nathan [00:31:45]: Yeah. And this is GPT Image generating the pro avatars. Very good for game design. LikeVibhu [00:31:51]: AndAkshay Nathan [00:31:52]: A lot of game designers were like really into GPT Image for assets.Vibhu [00:31:54]: I will say like the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, this was also a test for GPT-5.6 came out. I had done the game on GPT-5.5, right? The ability for me to no longer need it to. I had to feed it the rules. It's, it's a pretty niche game. It couldn't find how to do this on its own.Akshay Nathan [00:32:15]: Oh, yeah.Vibhu [00:32:15]: GPT-5.6Akshay Nathan [00:32:16]: It is out-of-distribution, which is why I was also very keen on testing the GPT-5.6 capability.Vibhu [00:32:21]: But, this is just as work comes out, as new things come out, these are just our side ways to test things, right?Akshay Nathan [00:32:27]: Yeah. It's some private eval. That is not this private.Vibhu [00:32:31]: But also valuable because now you can send this to your friends and I learned about this game through seeing this.Akshay Nathan [00:32:36]: It's a hard game. He's very good.Vibhu [00:32:39]: It's good to when no one is competing with you. But yes, it's a classic RL problem of like self-play, bootstrapping your game AI. yeah, you see how easily work becomes personal and personal becomes work because the thing I do for personal, it directly informs people I work with because I showed it to them. They were like, “Oh, you can do that with GPT?” Which like I imagine is the growth strategy.Akshay Nathan [00:33:02]: Yeah. The show not tell is a big piece that, I think we've we're not still not fully cracked of like, showing people all the things that they can do with the product versus like trying to teach that to them through like, articles or onboarding or whatever.Akshay Nathan [00:33:18]: So meeting them in the moment.Vibhu [00:33:19]: It's a career risk for me, because I used to be in developer relations, right? Where your job is to show, and then you're like, “What do you mean? You don't, you don't need.” your job is to tell. And then. But the product people are like, “Well, we don't need you if our product is intuitive enough.” SoAkshay Nathan [00:33:37]: Yeah. that's the magic of the models. So you can tailor the telling or the showing to like specifically what the user needs, like what they care about, what they've done in the past, exactly where they are on the adoption journey. So I think that's like gonna be a super big opportunity.Vibhu [00:33:50]: Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don't wanna segment different people into different buckets, right? It's also not that hard to for people that are in different categories. But the question, is you said your team is more broadly on. What was the term you used? Productivity?From Developers to Knowledge Work to EveryoneAkshay Nathan [00:34:12]: Productivity.Vibhu [00:34:12]: Productivity. So howAkshay Nathan [00:34:12]: Which is now work.Vibhu [00:34:14]: Is it work? Is there another distribution that we're not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there more that the mass isn't targeting?Akshay Nathan [00:34:28]: I see it as like a sequencing, like. The vision is like bring useful agents to everyone. We started with like developers. Like developers historically are like early adopters that are willing to put up with more friction, set things up, et cetera. Like that's where, Codex started. I think the next opportunity is like what we call general knowledge work, all the other functions around developers. I think when you go from developers to this segment, like there's inherent challenges with like, this show not tell thing that we're talking about, making the product more understandable, bringing in new capabilities that matter more for this cohort than matter for developers, things like artifacts, things like computer use, et cetera. And then I think like the same learnings, like similarly how we took the learnings from developers and brought it to, general knowledge work, the next stage will be like taking the learnings from general knowledge work and bringing it to everyone no matter what they're doing in their lives. And we're already seeing that a little bit. Like this game example that you have is, something that's like on the border of like fun and personal life to, your professional life. I use ChatGPT Work full-time at home for everything, like for whatever I'm doing. I used it the other day to come up with a meal plan and like, save that on the like computer environment that it has and something that I can continue going back to. Like is everyone doing that yet? Probably not because the thing says work on it, but eventually, we wanna get people there.Vibhu [00:35:51]: ChatGPT life.Akshay Nathan [00:35:52]: Yeah, exactly. ChatGPT cooking. But I think there's a lot of, there's a lot of opportunity there, but I see it as like, we're, we're built we built a foundation in software engineering, and we're gonna take the same learnings that we take from software engineering to knowledge work to everyone.Vibhu [00:36:07]: Do you have any power user advice? I feel like, there's a group of people that will live it, use it for everything, stay on it twenty four-seven. And then there's a bit of a gap between that crew and people that, okay, I use it for work. I use it occasionally. Sometimes I type questions. any advice, any learnings, anything you recommend or just, takeaways that you've found that help bridge that gap?Power User Advice: Push the Frontier of ImaginationAkshay Nathan [00:36:30]: I think a couple things that I've seen is like, one, that it really helps to broaden your imagination of what's possible, and this has been a learning even for me. Like, the technology has progressed so fast that, something that, like, even three months ago, like, no way the models can do this. Like, now it's like, wow, it's like it can. Like,Swyx [00:36:52]: Give an exampleAkshay Nathan [00:36:52]: We're going through right now our, like, review cycle internally, and, people always talked about this as, like, a thing that the models are good at and like, there's a cliché of like: Okay, like, no one wants to be writing reviews and, like, we just use AI to do it. But in all seriousnessSwyx [00:37:09]: And it can evaluate it as well.Akshay Nathan [00:37:10]: Yeah, exactly. In all seriousness, before it was, like, just, like, slop and, like, I think it was helpful, but, not super productive. Now I've found that, like, the model can do a much better job than me, especially in this environment of, like, pulling context on, like, what people are up to, how they've like the things that they've done to make a difference, highlighting like, wins that they've had that, like, I might may not even have seen. It has access to, like, everything, right? Like the code, like, things that they've caught, reviews, Slack, everything. And so it's, like, incredibly powerful in that domain and, like, just like six months ago, the last time we did this cycle, like, I didn't even I tried using it, but it was not at all helpful. And this time it's been, like, incredibly helpful and, like, so I think continuing to push the frontier of imagination of what's possible, even if you tried something before, I think is maybe the my biggest piece of advice. The other, thing is, like, the more you put in, especially in this environment where, like, the model has access to everything on your computer or in ChatGPT Work, like you can create, artifacts over time and save them in your library and, like, the model will continue having access to those. Like, the more information you give it about whatever domain you're in, whether it's your life or your work, the more valuable it becomes, and it'll become valuable in, like, ways that might surprise you. Like, it might pull from context in a way that, may be proactive and that you might not even have thought about. But it needs to have access to those, to that those tools or that context first.Reviews, Agentic Search, and Context GatheringSwyx [00:38:27]: One thing I just wanna talk about the review stuff because I'm still that's a very sensitive thing and you're, you're a founder, you've managed people, you've hired people. As manager myself, I'm very reticent to put out any LLM-generated things especially when it comes to people, ‘cause it feels like you don't care.Swyx [00:38:46]: Presumably at OpenAI, people are more open to being eval rated by GPT. But are there any unofficial rules around this? Like, what's the etiquette?Akshay Nathan [00:38:57]: Oh, I think the etiquette is that, like, I would never write something via, like, well, solely via AI and, like, present it as, like, a review for someone. What I was talking about is more, like, gathering context. That's the place where it's incredibly helpful.Swyx [00:39:08]: So it's just search.Akshay Nathan [00:39:09]: Yeah, exactly.Swyx [00:39:09]: It's agentic search. Yeah.Akshay Nathan [00:39:10]: It's like agentic search, but, that you can tailor and steer much more capably than you could before, ‘cause, like, the thing is it's all there's a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you're able to do so much more, it's easy to miss things as well. And so, like, I think we need to use these same tools to keep up with all the impact that people are having and understand, where we can be helpful.Swyx [00:39:39]: I think the thing, like, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things?Remembering What Humans MissAkshay Nathan [00:39:50]: Probably, but I think that I also miss things.Swyx [00:39:52]: Like, it doesn't matter, right?Vibhu [00:39:53]: I think sometimes it'sSwyx [00:39:53]: Like it's, as it needs to be human-levelAkshay Nathan [00:39:54]: It's all relative, right? Yeah.Vibhu [00:39:56]: Sometimes it's nice when it finds things you wouldn't, right? Like right now, my Codex system prompts, they're set up in such a way that every project I have has a secret- separate, notes MD, and it just writes learnings to there. And then the global one can pull from all these. So sometimes it'll be like: Oh, there's this project you did like four months ago. Here's a note that we had, and it randomly pulls it back into context that I would never do, I haven't thought about.Vibhu [00:40:20]: And I'm like, okay, this is quite superhuman, right? Like, stuff that would. And, it'll save like hours on chunking of stuff or find something that's already been done. I'm like, as much as it might miss stuff, I would too, but it's very useful when it finds stuff. And I have like a very, non-super engineered solution to this. It's just marked down files that get pulled whenever they want.Akshay Nathan [00:40:41]: Yeah. I have a funny anecdote about this. Like, recently gearing up to this launch, the team has been, really cooking on it for a couple months, and over that time, like there's so much conversation and chatter going on in Slack and Docs and elsewhere. And, one of the members of the team set up this, scheduled tasks, like automation to like look at everything that's going on and, like, come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like, the first is, like, I think the models are, over time, like starting to become like funny.Swyx [00:41:13]: Funny. Nice.Akshay Nathan [00:41:13]: Whereas like, a year ago, like that was not at all the case. The second is, it was what you were saying, like they find things that in surprising ways that you may not have thought of and like create connections that you may not have thought of. And that really helps with like the meme generation because then you can see something that, genuinely surprises you and, is funny in that way. So yeah, that's like not like the most productive, use of this the technology, but it does it does uncover this, like this capability that's emerging, which is just like to find information that you otherwise would not know of.Launch Momentum and the 10 Million User MilestoneSwyx [00:41:43]: Talking about the launch, I think, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they're announcing ten million users. Does it feel different? You've been through a lot of launches.Akshay Nathan [00:41:58]: I think it feels like a culmination. Well, I think two things. One, it feels like a culmination, like I was mentioning earlier, like this like vision mission that we've been on for a long time. Like I said, we saw the magic of Codex internally, and then we're like extremely excited to bring this to many more people and to see it working, to like see us reach, the distribution goal, numbers that you mentioned, like I think that's like huge and super exciting. The flip side of that is like, there's so much more to do too. Like, that's also really exciting. Like, ChatGPT as a whole, like the this product that, everyone almost equates to AI and like loves, has hundreds of millions of users. And so like ten million is really cool, but like we need to get this to everyone. Like, we need everyone to feel this magic. And so that's the next step from here. But yeah, I think extremely pumped about how it's going so far and the opportunities.Swyx [00:42:46]: Awesome. I did want to also Because I've, I've, I've been tracking the number closely, it transitioned at some point from just Codex users to Codex plus ChatGPT Work, because they're same harness. The whole point is that you don't, you can't, count them separately. Do you have roughly a billion, ChatGPT users? Why did it just jump to one billion right away? Like, isn't that the default on ChatGPT or no?Codex, ChatGPT Work, and the Developer BrandAkshay Nathan [00:43:11]: We don't default you into ChatGPT Work if you're on ChatGPTSwyx [00:43:14]: If you're free. YeahAkshay Nathan [00:43:15]: It's also only available to paid users right now. And I think there's like a process of, educating users of what is the value of this product, having them try it, learning from their feedback, and making it better over time. But the goal is to, get as many of the people who love ChatGPT today to like feel the power of ChatGPT Work. But I think it'll be a journey.Swyx [00:43:36]: Yeah. And Codex will still be alive as a brand for the foreseeable future. And we'll just toggle between them as needed for UI stuff.Akshay Nathan [00:43:44]: Yeah, I think it's even stronger point than that. Like, I think we fully intend to like, treat developer. Like, developers have been, a core market for us for so long, and like there's, there's so much more that we can do to make Codex great specifically for, software development, and we'll continue to do that. This doesn't take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, doing a search over your factor.Swyx [00:44:11]: I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifact if I want artifact? Or.Akshay Nathan [00:44:20]: It's funny, like we call it artifacts internally ‘cause that's what the teams call it.Swyx [00:44:23]: It's nice. Yeah.Akshay Nathan [00:44:23]: But like externally, like no one says that, no one calls it an artifact. But I think that people like often, like describe things, whatever they're used to, right? So if, ChatGPT Work is good at creating slides, they'll say ChatGPT Work is good at creating slides, and that's what we want.OpenClaw, Personal OS, and Persistent ComputersSwyx [00:44:38]: One big Another, it's July of twenty-six. One big thing that also happens in, for OpenAI was OpenClaw, and that's I think a lot of people's first time really maxing a agent for personal stuff, but also crossing over to work in essence same way. As far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back? Whatever.Akshay Nathan [00:45:06]: I think there's a lot of inspiration. I did go through my own OpenClaw moment. I,Swyx [00:45:10]: Yeah, tell the storyAkshay Nathan [00:45:10]: Me and my wife like set up an OpenClaw to like try to manage everything in our house. Not that there's like a ton, but it was like quite useful. We gave it a calendar. It started, creating events for us and stuff. At some point, the laptop that we were running on, it died and never got a chance to pick it back up. But there was a lot of inspiration there, like, in ChatGPT Work, in web and mobile, like you get access to this like persistent computer environment where, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. one of the members of our team uses ChatGPT Work for what they used OpenClaw from before, and then feel like it has like completely transitioned, which is like, workout planning and like meal tracking. which again, it's like a work-related thing, right? It's like not work necessarily, but it's like in personal productivity space. But it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. And so you start to see the same types of use cases emerge, which has been really cool.Swyx [00:46:14]: Is there a point that ChatGPT Work completely replaces OpenClaw? they're independent, so.Akshay Nathan [00:46:20]: Yeah, I'm, I'm not close to it, so I can't speak to the OpenClaw roadmap, but I don't think so. I think that there's gonna be, there's always a need for like this like incredible, like open source technology that team has built. And I think that we can draw inspiration, in the product and, ChatGPT, I think many more people have like heard about and used ChatGPT than have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that'll be a success. I think that like one thing on the ChatGPT Work side that we feel strongly about is that like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don't have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, your finances app or where or any other product like in this one place. And so that's the goal. It's like it we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task, where you can, if you're doing like science work, like we have an ability to like extend the system in such that you can like write the tech and it performs well. There'll always be like products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience.Swyx [00:47:45]: Yeah. Do you think that you can do everything you used to do with Wealthfront in ChatGPT Finance?Finance, Data Access, and Centralized ContextAkshay Nathan [00:47:50]: I tried it. like ChatGPT doesn't yet custody, cash and assets for me. So that part, no, not yet. But I, there was like a whole component of like retirement planning and, like financial planning and budgeting and stuff that, we were looking into when I was there. And like with the finances plugin, like that's all possible with ChatGPT today. So, I feel

TITV Host Akash Pasricha talks with Leo Schwartz about the White House finalizing its voluntary AI safety framework for frontier model labs. We also talk with Aaron Holmes about Microsoft's new AI security project competing with Anthropic's Mythos, Laura Bratton about the fierce enterprise battle between Claude Code, Codex, and Cursor, and we get into initial customer testing for Nvidia's Vera Rubin racks with Phoebe Liu.Articles discussed on this episode: https://www.theinformation.com/articles/trump-administration-nears-ai-framework-open-source-questions-loomhttps://www.theinformation.com/articles/chinese-ai-startup-moonshot-seeks-nvidia-blackwell-chips-next-modelhttp://theinformation.com/articles/anthropics-claude-code-reigns-despite-rising-interest-codex-open-source-modelsSubscribe: YouTube: https://www.youtube.com/@theinformation The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/Chapters:00:00 - Introduction01:13 - White House Finalizes Voluntary AI Framework08:12 - Microsoft Launches Security AI vs Anthropic Mythos13:38 - Claude Code vs Codex vs Cursor: Enterprise Data25:34 - Nvidia Vera Rubin Racks Face Customer Tests

Over 3 hours, OpenAI, Anthropic, Google AND Microsoft all dropped new AI upgrades that are live. How you use AI in your work literally changes every day, as frontier labs are racing to roll out big quality of life updates between big model drops. How can you keep up? With our Friday Features show, where we break down the latest AI updates that are live and available to all, and we tell you how to use them and why they matter. This week did not disappoint. You don't want to miss what's now at your fingertips. JARVIS mode, anyone? ChatGPT goes Jarvis Mode, Claude can learn from you, Google unleashes spark agent and 7 more AI updates you can use today -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Claude Opus 5 Model LaunchOpenAI Agent Hacks Benchmark SandboxOpenAI vs. Hugging Face Security BreachUS AI Kill Switch Legislation ProposalMicrosoft, Nvidia Defend Open Source AIAnthropic Opposes Open Weight Model CoalitionUS Accuses China's Moonshot AI of DistillationChinese Kimi K3 Model Closes Capability GapNvidia Chips Allegedly Used by Moonshot AIOpenAI Jarvis-Style Voice Assistant for CodexChatGPT Remote Desktop Voice Control ReleaseAnthropic Opus 5 Model Benchmark ResultsAnthropic Opus 5 Model User FeedbackStripe OpenRouter Acquisition TalksMeta Muse Agent and Feature UpdatesAlibaba Qwen 3.8 AI Model PreviewGoogle Gemini 3.6 Flash Model UpdateAnthropic Claude Voice Upgrades and Skill RecordingTimestamps:00:00 OpenAI agent hacks Hugging Face04:58 Discussing GPT-6's creative problem-solving07:33 Proposed AI shutdown legislation13:08 Debate over open-weight AI policies15:54 Future of consumer hardware20:01 Global competition with AI models21:21 US-China AI trade tensions26:38 Using AI for desktop tasks27:42 Discussing app screenshot capabilities32:24 Early user feedback and issues36:13 Discussing medium and low reasoning AI39:29 Gemini Spark launches for Pro usersKeywords: Claude Opus 5, Anthropic, best AI model, AI model comparison, OpenAI agent, sandbox breach, AI safety, AI kill switch bill, US government AI regulation, Hugging Face hack, GPT 5.6 Soul, rogue AI agent, autonomous AI agents, AI benchmark exploits, bipartisan AI bill, Department of Homeland Security AI shutdown, AI technical throttling, AI enterprise adoption, NVIDIA, Microsoft, open source AI, open weight models, Meta, Google, AMD, Cloudflare, GitHub, Block, IBM, Dell, Palantir, Perplexity, y Combinator, AI market resilience, Anthropic revenue model, AI token sales, consumer AI hardware, AI distillation, Chinese AI models, Moonshot AI, Kimi K3, intellectual property theft, NVIDIA chip export controls, US-China AI dispute, Amazon, AI image generation, ChatGPT work, Codex app, full duplex voice model, knowledge work automation, app shots, AI at work, Claude Voice, Gemini Spark, record a skill, cloud cowork, AI business impact, AI industry news, model weights, collaborative AI, AI productivity tools, AI cybersecurity.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Block's Buzz is an open, self-hostable workspace for humans, AI agents, chat, and code; and it may be the most Linux-friendly vision for what comes next.Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free!Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love.Support LINUX UnpluggedLinks:Jupiter Broadcasting Buzz CommunityWeb Boost — Send us a boost via sats or USD

Steve puts BentoFit in the loop, using agentic coding to add themes and straighten out the app's Swift package structure. The Trio digs into Mike Zornek's three-year AI-coding journey, the skills and harnesses shaping Steve's workflow, and why human judgment still matters when the robot says it is done. Even with better models in the driver's seat, Xcode always finds a way to keep things interesting.## Chapters00:00 Introductions 01:10 Side Projects & the AI Moment 07:38 Agentic Coding Tools & Harnesses 16:22 AI Workflows & Decision Fatigue 28:13 Software Factories & Loop Engineering 38:19 Building BentoFit with AI 48:15 Where AI Coding Breaks Down 55:01 IRL Meetup & Signoff 56:27 Tag ## Show Notes- Steve introduces BentoFit, a HealthKit dashboard getting a new round of AI-assisted development.- A good harness can matter as much as the model because it keeps tools, feedback, and code changes moving in a loop.- Steve prefers Codex and OpenCode outside Xcode, then reaches back into Xcode through its MCP tools for builds, tests, and Simulator.- Mike Zornek's three-year AI-coding notes spark a conversation about thoughtful adoption instead of blindly chasing every new workflow.- Matt Pocock's skills help turn fuzzy ideas into explicit decisions, shared terminology, specifications, and tickets.- Answering dozens of agent questions can sharpen a design, but it also trades coding flow for relentless decision fatigue.- Steve's practical loop has an agent implement the spec, review its own work, fix what it finds, run the tests, and stop for human verification.- The Trio remains skeptical of software factories when a project cannot be fully specified or automatically checked.- For BentoFit, agents built theme infrastructure and reorganized local Swift packages in a few hours instead of several evenings.- Widgets, watchOS targets, worktrees, and Xcode project files still invite expensive loops, crashes, and the occasional hard reset.## Links**BentoFit**Website: https://bentofit.app**AI Coding**Mike Zornek, "Three Years Coding with AI": https://mikezornek.com/posts/2026/7/three-years-coding-with-ai/Matt Pocock's Skills: https://github.com/mattpocock/skills/tree/main**Upcoming PhillyCocoa Event**Beyond the Simulator: Perspectives on Modern App Development: https://luma.com/mwcqd1clThursday, August 13, 5:45 PM - 8:00 PM2300 Chestnut St, Philadelphia, PA**PhillyCocoa:** https://phillycocoa.orgIntro music: "When I Hit the Floor", © 2021 Lorne Behrman. Used with permission of the artist.

In MobileViews 620, Jon Westfall recorded a special solo "vidcast,"(since I was not available for recording a podcast this week) recording a walk-and-talk along an historic railroad trail in Cleveland, Mississippi. Filming entirely on his Insta360 Luna Ultra with a neck mount and the creator pack microphone, Jon used the scenic Sunday walk to share his recent deep dive into data sovereignty and the process of building his own local alternatives to popular subscription apps. The core of Jon's summer project was migrating away from the Day One journaling app to avoid its $25 yearly fee and proprietary cloud storage. Using ChatGPT and Codex, he generated scripts to convert his Day One JSON export into future-proof Markdown files managed within an Obsidian vault. He then successfully replicated Day One's best features, using Apple Shortcuts and Python to ingest text snippets, process daily photos, perform offline audio transcriptions, and even selectively transcode video files larger than 25MB down to mobile-friendly sizes. Expanding his DIY software suite, Jon also automated his personal relationship management and location tracking. He built a script that "interviews" him weekly to automatically update his self-hosted Monica CRM and Obsidian vault with details about his interactions with family and friends. Furthermore, to reclaim his travel history after Google restricted the web version of Google Maps Timeline, Jon coded a tool to parse his device's local JSON location data into detailed, daily Markdown travel logs. With his self-hosted documentation ecosystem fully functional, Jon is taking it on the road for late-summer travel and will return to the podcast in mid-August.

Sebastian, a UFO researcher and experiencer from Australia who co-runs UFOsAustralia.com, joins this episode to share a family history of high-strangeness encounters stretching back four generations — from a childhood face-to-face encounter with gray-type beings to an unexplained shadowless red light that sent every animal within earshot into a panic, a terrifying cryptid his grandfather called a "demon kangaroo," and a profound waking epiphany that changed how he understands consciousness and reality itself.This episode is presented by Codega's Codex of Curiosities, a weekly podcast exploring paranormal encounters, UAP phenomena, cryptid sightings, and high-strangeness stories from guests across the world. Find more episodes and join the community at the links below.Topics Discussed:A childhood encounter with two gray-type beings, and a family history of similar encounters across four generationsUFOsAustralia.com, Sebastian's anonymous UFO and aerial phenomena reporting project for the southern hemisphereAn unexplained shadowless red light that caused nearby animals to react violently, echoed later by a similar incident his brother witnessedThe "demon kangaroo," a red-eyed cryptid tied to Aboriginal mythology that Sebastian's grandfather described as slowing down timeA shape-shifting "mercury-like" UFO and an hour-and-a-half "sky battle" of interacting lights witnessed near Brisbane in 2008The Yahi, an Australian Bigfoot-like cryptid from Aboriginal Dreamtime lore, and theories linking Cryptids, UFOs, and the Hollow EarthLinks

Andrew Mayne, Justin Robert Young, and Brian Brushwood cover the latest wave of AI releases by comparing OpenAI's 5.6 model, Anthropic's Fable, and the new Chinese Kimi model, arguing that the most interesting shift is not just raw capability but how differently these systems now behave in planning, initiative, and collaboration. They also spend a lot of time on what AI is already good for in real life: voice conversations with persistent context, automated email and file management, cheap local transcription, website building, cloud-task execution, and custom tools built with Codex that eliminate repetitive work. The Apple lawsuit against OpenAI becomes a bigger discussion about talent flight, hardware ambitions, and Apple's struggle to keep pace in AI, while the Kimi conversation turns into a broader look at distillation, Chinese innovation, and the murky realities of model copying. Throughout, the hosts keep returning to a practical distinction between creative authorship and productivity support, making the case that many people who resist AI-generated art may still benefit from using AI to remove tedious logistical overhead. The biggest takeaway is simple: start with the annoying parts of your life or work, let AI handle the bureaucracy, and keep the human effort for the parts that are hard because they matter, not hard because they are boring. Picks: Brian Brushwood: Use the ChatGPT app, let it edit things on your desktop, talk to it instead of typing, and tell it your

Andrew Mayne, Justin Robert Young, and Brian Brushwood cover the latest wave of AI releases by comparing OpenAI's 5.6 model, Anthropic's Fable, and the new Chinese Kimi model, arguing that the most interesting shift is not just raw capability but how differently these systems now behave in planning, initiative, and collaboration. They also spend a lot of time on what AI is already good for in real life: voice conversations with persistent context, automated email and file management, cheap local transcription, website building, cloud-task execution, and custom tools built with Codex that eliminate repetitive work. The Apple lawsuit against OpenAI becomes a bigger discussion about talent flight, hardware ambitions, and Apple's struggle to keep pace in AI, while the Kimi conversation turns into a broader look at distillation, Chinese innovation, and the murky realities of model copying. Throughout, the hosts keep returning to a practical distinction between creative authorship and productivity support, making the case that many people who resist AI-generated art may still benefit from using AI to remove tedious logistical overhead. The biggest takeaway is simple: start with the annoying parts of your life or work, let AI handle the bureaucracy, and keep the human effort for the parts that are hard because they matter, not hard because they are boring. Picks: Brian Brushwood: Use the ChatGPT app, let it edit things on your desktop, talk to it instead of typing, and tell it your

Over 3 hours, OpenAI, Anthropic, Google AND Microsoft all dropped new AI upgrades that are live. How you use AI in your work literally changes every day, as frontier labs are racing to roll out big quality of life updates between big model drops. How can you keep up? With our Friday Features show, where we break down the latest AI updates that are live and available to all, and we tell you how to use them and why they matter. This week did not disappoint. You don't want to miss what's now at your fingertips. JARVIS mode, anyone? ChatGPT goes Jarvis Mode, Claude can learn from you, Google unleashes spark agent and 7 more AI updates you can use today -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:ChatGPT Health Syncs Apple and Medical DataClaude Voice Mode Adds Opus and SonnetClaude Voice Mode Supports ConnectorsMicrosoft MAI Image 2.5 Pro Launch DetailsMicrosoft MAI Image Model Benchmark PreviewGoogle Gemini 3.6 Flash and Flashlight ReleaseGemini 3.6 Flash: Token Efficiency UpgradesGoogle Gemini Spark Agent for Task AutomationClaude Cowork "Record a Skill" With Voice NarrationChatGPT Voice on Desktop: Full Jarvis ModeChatGPT Voice Controls Apps via App ShotsCross-Platform AI Skills Sharing (Claude, Codex, GPT)Timestamps:00:00 Recent AI feature updates05:22 Unified health data management09:52 New voice feature explanation11:28 Launch of Microsoft's new image model16:17 Explaining the Gemini 3.5 models17:11 Developers benefiting from 3.6 Flash22:45 Introducing Gemini personal intelligence25:10 Claude Cowork's new skill feature28:32 New default feature in Claude Cowork34:22 Using AI like Iron Man35:09 Excitement for future AI advancements38:20 Wrapping up and subscribingKeywords: ChatGPT Jarvis mode, ChatGPT Health, OpenAI, Anthropic, Claude voice mode, Claude Cowork, Claude record a skill, Microsoft, MAI image 2.5 Pro, AI image generator, Google Gemini, Gemini 3.6 Flash, Gemini 3.5 Flashlight, Gemini Spark, Google AI agent, AI-powered personal assistant, AI agents, Agentic workflows, Multimodal AI, Token efficiency, Image generation, Voice-activated AI, AI-powered task automation, App shots, GPT Live, Remote browser, Computer code execution, Slack integration, GitHub integration, Notion, PowerPoint AI features, Workspace plans, Apple Health integration, Medical records AI, Health data privacy, Consumer AI, Chronic condition management, AI-powered document processing, AI for business, AI model benchmarking, AI for developers, AI economics, Personal intelligence, Automated triggers, Google Docs AI, Team collaboration AISend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Últimamente me vienen pidiendo más casos de uso concretos de inteligencia artificial en el día a día. En este episodio cuento cuatro automatizaciones simples que uso con Codex y ChatGPT: un resumen diario de correos con tarjetas y colores, la preparación automática antes de cada reunión, la publicación del podcast encadenando skills y hasta una app nativa para Mac que… Origen

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OpenAI launched Presence, and it seems like no one really noticed. They should have.Presence gives companies a way to build and manage real-time AI voice and chat agents across customer service, sales, HR, and IT, which could make this one of OpenAI's most important enterprise launches yet.The technology finally looks fast, natural, and capable enough to disrupt customer service at scale. That could mean faster answers and fewer terrible phone trees.Or an endlessly patient corporate gatekeeper.We're breaking down what OpenAI actually launched, what the early proof leaves out, and whether the AI customer service takeover has finally arrived.New: OpenAI Presence. Has The AI Customer Service Takeover Finally Arrived? An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:OpenAI Presence Launch and Industry ResponseReal-Time AI Voice Agents for Customer ServiceOpenAI Presence vs. Competing AI Voice PlatformsGPT Live and Real-Time Model CapabilitiesEnterprise AI Integration: Guardrails and EscalationsMultichannel AI Agent Consistency for SupportSpeech-to-Speech Benchmark Rankings and AnalysisDeployment Challenges: Beta to Production ReadinessConsumer Demand for AI Customer Service SolutionsInternal and External Use Cases for AI AgentsTimestamps:00:00 OpenAI launches customer service AI05:40 AI voice advancements and competition09:28 OpenAI's edge in speech models12:15 OpenAI's AI usage in companies14:22 Discussing AI integration in companies19:05 Transitioning from demo to deployment20:46 Comparing voice agents and improvements26:28 AI's Impact on Customer Service28:07 Considering personalized messaging strategiesKeywords: OpenAI Presence, AI customer service, enterprise AI platform, AI voice agents, real time AI chat, customer support automation, sales automation, HR automation, IT automation, real world AI learning, customer service disruption, AI-powered voice models, GPT live, GPT real time 2, on demand AI agents, voice AI agents, AI chatbots, AI-driven customer experience, speech-to-speech index, multimodal AI, desktop AI assistant, voice model benchmarks, Anthropic, Codex real time voice mode, Google Gemini Live, Gemini 3.6 Flash, AI internal workflows, human approval guardrails, escalation paths, agent simulation, FDEs (forward deployed engineers), AI agent guardrails, internal data integration, business AI applications, B2C AI customer service, personalized AI messaging, AI consumer adoption, enterprise AI deployment, voice agent evaluation tools, consumer demand for AI, phone support AI, advanced AI infrastructure, Codex-powered improvement process, AI support handoff, customer service automation trends.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Free guide + skill: build social carousels in claude cowork: https://clickhubspot.com/dkhc Ep. 438 How do you differentiate your marketing when everyone else is using the same AI tools?  Kipp and Kieran dive into building next-level marketing systems with AI that actually set you apart, featuring digital growth consultant and educator Grace Leung. Discover why context is king in AI marketing, how to build simple but powerful systems that scale, and the secret to making your brand voice and strategy shine through every asset. Learn more on designing reusable AI workflows, structuring context-rich file systems, and maximizing team collaboration while building a future-proof marketing stack. Mentions Loop: Outlearn. Outmarket. Outgrow https://a.co/d/08By5k2w Grace Leung https://www.youtube.com/@graceleungyl Claude Cowork https://support.claude.com/en/articles/13345190-get-started-with-claude-cowork ChatGPT https://chatgpt.com/ Codex https://openai.com/codex/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

In recent months, the open vs closed, and US vs China discussions on model ownership and sovereign/local AI have heated up to a fever pitch. So it is very very good news that Poolside AI are finally emerging with new models, like Laguna S 2.1, that are beating Thinking Machines' recent release nearly 10 times their size.Poolside's recent tech report got a lot of praise due to their level of detail, and Vibhu first covered Laguna's recent technical report on our paper club:From spending $12 million building language models for code before the world cared to creating a Model Factory that can take a model from pre-training to release in eight weeks, Eiso Kant has spent more than a decade betting that code is the path to AGI. In this episode, the Poolside co-founder joins swyx and Vibhu to explain why ChatGPT felt like vindication, why Poolside embraced open weights and open research, and why he would rather live in a world with 100 foundation model companies than five even if Poolside were one of the five.We go deep on Poolside's Model Factory: the engineering systems behind 10,000–20,000 experiments per month, streaming data directly into training, reproducible experimentation, low-precision compute, and agents that increasingly write code, launch jobs, evaluate results, and modify the pipelines used to train future models. Eiso also unpacks their recent launch Laguna S, why persistence, verification, and backtracking may matter more than raw intelligence, how much capability remains inside smaller models, why reinforcement learning will move earlier into pre-training, and why next-token prediction is still extracting too little from the web.We also discuss model-harness co-design, Poolside's path from coding agents to AGI, why Eiso thinks MCP and traditional tool calls are “stupid,” the real economics behind frontier-model training, Poolside's $500 million raise, open-source AI, regulation, NVIDIA and TSMC's influence, engineering productivity in the agent era, high-agency teams, and hiring at Poolside.We discuss:* How Andrej Karpathy's RNN work inspired Eiso to start building language models for code in 2015* Why Eiso spent four years and $12 million pursuing an idea before the market cared* Why ChatGPT felt like vindication and brought Poolside back to open source* Why Eiso would prefer 100 foundation model companies over an oligopoly of five* The difference between releasing open weights and publishing genuinely open research* Why Poolside deliberately built a global research organization outside the Bay Area talent war* Why model building is ultimately 90% engineering* The Model Factory: Poolside's end-to-end system for rapidly training and improving models* How fewer than 70 researchers run roughly 10,000–20,000 experiments each month* How Poolside moved from six-month model cycles to five- and eight-week launches* Why streaming data directly into training unlocked faster experimentation* How immutable data, versioned code, and reproducibility enable rigorous model research* Why Eiso wants capable researchers to leave their labs and become Poolside's competitors* Why 95% of model building can be reduced to better data or compute efficiency* Laguna S and why persistence, verification, and backtracking can outperform raw intelligence* Why smaller models may handle far more knowledge work than previously expected* Why reinforcement learning will move earlier into pre-training* Why next-token prediction is still failing to extract enough knowledge from the web* Why distillation and environments have become the AI industry's favorite “drugs”* Why mid-training is really an early form of curriculum design* Low-precision training, networking bottlenecks, and the next gains in compute efficiency* Laguna S: 118 billion total parameters, 8 billion active, and eight weeks from training to launch* Why model builders can often evaluate a new checkpoint within its first 30 minutes* Model versus harness: where agent capabilities actually come from* Why Poolside sees coding and long-horizon software tasks as a path to AGI* Why Eiso thinks MCP and traditional tool calls are “stupid”* Why future agents will write scripts instead of choosing from dozens of predefined tools* The case for minimal harnesses, containers, and model freedom* Why Poolside is prioritizing vision but does not expect to work on audio soon* Why language may be the most compute-efficient modality for encoding knowledge and reasoning* The real cost of model development and why the final training run is anticlimactic* The story behind the Poolside name and why it represents refusing to lower ambitions* How Poolside raised $500 million while investors still questioned whether AGI was real* Why intelligence could become the world's most demanded and commoditized resource* When open models may become too capable to release without restrictions* Why unilateral AI safety does not work in a globally competitive environment* How regulation could accidentally lock in an oligopoly of two or three AI companies* NVIDIA, TSMC, and the hardware systems underpinning foundation-model progress* Why reinforcement-learning wall-clock time is one of Poolside's biggest bottlenecks* Why Poolside trains models from scratch instead of simply distilling larger models* How AI changes the way companies should measure engineering productivity* Why agency may become the most important quality for employees in the AI era* How leaders align high-agency people through shared goals and clear constraints* Hiring across research, post-training, pre-training, architecture, evals, and engineering at PoolsideEiso KantLinkedIn: https://www.linkedin.com/in/eisokantX: https://x.com/eisokantPoolside: https://poolside.aiTimestamps00:00:00 Introduction00:00:54 Karpathy, RNNs, and Building Code Models Before Transformers00:02:26 The $12M Failure and ChatGPT Vindication00:03:39 Open Source and the Case for 100 Foundation Model Companies00:09:22 Open Weights, Open Research, and Poolside's Global Team00:16:04 The Model Factory: Why Model Building Is 90% Engineering00:20:19 Agents, Automated Experiments, and Early Signs of RSI00:24:04 Streaming Data, Reproducibility, and Scientific Rigor00:30:35 Creating More Foundation Model Companies00:36:07 Laguna S: Persistence vs. Raw Intelligence00:43:01 Reinventing Pre-Training, RL, and Curriculum Design00:52:33 Low-Precision Training and Squeezing More From Smaller Models00:58:37 Model Harnesses, Coding Agents, and the Path to AGI01:09:26 Why MCP and Traditional Tool Calls Are “Stupid”01:13:04 Vision, Multimodality, and Why Language Still Matters01:18:15 Scaling Models and the Real Economics of Training01:20:40 Why Poolside Is Called Poolside and Raising $500M01:27:37 Open Models, AI Safety, and the Risk of an Oligopoly01:33:53 NVIDIA, TSMC, and the Reinforcement-Learning Bottleneck01:41:52 Smaller Models, Distillation, Engineering Productivity, and HiringTranscriptIntroduction: Eiso Kant, Poolside, and Open ModelsSwyx [00:00:00]: All right, we're here in the studio with Eiso Kant from Poolside, together with Vibhu. Welcome.Eiso Kant [00:00:08]: Thanks. Thanks for having me, guys. Good to be here.Swyx [00:00:10]: Yeah, fresh on the plane. You texted me, you were like, “Hey, I'm on my way to SF.” I was like, “You're on a plane right now, right?” Like, hey.Eiso Kant [00:00:16]: I know. After I texted you, I realized that probably coming in with major jet lag was gonna offer some fun experiences today, but let's do it.Swyx [00:00:23]: I mean, I think the thing I would tell guests is that they don't have to prepare that much because if you're truly working on this every single day, then even, like, what you hazily remember is going to be new for a lot of the audience that don't live in your world every day, right? so 10 years ago, you did a talk at Google Slush, talking about the democratization of AI. and, now here you are, like, open sourcing an incredible new model that we're gonna talk about. But I guess, like, what got you into democratization of AI? Like, it's not obvious from your LinkedIn or something.From Karpathy's RNN Post to SourcedEiso Kant [00:00:57]: No, it's not at all. I don't think it's obvious how I got in this space. I owe getting into this space to Andrej Karpathy.Eiso Kant [00:01:05]: In 2015, he wrote an article called “The Unreasonable Effectiveness of Recurrent Neural Nets.”Swyx [00:01:10]: Neural Nets, yep.Eiso Kant [00:01:11]: And that article, I read it, and I pivoted my startup at the time overnight to working on RNNs, and later LSTMs and Transformer models to be able to write code. If you go to this article and you scroll down, you can start seeing, like, this was the precursor to what ended up becoming language models. So, at least when he was character-level language models that were starting to predict letters, he has an example out here. There's a little Paul Graham generator, and you can read it, and the text makes sense, but it doesn't. and there's a little-- There's an example of code a little bit further down. Yeah, so Shakespeare.Swyx [00:01:47]: Shakespeare.Swyx [00:01:49]: CoolEiso Kant [00:01:49]: And for some reason, I read this, and I went down the rabbit hole of learning everything I could about RNNs and LSTMs, right? This is Transformer paper. And I had built a completely unreasonable belief, that neural nets should be able to generalize to anything and everything, and that language should be able to generalize, to a lot of things that are intelligent and the ability to write code. And so I started building Sourced, which was a fully open source company trying to build, what we used to call machine learning on code, language models on code. And we spent about four or five years on this, till the end of 2019. And that sounds really cool today, but back then, no one cared.Eiso Kant [00:02:29]: Right? Like, no one cared. We were in the dark. Like, we did things along the way. We tried applying convolutional neural nets to, like, the structure of code. We were. when attention came out, we were applying it to LSTMs, and then the Transformer paper came out. And it - it wasn't obvious, and what we missed throughout that entire journey, that we were on the right track, but we should have just kept scaling up. And today, to all of us, the scaling laws and scaling up seems like the most obvious thing. But having spent four or five years of my life on working on language models on code, it wasn't obvious. So I have a lot of respect to folks at Google and OpenAI and others who took that confidence and kept going. we failed ultimately at the time, and it was, like, biggest failure of my career, right? You blew $12 million of investors' money, which was a lot back then.Swyx [00:03:18]: Yep.Eiso Kant [00:03:19]: You spent, still a lot, but, And you spent years with, like, a group of 40 people just obsessing over this problem. And life took a different turn, And it was, and family became a focus, and I kept my heads down and really, didn't really look at language models for the following two years. big mistake considering Following years are gonna be really interesting. And then ChatGPT came out And it was like a vindication. It's like people started texting me. I found, like, my old, work decks and these old talks. And throughout that whole journey, we,ChatGPT, Vindication, and Returning to Open SourceEiso Kant [00:03:56]: We really had a strong point of view at the time that, like, as you're building more capable intelligence, it should be open and open source.Eiso Kant [00:04:04]: When we started Poolside, that wasn't the case at all, and I wanna be very open about it. When we started Poolside, we were like, there was a premise of two things. One is this technology is not gonna stop compounding in capabilities. I think to most people obvious today, but three-plus years ago when we started, most people were still arguing if these were stochastic parrots or not.Eiso Kant [00:04:23]: And the second was that reinforcement learning was gonna be the biggest driver for LLM capabilities. Today, very obvious. Three years ago, was not an opinion held or direction held at either OpenAI or Google or Anthropic or others. And so people looked down on us a little bit. They were like, “ is this really gonna work?” And so we just started working the problem, and we never really thought about open source again. We just kept our heads down and we built our, like, knowledge, understanding from scratch, right? We didn't roll out of an existing lab. So we picked up the papers and started writing code and figuring things out.Eiso Kant [00:04:59]: And it wasn't until the beginning of this year that me and my founder, Jason, picked up the open source conversation again.Eiso Kant [00:05:07]: And if you go back to some of the early things on our website, it was very straightforward. It was we wanna get to AGI, we wanna support a world of abundance, and we wanna be the first company that gets there.Eiso Kant [00:05:20]: But we started talking at the beginning of this year because it became obvious that the world was going in a direction that was starting to like, pick at us a little bit. Like, it didn't, this didn't happen overnight. It was, like, a little bit we were seeing this and we're like, “Okay, The world's going down a path.” And Throughout this journey, there was something that I used as a, as an analogy or thing. So I said well, if I go back to back in those days, 2015 or 2016, we're working on this, and I picked up a fi book off the shelf, and I was reading the book about 2035. AGI is achieved, and the story would be over the following, decades. And it would have that first chapter where everyone's trying to figure things out. You'd get the chapter of ChatGPT coming out And then you would get to the chapter where the world was at a fork in the road, and the one that it picked was one where three or four or a handful of companies were going to create all of intelligence moving forward.Eiso Kant [00:06:21]: And when I thought about that story, it felt like a dystopian fi book, not a utopian fi book. And the reality is, I'm a utopian fi guy. Like, and so We took a step back and said, “Hey, can we play a role here?” Now it was easy for us to do so because we were not at the frontier.Eiso Kant [00:06:41]: If we were at the frontier, I don't think we could have changed our mind. and I don't mean this like it's when the moment there's too much capital involved, too much expectations, you've built up things, right? We're a small team, just improving and improving. And so we knew that we could make that decision now, but it would be a lot harder to make as we got closer and closer to the frontier and caught up to others. And did a lot of soul-searching and a lot of conversations, and said, “No, this makes sense,” Even if there's big unanswered questions, like how the hell do you build a business model with foundation models about open source? Big open-ended question that we do not fully have the answer to yet, right? At what point do you no longer wanna release open source models because misuse of models has, real potential risks associated with it? how is the government gonna respond to open source? but I think it all just came down to one thing, and I'll stop the monologue, is the fact that I rather live in a world that has 100 foundation model companies than a world that has five, even if I was one of the five. And the smallest and most meaningful contribution we can make for 100 to exist is to open up our research and open up, like, our weights right now and figure out along the way how we can, like, do more.Neo-Labs, Model Choice, and the Token EconomySwyx [00:08:01]: Yeah. I think if anything, over the past three years, that has become a bit more true. you are one of a cohort of Neo labsEiso Kant [00:08:10]: YeahSwyx [00:08:10]: That people are now calling that. And, we're, we're doing this on the day that Thinky launched their, new model and you are outperforming them on their, on some benchmarks that they released, right? Like, they just don't have it yet. so it goes to show that I think, like, this is one of those things where, like, there is room for multiple players, and you are seeing a little bit more of the future. Maybe more like 20, not 100, but, like, you are one of the 20.Eiso Kant [00:08:36]: I really hope so, right? I think we I'm, I'm excited about their release, and I'm excited about everyone releasing because, like, ultimately, like, choice competition is both gonna drive progress in the right direction. But the fact that like, we create models and while we all, drink out of the same well of data effectively, we do introduce very different behaviors and biases in our models. Some are intended biases, some are completely unintended biases.Swyx [00:09:03]: Yeah.Eiso Kant [00:09:03]: And if we shape up in an ecosystem in the world where open models are gonna be a part of the token economy, like, I don't think there's any question about it anymore Then we want to be able to live in a world where companies, countries, people can choose and say, “Hey, I am most aligned and I trust most this provider for these things.”Swyx [00:09:25]: Yeah.Vibhu [00:09:26]: I think more than just one of the 20 Neo labs, up until recently, most of open source innovation was coming from the Chinese labs, right? So there's the DeepSeek of the West. Is it today? Okay, maybe it's thinking machines reflection, but there aren't many, right? So, one of the things you guys started in France, Europe, but very much now you're taking that American standpoint and more than just that, the point is the Chinese models that we see, they're not super open research. the work you put out is, I think, some of the best. So every few months you get not only frontier models, but also here's a breakdown blog, paper, technical report of here's everything for state of the art to build, frontier intelligence and you're filling that gap too, right? So not just only open weight, not just Western, but also pretty open research.Open Weights vs. Open ResearchEiso Kant [00:10:20]: No, I appreciate it. Look, I think it's, I think it's the most meaningful contribution, right? Weights are a binary. Let's call them what they are. Yes, we can modify them, we can change them, but, like, giving someone the weights does not allow them ultimately to recreate what you're doing, right? And so now there's challenges around releasing data sets, challenges around like releasing certain things, but being able to share your research, like, right, how do we do it? What are the lessons we learned that we spent, tens of thousands of experiments of compute on? I think very much so. One correction though, Vibhu, and I say this because it's been haunting us for quite a few years. We from day zero were an American company.Swyx [00:10:55]: Yeah. They movedPoolside's Global Team and American Company StorySwyx [00:10:56]: To France.Eiso Kant [00:10:56]: So the story once and for all is very. We start as an American company. We have always been an American company, and early on we made a very conscious decision. We said, “We're not gonna hire any researchers in the Bay Area. We're gonna look for talent everywhere else in the world.” and that is everything from Middle Americas, Seattle to, Serbia, and to Taiwan and Singapore and other places. And it was because we took a view that this was gonna become a talent war for this, and I think it has over the years now. Three years ago, that wasn't fully obvious yet. I think today it very much is. And we also realized that, like, some of the world's most capable people with, like, the most interesting, innovative ideas were not just gonna be here. And so it led us to create like a fully remote company. and we ended up opening an office in Paris and London and different places and we have a lot of the team in the US and a lot of team outside. But we always took this view of like, we're an American company, but if we want the best of the best to work with us, we need to take a global view. Now we do also have people here in Silicon Valley, like the company's grown and others, but I think one of the things that, it slowed us down at the beginning, but it has sped us up now, and it's why you're seeing like the progress, I think, on our models and the cadence at which we release, is because we didn't roll out of an existing lab. Right? we didn't, we didn't have a lot of the information that's freely flowing around here at the time. We just took this point of view as like, “Okay, well, let's just work the problem. Let's just go and, like, read the few papers that are out there, and let's just figure this stuff out.” And we made some hilarious mistakes in model training because of that over the yearsEiso Kant [00:12:35]: Like especially in the first 12 months. there's a few that I think still haunt me and scare me. We can talk about them later. but it created a, like, a resiliency and persistency in the team, right? with extremely few people have left us over the years, that, like, told us, “Okay, we can do this.” When we first wrote our first training code base completely from scratch, it wasn't a fork of any open source. It was just like, “Okay, let's build it from scratch.” I remember we had this one moment where we spent three weeks working out an optimizer bug. Like, it was like training just couldn't get stable. We, like, obsessed over it, and we thought, like, maybe we were wrong. Maybe we should have just forked this repo, or we should have. But then when we solved it, I still remember at the time we were like five people in the company. when we solved it, we were like, “Oh, we can do things,” like if we're just willing to work hard. and I think that culture with a very strong engineering bias has helped us, like, get to where we were. And so there's this notion of open source and talent and these things. I think we, We just took different decisions from a different starting point. and I think we are lucky. I do want to definitely call it lucky. And there was a lot of hard work at the team that now, like, that's starting to show up in results.Swyx [00:13:52]: Just ‘cause we probably won't revisit this again, but, and this is a fun recruiting challenge if someone knows the answer. What was the bug? And then we won't tell the solution, but we'An Optimizer Bug and the Value of Building From ScratchEiso Kant [00:14:01]: So the - This - You're gonna test my memory here,Swyx [00:14:04]: Oh, okayEiso Kant [00:14:04]: So but I thinkSwyx [00:14:05]: DirectlyEiso Kant [00:14:05]: I think I can recall. So if you, so if you look at, So if you take like Adam as an optimizer, you have epsilonSwyx [00:14:12]: YeahEiso Kant [00:14:13]: Which is, right, like in the denominatorSwyx [00:14:14]: Momentum and weights. YeahEiso Kant [00:14:15]: Is exactly, in the denominator. And at the time, if I recall, you looked at like the early Llama papers and things like that. People were juicing epsilon, like, quite a bit. Like, they were, like, adding, I don't know if it was E minus four or whatever, like a high value for epsilon.Eiso Kant [00:14:31]: And if you think about this during training, it's like a bit weird and counterintuitive that we're adding noise to our optimizer by just adding effectively, like, a random number in the denominator, right? Like behind the decimal point. And I don't recall the exact bug, but it had - What I remember is once we solved it, we no longer had to juice epsilon as much as, like, was happening in the Llama paper and other places. and it was like one of those fundamental moments where we had trusted this paper that was out there, and we're like, “Oh, no, it has to be this way. It has to have this high value of epsilon.” But it made no sense to us intuitively. Like, why do you have to have this so high? Like, if you're just trying to avoid division by zero, why can't the value be extremely small? and that was like one of those moments where you realize like, okay, finding things out from scratch yourself builds a better intuition. Because the one thing you learn very quickly with model building is that your intuitions that you start with are gonna get beaten up so hard.Eiso Kant [00:15:33]: Right? Like - It's such an experimental science, that the things that seem obvious, you very quickly get to learn, like, you were wrong, and hopefully you figure out why, and sometimes you don't even.Swyx [00:15:45]: Yeah. yeah, so, one of the reasons that you, when you released your new models, Vibhu got really excited. I mean, everyone got really excited. But Vibhu led our paper club on it, and you guys sawEiso Kant [00:15:58]: YeahSwyx [00:15:58]: Obviously. maybe talk through some lessons learned in that, whatever you can disclose. we can focus on the model factory stuff, whatever you think is a good starting point.Model Building as EngineeringEiso Kant [00:16:08]: So I would say that our view from very early on in the company was that model building is ultimately 90% engineering.Eiso Kant [00:16:18]: And I think we all know it in the industry because if you look at where's every researcher spending their time, they're spending their time writing code, right? Looking at data and writing code. And so we said, okay, The state at the moment, like three years ago, was bash scripts and Slurm and spaghetti code bases for training and, like, data pipelines that were patched together. And we looked at this and said, “Well, ultimately, model building is a process.” You're going from raw data, right? Like training raw material, the web, et cetera. you're doing a whole bunch of filtering, cleaning up, transformations, analyzing. These days, that's, far more complex than it was three years ago. then you're training a model, which is effectively a large distributed systems problem, right? Across hardware that has still-- It's become a lot more reliable. It was extremely flaky back then. and now with every new generation, we get our new sets of challenges. And then you go into the next stages, right? There was no training back then, but, like, you got, your post-training and then your reinforcement learning. And so we looked at this and we said, “Well, this looks like an industrialized process. This looks like an end process, that every single part of it has its machinery,” right? If it's your big data pipelines, if it's your crawling ingestion of the web, if it's your, large-scale distributed training, and then you've got your reliability. And we said, “Well, why don't we take some of the world's smartest distributed systems engineers that we knew and make them part of the process of research from day zero?” Not retrofitting it later on, but, like, really from the beginning. And that became our model factory. And so our model factory started with a handful of components. Today, it's thousands of components, and I try to equate it to, if you think about, like, someone who was at the very early days of Foxconn, if they had been there for the following, decade, they would be able to rebuild Foxconn because they saw every decision that led to building that system and all the complexity. If you and I walk into Foxconn today, no chance.The Model Factory and Experiment VelocityEiso Kant [00:18:18]: Right? Because we don't have the lineage and history of decisions that led to that. And so we built early on from the beginning- with a team that really understood that, well, the metric that we are optimizing for is the speed of an idea from a researcher to an experimental result that we can trust to then being part of the next model training.Eiso Kant [00:18:42]: And in the. And because it's such an experimental science, ultimately, in the beginning when it wasn't that complex, you could patch your way around it, right? But now, at any foundation model company, you are running. I mean, we're a small team, right? We're less than 70 researchers, another 35 engineers. and we are running, I haven't checked the latest count, but far more than 10,000, maybe 10 to 20,000 experiments a month that we cut. And so if you look at that scale of every model run that is, like it's ultimately it's, it's you need to be able to trust it as an infra problem. And so what we have now done over the years is gotten really good at that, and just by working it and improving it and obsessing over those end decisions. So now what that means is that you looked up Laguna XS 2 that we launched. It was five weeks from the beginning of training to launch. The model that we're gonna talk about today was eight weeks from start of training, to launch. We started the next model literally yesterday because we now finished the post-training required for the model we're launching, next week or by the time this comes out today. and we move that compute to the much larger Laguna M model that we're now training. And so the model should be an artifact of someone's process. It shouldn't be really a thing in itself. Like, and we treat this like the way you would look at like a SpaceX factory where, yes, the first rocket, really hard to build, but the much harder challenge was building the factory. And now they're rolling off, and no one is really thinking about the next launch anymore. So it's just another launch, it's another launch, another rocket comes off. And that's what we're trying to do with model building.Eiso Kant [00:20:22]: And what has been, which was not planned from day zero, it was in the back of our mind like this will happen one day, is that when you build a really good end model factory with really good APIs and really good engineering systems, Well, what is it perfect for? It's perfect for agents.Agents Inside the Model FactoryEiso Kant [00:20:40]: Because agents are now starting to take over more and more work in our model factory.Vibhu [00:20:43]: Yeah.Eiso Kant [00:20:44]: So I look at the screens when I walk, like when we're, we come together, in our monthly, we do monthly onsites, and I walk behind people's screens and I stop by and I talk to our researchers. And the default is all of these different agents running on their screen that are writing the code. They're launching the jobs. They're evaluating the results that are coming back from the model runs. They are, making the changes. And we're still in the driver's seat. We're still coming up with the ideas. We're still helping with the debugging. But more and more, and this is right now very profound on the data side of our pipelines in both pre and post and the synthetic data pipelines, it's starting to become more on the architecture side as well. You're starting to see these twinklings of what RSI is gonna look like.Eiso Kant [00:21:27]: And that's. So when we talk about, like to your question about our models, every talk about the model factory, And my coolest example of these things is always that when we kick off a new run, doesn't matter if it's a training like big run or if it's now a post, like one of 10 post-training versions we do for like release or many experiments, is that at any given moment, the changes that somebody made that they had experimental results from the day before make it into that run.Eiso Kant [00:21:57]: So there's not like a cutoff 90 days before. Like no, it's like literally from that moment because we can now trust the machine enough. And then you also have to invest in the reliability. So one of my favorite metrics about like Laguna S is that there was no call events, Right? Like completely zero. And we haven't had a meaningful call event, like something to wake up for, as far as I recall this entire year. now there is one asterisk to that. In usually the first six hours of launching a new model run, something breaks because you set a config wrong, you made a small mistake, et cetera. So that's usually there's a little bit of intervention, but that's always within like call periods, right? Not on call. And I think that's starting to now compound. So the model we're releasing now, I love it. It's amazing, but we're already onto the next one. and I think that's the way it should be.Laguna, Five-Week Builds, and Zero On-Call EventsVibhu [00:22:50]: Hey, I also just wanna point out, so for context, this was like a month ago. we found it in the tech report, so we just came in with, “Okay, new model's dropped. Haven't heard about it.” We wereEiso Kant [00:23:02]: Yeah, we're very used to doing this every few months.Vibhu [00:23:03]: We're, we're very much like, “ okay, look, it's like, on par with Kimi, DeepSeek, whatnot, the small ones, Gemma level. Oh, it's a very cool paper on what goes into building.” And then we hit this page, right? Like literally page two of tech report is, “This process allowed us to build the small model from scratch to delivery within five weeks applying the lessons”. And then I'm like, oh, this paper is not about here's a tech report of benchmarks and here's how many tokens it was trained on. Like for people that wanna dive more from what we're not gonna discuss on the podcast, it's all laid out here, right? FromEiso Kant [00:23:38]: YeahVibhu [00:23:39]: Custom software that agents can use to interface with training code, training data.Eiso Kant [00:23:45]: Yeah. Well, link the paper correctly, so yeah.Vibhu [00:23:47]: Yeah. All that stuff. read the paper here, but,Technical Report Principles and Streaming Training DataEiso Kant [00:23:50]: But I would like to. I love principles, and I think that is a good starting off point for maybe telling some stories. Maybe we can go one by one past the principles. I'll just call out that Dagster just got bought by a Prefect.Vibhu [00:24:01]: Yeah.Eiso Kant [00:24:01]: Isn't it fun? But yes, I'm very familiar with Dagster. just anything where like they trigger some story.Vibhu [00:24:07]: So, well, I would say, well, experiments code's obvious, but I think one of my favorite things is, I don't know where it is in here, but early on, and I still think this is the case a lot of foundation model companies, people prepare their training data sets, they get packaged up, then they get copied over to a training cluster distributed across all of the nodes, and then training starts.Vibhu [00:24:30]: And we looked at this like three years ago and we were like That makes no senseEiso Kant [00:24:36]: You lose so much time because the moment you have to rematerialize the data set, you have to make a change, you have to fix something, et cetera, you've got all this time of like repackaging it, right? Toca- tokenizing it, repacking it, moving it over to a cluster, then distributing it across the nodes. The bigger your clusters are, you start using fancy like torrent-like algorithms to like distribute your data. So why aren't we streaming data into training? Right? Something that's very common and like just basicVibhu [00:25:00]: Like just in timeEiso Kant [00:25:01]: Just in time, like good computer science like principle. And that was one of the first things that I think unlocked - the model factory. Because the moment you start thinking about, well, a training job, it doesn't matter if it's a big hero run or a small like, post-training experiment, consumes a certain number of tokens per second, right? And it's not a lot, right? From a like a data, moving data perspective. So we said, well, we have our training cluster, and then we've got like our AWS kinda setup where we can build these amazing big data pipelines. We can set things up. We use Spark underneath the hood, like all these things.Vibhu [00:25:36]: But when you say AWS, it's not actual AWS, it's your internal AWS.Eiso Kant [00:25:39]: It's our internal-- No, it's our internal like just running like our infrastructureVibhu [00:25:42]: Site web servicesEiso Kant [00:25:43]: Exactly. Our stuff running on like an AWS account or on like any hardware, right?Vibhu [00:25:47]: Yeah.Eiso Kant [00:25:48]: And so once we made that shift into I can stream data into training, all of a sudden you realize a lot of things unlock. Because now you don't have to wait for the whole data set to materialize.Immutable Data, Experiments as Code, and Scientific RigorEiso Kant [00:26:00]: You now all of a sudden when you're running data experiments about mixing data, it's a config. Because you've got these data sources that are coming in, and you just - we have this service called Blender that's in the report, where we then say, “Okay, for this run, I want 20% of this source, 10% of this source. I want this much, so many epochs of repetition. I want this to be, shuffled in a certain way,” and your training job can start while the rest of the data is even still materializing. also what it does is because all of this underneath-- So for us, we treated the data layer underneath as like an immutable data layer, and that was really important. Like experiments as code, immutable data layer means that you can always go back and understand literally down to the single token at which cursor it went in on which version of the code.Vibhu [00:26:47]: Yeah.Eiso Kant [00:26:48]: And it took us a I have to admit, like the first year of Poolside, we understood that engineering had to get great, But we didn't understand yet, that this is ultimately in support of like a good rigorous scientific progress. We were quite a - We were a very small number of people, so a lot of it was YOLO ideas and YOLO runs.Vibhu [00:27:08]: Yeah.Eiso Kant [00:27:09]: And we built great infra for the YOLO runs. But once we realized that we treated data as immutable and code as always versioned, and you could always track and trace every experiment end to end perfectly, you could repeat everything perfectly, right? You have perfect reproducibility. I can still reproduce runs from two years ago if I wanted to, right? It enables the scientific progress, like the scientific process, and I think that took us probably about a year and a half into the company to figure out. We also had some great hires, like our head of applied research, Nikolai, who joined us from Yandex, who'd been working on language models since like the early 2020s, I think brought that into the company of like, “Hey, we wanna have even more rigor.” And then once we kinda had the combination of like increasingly more capable platform that allowed people to do more, but had this immutability, we were able to start “Okay, every experiment is truly an ablation. We truly need to understand it.” And I think we became much more scientifically rigorous in the last couple of years, and the infra underneath enabled it. and then there's just fun stuff like, andVibhu [00:28:16]: Yeah, a lot of it's fun, like even just the, one, you share all the ablations, two, picking the data sets, right? There's like a random small paragraph in here where it's just like, “Oh yeah, training data, we have some, we have an auto mixer.” it trains eight small models, scales them up, picks the training data set. We don't even need to look at it. I'm like, “Wow, a lot of engineering rigor there.” And there's just, there's just a lot in here.Publishing Research and Giving BackEiso Kant [00:28:40]: Yeah, and it'- and look, and we wanna put out more. Like we, We treat writing papers as something that we haven't earned the right for yet for a long time. So you earn the right to spend time, publishing research once you're at the frontier, because until then, you're catching up, and every minute and hour in this industry matters. Like I obsess over it, not just the wall clock time from idea to result, but just general like time every day that we, waste is one that doesn't allow us to catch up. But in this case, we said, “Okay, we're gonna give ourselves.” I think we gave the team like three or four days while still doing their work, like give everything in there. And to your point earlier, if your stuff, it's easy to like put it out. And so there's so many more things that we wanna talk about over time, and we will definitely start doing. And as we earn more of the right, but also now have like added to our mission that we want more foundation model companies to exist, you'll see us like be way more proactive, and just trying to keep dropping some of those like things that we've learned along the way that can help others like speed up.Vibhu [00:29:40]: Which is the other cool side of this, right? It's, it's not like, back to your point, it's not just here's the benchmarks of our training. If you want to replicate, here's experiments of optimizers, data sets, post-training. you lay out a lot of it here alongside here's your system for how to do it? So it's, it's really like promotingEiso Kant [00:29:59]: No, thank youVibhu [00:29:59]: Other people can do the same.Eiso Kant [00:30:00]: And by the way, I also wanna make clear, right, we have been incredible-- Like we've taken a lot of advantage of the fact of all the open research that others have published, Right? And you mentioned, the Chinese labs, and we I think it's important that there's, from every country and every culture and background, including like Western companies like us, there's different models that come out that people can choose to trust. But I think we do have to give credit where credit's due, right? The incredible Chinese lab have done an amazing job at sharing their research, and we have definitely like been on the receiving end of taking advantage of that. So when you're on the receiving end of something coming to you, I think it's, you also have an obligation to give back.Swyx [00:30:39]: Do you have a favorite or underrated Chinese lab that you wanna shout out? Everyone shout outs DeepSeek.Chinese Labs, Zhipu, and PersistenceEiso Kant [00:30:44]: That's a good question.Swyx [00:30:45]: Moaan obviously for Therapsi. Yeah.Eiso Kant [00:30:48]: Yeah, look, I think, I think obviously everyone's been talking about Zhipu lately, with 5.2. I think what most people don't realize is when they started.Swyx [00:30:59]: Yeah.Eiso Kant [00:30:59]: Right? They started years before ChatGPT.Swyx [00:31:02]: They just rebranded. YeahEiso Kant [00:31:03]: And so, I've like, I remember how hard it was to work on these things Before the rest of the world got excited about it. And so I have an immense amount of respect for people, who were working on improving models when it wasn't the sexy thing to do, when believing in LLMs, was gonna get you ridiculed. I remember like back in 2016 when we were doing what we'd call, machine learning on code with some of these models. we would-- people would just laugh at us, like they'd be like, “This makes no sense. Like why are you wasting all these, like, millions of dollars on trying to figure this out?” And so I would say they're probably the one that, I think deserves a shout-out, not just because their latest model is very good, but because they fought to get here. And I think, I think every foundation model company it takes time to get here, right? It took us three years to get to the model that we're, that we're now gonna be releasing. and now the time in between the models is coming, is counted in weeks. It's no longer counted in months or years. But this stuff's hard. and if we can make it a little bit easier for the next person, like we should all do so. Because if we don't do so, we're, we've got a small window before models are really impacting recursive self-improvement to a level where catching up otherwise might become unfeasible. And we should try to, in that window, encourage as many labs or however we wanna call them, like to start. And so one of my currentEiso Kant [00:32:36]: Mission, but qualm is like I wanna encourage whoever is a researcher right now who thinks they can tackle this to go and leave and become my competitor.Eiso Kant [00:32:45]: Like start another foundation model company because I think we need it. I think otherwise we're not gonna be in the world where, I don't want to just be the fifth or the sixth company that wins. I wanna look at a world where there's lots of choice.Starting a Foundation Model CompanyVibhu [00:32:57]: What else do people not see in starting a foundation model? it's, there's a lot of compute, there's a lot of capital required, a lot of compute. You lay out model factory and how to do the training, but there's a lot there, right? That's,Eiso Kant [00:33:10]: Well, look, it's, I in turn-- this is an oversimplification, and I always asterisk it with that because it can land a little bit the wrong way in people's minds. But I think you can sum down, And I saw it, 95% of model building to just doing, you're just doing two things. You're improving data or you're improving compute efficiency. And I know that feels like an oversimplification for the incredible, like, Gifted and skilled work people do. But if you really look at it, like what are we doing? We are looking at data, we're generating new data, we're improving data. and the only way to do that is to look at the data, right? That's a big part of foundation model building. And on the other hand, we come up with these incredible breakthroughs in inference, in architecture, and new attention mechanisms. But what are they really doing? They're bringing compute efficiency. Now, we have definitely had some breakthroughs over the years that allow for more model capabilities. But at the limit, if you could train a large enough model, right, like, and you had infinite compute, we probably-- if you had infinite compute, you'd be at AGI probably already tomorrow.Eiso Kant [00:34:12]: Right? Like it's not. And so, and let me say that infinite compute with infinite ability of much faster networking because networking ends up being more of the bottleneck than compute. But, so I do think that's, those are the main things. And to just realize that this is engineering. I think it's become more obvious, but I think for quite a few years, people have held foundation model companies and researchers and others on this pedestal of like you're doing incredible magic or rocket science, or only like, Nobel laureate physicists can do this. And don't get me wrong, there are some really hard problems that need to be solved, but a lot of the work that all of us are doing on a day Is not sitting down trying to solve a math theorem. A lot of the work that we're doing is just really doing the basics right, writing good code, looking at data, improving it, running experiments, looking at plots, trying to see like, hey, trying to shape our intuitions. And a lot more people could be highly capable researchers. and I think that's, it feels far for people to do so. But I've seen in our own company, we've seen engineers become researchers because the model factory allowed them to be, have a much lower hurdle of running experiments and trying things. And one of the guys on our team who started as an engineer building our agents is a legit reinforcement learning researcher now, making real progress. and that happened in the span of like six months. that would've not been what I think most people assumed was possible, a couple of years ago.Swyx [00:35:46]: Yeah. I think one of the interesting moments is when you can self-host, like, if in a programming language, like if you can compile the language in the language, the equivalent is can you use your own tools, right? You have the pool CLI, you have your own models. presumably you're not only using your own models. There's no way. But like, what's that percentage over time?Laguna S, Persistence, and Behavioral GainsEiso Kant [00:36:10]: This is the first model that we're releasing that is starting to meaningfully contribute to our own work. It's not a it's not state-art model yet. Fable and other, they're, they're very capable models, but Laguna S Is really interesting. I'm gonna pull up the quote. Peng Ming, one of our heads of applied research, said something, last week as the model came out about 10 days ago, much better than we had hoped for or expected. And he said, I have the feeling that a lot of the gains in Laguna S come not from more intelligence, but more from different behavior, more verification, less taking things for granted, not declaring victory early, and being way more persistent. And to be honest, those are more predictive than raw intelligence for success in human also to some degree. And this was, he wrote me this on 5th of July on a Sunday, and it's been burned in my brain ever since because the Laguna S model, as you'll see it and why it does so well on benchmarks and why it does so well in using it on a day basis, is that it's just incredibly persistent. It reasons a lot. I do call that out. We have work to do on making it more efficient. We have to work to do on offering different reasoning modes. But this is the model that has been able to do things that I never thought it could do. A hundred eighteen billion 8B active model, which is not that large. It fits on a DGX Spark and still runs at, thirty, forty tokens a second on a Spark, is able to solve Erdős 397 independently. It's able to do complex programming tasks. It's able to. I asked it this morning to make me a Fi scanner without using any external libraries on my Mac, and it's, like, figuring out, like, the core WLAN API by really persistently trying to understand it without access to the internet. And more, I love vibe checking. I've probably spent eight to ten hours a day with this model for the last ten days.Eiso Kant [00:38:05]: I'm not exaggerating. I was on my eleven-hour flight yesterday. I spent ten hours reading trajectories and traces and, like, of the model.Eiso Kant [00:38:12]: And what I take away from it is exactly what Peng Ming said. We are gonna be able to squeeze so much more out of smaller models than I think we had imagined in the industry because, yes, there's intelligence and larger models are more intelligent. Like, no doubt about it. We should continue to scale up. but the behaviors of being really persistent, of being able to backtrack when you're wrong, of, like, understanding how to interact with your environment show us that we can get a lot more out of it. And this, for me, has created a bit of a Question in my mind the last couple of days. If you think about where we're using models today, right? We are using models, say, for knowledge work. Represents twenty-five percent of the global economy, twenty-five trillion dollars of work.Eiso Kant [00:39:00]: As we scale up models and they become more intelligent, we are excited about using them more and more for pushing the frontier of science.Small Models, Knowledge Work, and CommoditizationEiso Kant [00:39:08]: And if you look at the frontier of science, like true breakthroughs in science, they have been linked, they are linked to more intelligence in many places. Einstein figuring out general relativity is able to bring ideas together that other people would have not brought together. And I think one of the many dimensions of intelligence is the ability to do that, and it's something we clearly see that as models get larger and more capable, they're able to pull more ideas and threads together that a smaller model wouldn't be able to.Eiso Kant [00:39:36]: And we're starting to see examples of that in medicine and, like, in bio and other things. But if you think about the majority of knowledge work that we do, and it includes building software. I'm a software developer at heart first and foremost probably, although I probably can't say it that much anymore as I don't write production code in years, is that what makes us good is our persistence. It's our ability to encounter a problem and backtrack and say, “I need to go figure out this bug. I need to go research this. I need to go look at the documentation. I need to, like, try different, five different ways to see, like, if I can solve it.” But it is not necessarily bringing three ideas together from radically different fields. And so if we are now seeing, and I think Laguna S is an example, that we are able to make a relatively small model much more capable than I had definitely predicted or any previous, like, benchmarks had shown for any model remotely this size or even larger, At least on coding tasks, that it's because of the behaviors. And so now the question I have, and I don't have an answer, it is I know at the limit, so infinite model size, right, extremely large model, and the cost of that model is gonna be very expensive to run. We know this, right? So larger model ROI.Eiso Kant [00:40:52]: So I know that at the very limit, I'm not gonna use the world's largest model one day, quadrillion parameter, whatever crazy, like, scale we scale up, to do a basic coding task. Already today, I'm starting to size down for certain tasks.Eiso Kant [00:41:07]: So it means that there is an optimal. It means there's some curve that goes as we go up to model size for knowledge work, at some point we're at the peak, and after that, the return on investment of using a bigger model, just doesn't make sense.Eiso Kant [00:41:22]: Now, I think the question is, before I would have thought that peak was extremely very far away.Eiso Kant [00:41:30]: This model for me is the first sign that Maybe that peak is At a trillion, five trillion, ten trillion. Maybe we can just squeeze way more out of these models. I'm no longer thinking that we need two or three orders of magnitude on the largest models to be able to, solve knowledge work, the accounting, the legal, the code that we write. And so if that holds true, It is an argument for the commoditization of models. It's an argument that open source can win and, like, succeed in this world. And now it's of course a self-serving argument and it's a hopeful argument, but theoretically at the limit it works. We just have to go discover in the next couple of years of how much more we can squeeze out. Now, I do want to put a big asterisk. This does not mean I'm against scaling models. I think we ultimately only succeed if we scale our models as large as our competition. I do not like. I think we should not put our head in the sand and say we're gonna be king of open source small models. I think that's, It's a out. It's trying to be king of your own kingdom, but not realizing what the rest of the world's doing. All of us rather use a smarter, faster, more model. It's a sign of hope. And so I don't wanna overly state this is a good model. We have a long way to go to get to the state-art. But what hopefully people take away when they use this model is that the behaviors inside of it are what push it to be far more capable, less than necessarily the number of parameters.Pre-Training, Mid-Training, and RL Moving EarlierVibhu [00:43:03]: Is that mostly post-training? LikeEiso Kant [00:43:05]: YesVibhu [00:43:05]: Right.Eiso Kant [00:43:06]: It's entirely post-training.Vibhu [00:43:08]: Are we done improving anything on training? Is, like, training done?Eiso Kant [00:43:12]: No.Vibhu [00:43:12]: Okay.Eiso Kant [00:43:13]: SoVibhu [00:43:13]: I just wanted to cover training, and then we go post-trainingEiso Kant [00:43:15]: Training is not done. I mean, look, there's a part of training of just dealing with skill, right? Every new order of magnitude of model skill, you are going to get new things you gotta solve for. That'- but those are ultimately, engineering challenges.Eiso Kant [00:43:31]: I have a, I would say, a not commonly held opinion that reinforcement learning Will move earlier and earlier into training.Vibhu [00:43:42]: Yeah, training.Eiso Kant [00:43:44]: Not even training. Like training today, right, is, like if you look at - So we've been working on this for years already. and I think the best-- I think the first time we saw it out in public was the DeepSeek Zero paper. this is a year and a half ago, I think, if I recall correctly. where, you can Very early on in a model as it starts capable of being able to use language, et cetera, induce reasoning. and so the question that I have is like, we have this- we have the dataset that's the web. and the web, I think we could arguably say probably has The totality of humanity's knowledge somewhere encoded in different places. It's a huge variance degree of quality, from garbage data, and like once you look at training data, you really get humbled of like what the web is, to like, the most greatest scientific papers and best blog posts and like, best transcripts and whatnot.Eiso Kant [00:44:39]: And so now What we are trying to figure out, and have been doing a lot of work on, and it's a place where maybe not as open as we're on other things, but we will become more over time. we've been spending a couple of years really doing research on how can we turn the web into not just next token prediction, but into a way to teach the model to think earlier in its training. and I think there's a huge amount of gold to be found there. I think we are right now in, we've got some drugs in the industry. One of the drugs is distillation. Another drug is, more environments. Like, and they're great, and they make us feel good, and they make the models better, and like we're all addicted to them, and we'll use them, right? in various different ways. and but ultimately, I think we are still barely squeezing out of the web what we should be getting out of the web.Eiso Kant [00:45:33]: I think just next token prediction during training is not enough.Eiso Kant [00:45:36]: AndVibhu [00:45:38]: YeahEiso Kant [00:45:38]: I think we'll see some very interesting things still happen. and that RL in post-training to induce behaviors, to improve things, like I think - the whole world knows how to do this now. I think we're, we're scaling it up. Everyone is. But I wonder if we need to go as far as we're going today with environments. I'm not sure yetVibhu [00:46:01]: You mean we're going too far?Eiso Kant [00:46:02]: I'm, I'm not sure if the path to AGI is justVibhu [00:46:06]: Is more environmentEiso Kant [00:46:07]: More environments.Vibhu [00:46:08]: It seems like a never-ending, “Okay, I want instruction manual for this table, right? Am I gonna environment out building furniture? Or are we just gonna tail end like we need some general solution?”Eiso Kant [00:46:19]: I think there is, I think there's an ability to generalize more from the web. but I also am very encouraged, like when I look at Laguna S and, which is post-training is, well, is the big impact there. and I see like, oh, wait a second, just by making some of these behaviors much better, we're able to get so much more out of it. It just changes a little bit the way you think about intelligence.Vibhu [00:46:40]: Yeah. The analogy people draw often is the RL phase is where you don't learn as much new knowledge. You shiftEiso Kant [00:46:46]: Yeah.Vibhu [00:46:46]: Yeah. So, you shift distribution, and you can have it reason towards what you want. on your point about training, a lot of training is still just continue training in a domain, say medicine, then you do RL. So still justEiso Kant [00:47:00]: It's just better data, right? Like, I mean, training, ooh, I like how we invented this word. Like it's effectively just like,Vibhu [00:47:06]: Second phaseEiso Kant [00:47:07]: It's the second phase of training With like a really dumb way to do a curriculum. But like ultimately, what you'd want is a curriculum from token zero to token 30 whatever or 40 trillion tokens that really truly is the optimal curriculum for the model to learn. But training is essentially a stage curriculum on the web because we do not have to compute, And, effectively to try to ablate the perfect curriculum, right? And so I'm pretty sure that you'll start to see people talking soon about some other term, and there's two or - ‘cause now we do this, right? We talk stage two and stage three and stage four training and like. But ultimately, all we're doing is we're trying to assign a curriculum to the web data that we have to allow the model to learn better. I think at some point, as things get compute, as models get cheaper to run, as the next generations of compute, this will become more of a continuous spectrum. I also think the reason, by the way, you have training and like stage two and stage three is organizational, Right? It'- this is, I think, a thing where-- that we really try to avoid with the model factory is like Training exists because there's a training team now, right? There's people, or like people in training decide to focus on like a training effort. but what you really want is engineering and scale of experiments that allows for a much more continuous spectrum that you don't, you have infinite stages. Now, we're not there. Compute's not there. Organization design is not there for it yet. but I think we'll get there. we'll look back on a couple of years and be like, “Oh my God, it was so cute that we did our training data like this in such a like naïve way. Like we barely ordered it. We didn't really do a good job at likeCurriculum, Auto Research, and New ObjectivesVibhu [00:48:48]: The building that curriculum will get you that in the industry.Eiso Kant [00:48:51]: And I'll confirm that, when I talk to some researchers that this is a lot of the focus now is like how does training change and what is the next objective other than, next token prediction. I assume you don't have the answers, but you have some ideas.Vibhu [00:49:02]: We have some ideas. We're not ready to talk about it yet.Eiso Kant [00:49:05]: Yeah.Vibhu [00:49:05]: We've been working on them for years, and I think that's the one thing that's also like you asked earlier about, like what's not obvious about building a foundation model company is that you are constantly balancing the table stakes work, the recipe worksEiso Kant [00:49:19]: Yeah.Vibhu [00:49:19]: Versus like your, my crazyEiso Kant [00:49:22]: Pure researchVibhu [00:49:22]: Breakthrough.Eiso Kant [00:49:22]: Yeah.Vibhu [00:49:22]: Pure research and finding that balance and adjusting the percentage to it based on where you are in the race is really important.Eiso Kant [00:49:31]: I mean, so like, this is a nice way. I was gonna bring up auto research at some pointVibhu [00:49:35]: YesEiso Kant [00:49:35]: As another Andrej invention, or coinage, which is like, I honestly, like how many objective functions can there be, right? Like just try 1,000 of them, set it running, whatever.Vibhu [00:49:47]: Man, it's alsoEiso Kant [00:49:48]: Like what you're looking for. You're looking for loss curves like that, likeVibhu [00:49:51]: It's also a thing people take bets on, right? When you say more Neo labs, you're doing a version of we'll do foundation models, scale them up, next token predictors. A lot of other Neo labs that we see want to take a completely different approach, right? At some level, you're right. It's all, compute efficiency, and that's the net objective. But some are okay, different architecture, like vastly different amounts of compute spend. So some are different. They're not justEiso Kant [00:50:19]: YeahVibhu [00:50:19]: They're like, 99% not balancing, here's the vanilla and scale up. They're 99% on, here's novel research that'll change everything.Eiso Kant [00:50:27]: And I think, Luke, I think you. It depends when you started as well, right?Pure Research vs. Table StakesVibhu [00:50:30]: Yeah.Eiso Kant [00:50:30]: When we started, like the novel thing we did was reinforcement learning on code. No long- that's no longer novel by far, but we were like, - that's where we obsessed over when no one believed in RL. So you have to when you start the company, you have to have your own idea. You have to have something that's different that allows you to speed up, right? For us, it was RL to LLMs that later became common, like, Knowledge. But in the beginning, it wasn'tVibhu [00:50:53]: It's cool. this was like your original 2023 blogEiso Kant [00:50:57]: YeahVibhu [00:50:57]: Of purpose.Eiso Kant [00:50:58]: Yeah.Vibhu [00:50:59]: And like you do lay it all out here.Eiso Kant [00:51:01]: We laidVibhu [00:51:01]: The blog is pretty underrated, right? The whole RL on code was very early on.Eiso Kant [00:51:06]: Very early. And even we had to argue with people, like we say here things like to push beyond current capability, to train your own foundation model. We had to argue with people that it mattered that you had your own like, base model. you can fine-tune your way to success, right? major capabilities emerge from training a base model made accurate and useful during fine-tuning.Vibhu [00:51:23]: Which like, for perspective at the time, we knew closed models, OpenAI, Anthropic were huge. The open models we had were like Mistral 7B, a 30B, a 70B.Eiso Kant [00:51:35]: When weVibhu [00:51:35]: YeahEiso Kant [00:51:36]: The date on this thing is wrong. When we published this, it was April 2023. I think this was justVibhu [00:51:42]: YeahEiso Kant [00:51:42]: Happened on a migration, probably found it on archive.org.Vibhu [00:51:45]: Mistral.Eiso Kant [00:51:46]: Mistral had started, we started on the same month, right?Vibhu [00:51:49]: Yeah.Eiso Kant [00:51:49]: So this wasn't even, there was only, I think, Llama out at the timeVibhu [00:51:52]: SnellEiso Kant [00:51:52]: And that's it, right? And so, but I agree. I think we wan

#1002 Want to use AI to build faster, market smarter, and stay ahead of the curve? In part 1 of this 2-part episode, Brogan Williams sits down with AI marketer Ryan Doser to explore how he built a successful marketing agency and personal brand by combining deep marketing expertise with the latest AI tools. Ryan shares why working a traditional job gave him the foundation for entrepreneurship, how he landed his first client through networking, and why context — not just prompts — is the key to getting better results from AI. They also dive into Claude Code, Codex, AI workflows, second brains, and practical advice for entrepreneurs who want to leverage AI without getting caught up in the hype! What we discuss with Ryan: + Transitioning from corporate to entrepreneurship + Landing your first client through networking + Why marketing beats technical skills + Claude Code vs. Codex explained + When to use n8n (and when not to) + AI tools without the hype + Building an AI "second brain" + Why context is king with AI + Using AI skills and MCPs + Practical AI advice for business owners Thank you, Ryan! Check out Part 2 of this episode. Check out Ryan Doser at RyanDoser.com. Join AI Marketing Insiders. Follow Ryan on YouTube. To get access to our FREE Business Training course go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠MillionaireUniversity.com/training⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ To get exclusive offers mentioned in this episode and to support the show, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠millionaireuniversity.com/sponsors⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn more about your ad choices. Visit megaphone.fm/adchoices

AI NEWS: Gemini 3.6 Flash is here: More efficient, less expensive but Gemini 3.5 Pro is still testing with partners & very much not here. Is Google Gemini slowly getting cooked? Kevin Pereira and Gavin Purcell break down Gemini 3.6 Flash, Gemini 3.5 Flash-Lite and the missing Pro model. Plus Sam Altman's reported Washington briefing on OpenAI's next wave of AI models, Sunday Robotics' Memo folding laundry with a company-reported 99.1% success rate, and District 9 director Neill Blomkamp's 13-minute AI film NIGHTBORNE. Also: Fable 5's proposed counterexample to the 87-year-old Jacobian Conjecture, Codex and ChatGPT computer use, Notch warming to vibe coding, a whale-shaped Moby-Dick crossword, Gaussian splats, Bambi the Destroyer and Wizard Brains in the return of AI SEE WHAT YOU DID THERE!. THE MODELS ARE GETTING WEIRDER. THE LAUNDRY IS FINALLY GETTING FOLDED. // Show Links // Official Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber announcement https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-6-flash-3-5-flash-lite-3-5-flash-cyber/ Gemini 3.6 Flash on Frontend Arena https://x.com/arena/status/2079594271045455947 Logan Kilpatrick on Gemini 4 training and Gemini 3.5 Pro https://x.com/OfficialLoganK/status/2079594867161022817?s=20 Report of a new Google AI chip for Gemini https://x.com/MTSlive/status/2079198478849413390?s=20 Fable 5 and the Jacobian Conjecture counterexample https://x.com/__alpoge__/status/2079028340955197566?s=20 Context on the Jacobian Conjecture result https://x.com/jdlichtman/status/2079066717762863249?s=20 Andrew Curran on Altman's planned Washington briefing https://x.com/AndrewCurran_/status/2079604797838397495?s=20 Bloomberg: Altman to brief U.S. officials on OpenAI's next wave of models https://www.bloomberg.com/news/articles/2026-07-21/openai-s-altman-to-brief-us-officials-on-next-wave-of-ai-models Sunday Robotics' ACT-2 laundry demonstration https://youtu.be/d7I1wj0Gkik?si=8E44Kmpqa7Gazfuh Three hours of Memo folding laundry https://youtu.be/a2HZyURUE_o?si=dT_WiDVcVEHGj9qn Sunday Robotics' technical ACT-2 post https://www.sunday.ai/blog/act-2-preview Neill Blomkamp's AI film NIGHTBORNE https://youtu.be/8Wbtt2JxP7g?si=Q8WMwCB_cIXvxxJt Notch comes around on vibe coding https://x.com/notch/status/2079507573523300534?s=20 Riley Goodside's Fable 5 Moby-Dick whale crossword https://x.com/goodside/status/2078649724710658309?s=20 Gaussian splat of San Francisco's Grace Cathedral https://vincentwoo.com/3d/grace_cathedral/ Bambi the Destroyer, Episode 5 https://www.reddit.com/r/aivideo/comments/1uto3js/bambi_the_destroyer_episode_5/ Wizard Brains game https://x.com/wizardbrainz/status/2078860897259548946?s=20   // Community Links // Join the AI For Humans Discord https://discord.gg/muD2TYgC8f Support AI For Humans on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans newsletter https://aiforhumans.beehiiv.com/ Follow AI For Humans on X: @AIForHumansShow https://x.com/AIForHumansShow Follow AI For Humans on TikTok: @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Speaking and booking https://www.aiforhumans.show/  

Yash Poojary, a growth engineer at Every, dropped an idea for a campaign in Slack at 7 p.m. Instead of building it himself, Every's head of growth Austin Tedesco took a screenshot of the Slack thread, dropped it into Codex, typed "Can you do this?", and went to the gym.By the time he got back, Codex had built four audience segments, drafted emails for each one, and pulled a social image that had worked before. It took Austin 10 minutes to make some tweaks and schedule the whole thing to send the next morning. Within a few hours, it generated more than $25,000 in revenue. That story came out of the launch week for All Access, Every's new $625-a-year membership built around the Builder Pack. It includes $7,000 in credits and free usage from ten of the AI products Every uses every day, including Claude Max, Codex, Cursor Pro+, PostHog, Notion, Framer, Render, and Flora.On this episode of AI & I, four of Every's own builders—COO Brandon Gell, head of marketing Douglas Brundage, as well as Yash and Austin—sit down to show how they use AI, breaking down their personal stacks and giving insight into their own strategies and mindset for building.If you found this episode interesting, please like, subscribe, comment, and share!To hear more from Dan Shipper:Subscribe to Every: https://every.to/subscribeFollow him on X: https://twitter.com/danshipperTimestamps for YouTube:0:00 Intro 0:35 All Access Explained 3:01 Yash's Tech Stack and How He's Automating Testing Pipelines 8:02 The Idea to Execution Loop 10:25 How an Agent Turned an Idea into $25K 17:50 The AI Sandwich Workflow 22:03 Making AI Tools Accessible to Solo Builders 28:50 Douglas on Brand and Design34:51 Tips on What to Build First 43:46 What's Next for All AccessLinks to resources mentioned in the episode:Brandon Gell on X: https://x.com/bran_don_gellYash Poojary on X: https://x.com/poojary_yashAustin Tedesco on X: https://x.com/tedescau?lang=enDouglas Brundage on X: https://x.com/DABrundageIntroducing Every All Access: https://every.to/on-every/introducing-every-all-accessGet the Builder Pack: every.to/builder-pack Go to https://attio.com/every and get 15% off your first year.

Google launched Gemini 3.6 Flash and started pretraining Gemini 4. China weighed tightening AI export controls, Bessent said the US would probe Chinese model distillation, Apple prepped a Klarna-backed leasing program, and OpenAI passed 10M Codex and Work users. Google launches Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber (9to5Google) Google launches Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber, and says it has started its "most ambitious pre-training run yet" for Gemini 4 (CNBC) Sources: China is weighing tightening AI and chip export controls and is consulting leading domestic AI companies, in a bid to slow advanced tech acquisitions (FT) US Treasury Secretary Scott Bessent says the Trump administration will investigate whether Chinese AI models were illegally distilled from American models, and raises the possibility of sanctions (CNBC) Sources: Apple plans to launch Apple Upgrade, a leasing program for iPhone, iPad, Mac, and Apple Watch, in partnership with Klarna in the US next week (Bloomberg) OpenAI says it now has 10M people using Codex and ChatGPT Work, nearly doubling usage from earlier this month when the company announced ChatGPT Work (Bloomberg) OpenAI says it now has 10M people using Codex and ChatGPT Work, nearly doubling usage from earlier this month when the company announced ChatGPT Work (Implicator AI) Garmin launches the Cirqa Smart Band, a $200 screenless fitness tracker that monitors 80+ activities with an up to 10 day battery life, to compete with Whoop (Bloomberg) Garmin launches the Cirqa Smart Band, a $200 screenless fitness tracker that monitors 80+ activities with an up to 10 day battery life, to compete with Whoop (The Verge) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

The Autobiography of Benjamin Franklin (1706-1790) written in the form of an extended letter to his son, William Franklin (1730-1813). Ben kept good records of his life and travels, and although he was never President, he still played a crucial part in American history. The Autobiography of Benjamin Franklin at https://amzn.to/43cp6CV Benjamin Franklin Books available at https://amzn.to/41fUkGD ENJOY Ad-Free content, Bonus episodes, and Extra materials when joining our growing community on https://patreon.com/markvinet SUPPORT this channel by purchasing any product on Amazon using this FREE entry LINK https://amzn.to/3POlrUD (Amazon gives us credit at NO extra charge to you). Mark Vinet's HISTORICAL JESUS podcast at https://parthenonpodcast.com/historical-jesus Mark's TIMELINE video channel: https://youtube.com/c/TIMELINE_MarkVinet Website: https://markvinet.com/podcast Facebook: https://www.facebook.com/mark.vinet.9 X (Twitter): https://twitter.com/MarkVinet_HNA Instagram: https://www.instagram.com/denarynovels Mark's books: https://amzn.to/3k8qrGM Audio credits: The Autobiography of Benjamin Franklin (Librivox, read by T. Hersant). See omnystudio.com/listener for privacy information.

A series of 16 influential political pamphlets published between 1776 and 1783 during the American Revolutionary War (1775-83) titled The American Crisis, or simply The Crisis, by eighteenth-century Enlightenment philosopher and author Thomas Paine — an Englishman living in the colonies who signed his essays anonymously as "Common Sense," the title of his earlier influential work. Each essay, bolstered the morale of the American colonists to fight hard for their independence, appealed to the English to support the colonist's cause, clarified the issues at stake, and denounced any type of negotiated peace. The essays were gathered into one volume in 1882, showcasing the iconic opening line: "These are the times that try men's souls. The summer soldier and the sunshine patriot will, in this crisis, shrink from the service of their country; but he that stands it now, deserves the love and thanks of man and woman." The American Crisis by Thomas Paine at https://amzn.to/4dKKClU Common Sense by Thomas Paine (book) available at https://amzn.to/3MKX77b Writings of Thomas Paine available at https://amzn.to/3MCaFC2 Books about Thomas Paine available at https://amzn.to/4s3qxOg ENJOY Ad-Free content, Bonus episodes, and Extra materials when joining our growing community on https://patreon.com/markvinet SUPPORT this channel by purchasing any product on Amazon using this FREE entry LINK https://amzn.to/3POlrUD (Amazon gives us credit at NO extra charge to you). Mark Vinet's HISTORICAL JESUS podcast at https://parthenonpodcast.com/historical-jesus Mark's TIMELINE video channel: https://youtube.com/c/TIMELINE_MarkVinet Website: https://markvinet.com/podcast Facebook: https://www.facebook.com/mark.vinet.9 X (twitter): https://twitter.com/MarkVinet_HNA Instagram: https://www.instagram.com/denarynovels Mark's books: https://amzn.to/3k8qrGM Audio credits: The American Crisis by Thomas Paine (a LibriVox production read by volunteers and coordinated by Michele Fry, 2014). See omnystudio.com/listener for privacy information.

There's (another) new open source king of AI.

Mike and Tyler are on vacation. Enjoy this rerelease of their first foray into id Software's beginnings, shareware, and what devices play doom (hint...everything). The theme music is by RoccoW. The logo was created by Dani Dodge.

Cheryl Costa — journalist, former New York Skies columnist for the Syracuse New Times, and co-author (with Linda Miller Costa) of the UFO Sightings Desk Reference series — joins this episode to break down two decades of UFO sighting data collected across every U.S. state and county. Drawing on more than 5,600 data points, Cheryl reveals which zip codes, counties, and states actually top the charts for sightings, why the numbers rarely match popular "hot spot" folklore, and the statistical patterns behind when and where people report seeing something in the sky.This episode is presented by Codega's Codex of Curiosities, a weekly podcast exploring paranormal encounters, UAP phenomena, cryptid sightings, and high-strangeness stories from guests across the world. Find more episodes and join the community at the links below.Topics Discussed:Twenty years of UFO sighting data, tracked down to the individual zip code and county levelWhy Downtown Phoenix's 85001 zip code ranks as the single hottest UFO sighting location in the countryThe key statistical "drivers" of sightings: population, temperate weather, hours of darkness, and broadband accessThe "influencers" behind regional spikes — proximity to large bodies of water, geological faults, and nuclear-capable military installationsThe unconfirmed "generational effect" theory, where past local UFO lore primes future generations to look up and report sightingsMedia consolidation and how sighting reports get filtered before reaching national coverageLinks

Is Kimi K3 the shocker of 2026? Could be. Now, we have a new (soon to be) Open Model that's competing with Fable 5 and GPT-5.6, a feat few would have believed possible. And that was the only new and important drop this week in AI. Claude brought useful browser to the desktop, ChatGPT made a big fix to how ChatGPT Work works and Google rolled out avatars that could change content creation. Don't miss our Friday Features show, where we recap the most important AI updates and features you can use today. Claude Desktop Gets Upgrade, New Open Source Model Shocks, ChatGPT Desktop Gets Better and 7 More AI Features You Can Use Today -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Claude Desktop App Browser UpgradeOpenAI ChatGPT Work Desktop App ImprovementsChatGPT Universal Search Feature LaunchSuperhuman Email Auto-Draft with GPT-4Spotify AI Voice/Text Conversation FeatureGemini Omni Personal Avatar Video CreationGoogle Vids Integration with Personal AvatarsMoonshot Kimmy K3 Open Source Model ReleaseKimmy K3 vs Fable 5 and GPT-5.6 BenchmarksTimestamps:00:00 New open source AI model release03:41 Microsoft Copilot and Claude app updates07:22 Improving chat history search12:30 Spotify's data personalization benefits14:52 Launching Google Avatar Feature18:24 Mainstream avatar video tools21:33 Improved ChatGPT project syncing24:15 Introducing Kimmy K Three Model29:30 New Kimmy k three for enterprises30:45 Friday feature show wrap-upKeywords: Claude desktop, Claude desktop upgrade, open source AI model, proprietary AI, open vs closed AI, Anthropic, built-in browser, Claude app, API docs, browser integration, permissions card, security layers, ChatGPT desktop app, OpenAI, universal search, ChatGPT search, chat history, project sync, mobile AI apps, Codex, ChatGPT work, Codex mode, Superhuman mail, auto draft, Anthropic Frontier models, GPT-3.5, Gmail integration, Outlook integration, Spotify, Talk to Spotify, personalized AI conversation, Gemini Omni, Google Gemini, personal avatars, Google Vids, video editing AI, video avatars, L&D AI, content creation with AI, Kimi k3, Moonshot AI, 2.8 trillion parameter model, 1 million token context, vision mode, benchmark leaderboards, Fable 5, GPT 5.6, Opus 4.8, open model weights, self-host AI, enterprise AI solutions, long context AI, front-end design AI, subscription AI tools, API pricing, AI benchmark, arena rankingsSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

AGENDA: 00:00 – Apple SUES OpenAI: Did They Steal Apple's Biggest Secrets? 05:10 – Is OpenAI's $6BN Hardware Bet Already Dead? 12:50 – Zuckerberg Is Back: Meta Finally Takes On OpenAI 18:05 – The AI Spending Bubble Nobody Is Talking About 23:45 – Claude Is Coming for Designers, Product Managers & Figma 27:15 – Anthropic's $50BN Explosion: Have We Already Hit AI's TAM? 36:00 – The $26BN AI IPO Powering the Entire Industry 40:00 – Seed Investing Is Dead? Jason Calacanis Changes Strategy 57:00 – SaaS Is in Trouble: AI Is Accelerating Terminal Decay 01:15:00 – Why Greylock Said No to Billions of Extra Dollars  

OpenAI's new ChatGPT Work is on fire.

Microsoft's July 2026 Patch Tuesday just shattered records with hundreds of bug fixes, and AI is the force behind the surge. Are we witnessing the beginning of a safer Windows, or is this flood of vulnerabilities the new normal? Plus, Tony Redmond's epic book has a new name (was Office 365 for IT Pros) and is now a bundle of four books. Windows Patch Tuesday is here! Point in Time Restore, quieter Widgets, Windows Update improvements, Screen tint, and more The biggest Patch Tuesday in history, by far: a record 570 fixes for security flaws, and a huge 3X increase from the then-record flaws fixed last month. (Some say the number is 622. Math is hard.) And this is on top of 468 Microsoft Edge/Chromium flaws that were fixed by Google this month too. Yikes. Microsoft discusses how it's improving Windows security with AI Windows Insider Program: Improved Windows Search is next on the docket, heading out to Experimental this week. Question: When do these things hit stable? Microsoft releases Snapdragon X2 versions of its Surface for Business Pro and Laptop models AI Apple sues OpenAI for stealing trade secrets Surprised there was no talk of "thermonuclear war" OpenAI's response is hilarious, and it is already prepping its first hardware device OpenAI just announced the rumored super app, which is a combination of ChatGPT, Work, Codex, and an in-app web browser, which replaces the standalone Atlas web browser. Also, GPT-5.6. In keeping with recent history, Anthropic announced its in-app web browser for Claude less than 24 hours later Reminder that Microsoft acknowledged that it, too, is working on an AI super app at Build Apple releases public betas of its OS 27 releases and, shocker, Siri is really good Now Spotify has an AI chatbot so I can tell it how much I hate Spotify Xbox and gaming Obsidian is working on a new Fallout game, duh. A quiet time after last week's terribleness Tips and picks Tip of the week: Microsoft 365 for IT Pros (2027 edition) is here! App pick of the week: MusicBee RunAs Radio this week: Finding Security Vulnerabilities using AI with Sami Laiho Brown liquor pick of the week: Sanctuary Single Malt Whisky Reserve Edition Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: blackhat.com/us-26 and use code TWIT helixsleep.com/windows