Podcasts about UI

Adam Engst returns to the show to talk, in detail, about certain of the UI changes in iOS 26 and Apple's version 26 OSes overall. In particular, the new Unified view in the Phone app, and the Filter pop-up menu in both the Phone and Messages apps. Also: a shoutout to Balloon Help.

In episode 561 of 'Coffee with Butterscotch,' the brothers dig into Mewgenics, exploring its development history, gameplay quirks, and the ways players actually engage with it. The game becomes a springboard for a broader look at how UI, quality-of-life choices, and genre-blending shape player trust and expectations. The conversation closes on the realities of indie launch windows, where timing can matter just as much as design when it comes to standing out.Support How Many Dudes!Official Website: https://www.bscotch.net/games/how-many-dudesTrailer Teaser: https://www.youtube.com/watch?v=IgQM1SceEpISteam Wishlist: https://store.steampowered.com/app/3934270/How_Many_Dudes00:00 Cold Open00:25 Introduction and Welcome01:13 Exploring Mewgenics: A Game Overview02:58 Nailed It or Whiffed It: Game Critique06:29 Player Engagement and Game Longevity10:18 Quality of Life Issues in Gameplay12:23 User Experience vs. Developer Intent15:28 Cognitive Load and Player Frustration18:34 The Role of UI in Game Design26:32 Humor and Theme in Game Design30:17 Developer Insights and Future Improvements39:27 The Disconnect in Game Development Quality41:57 Trust and Player Expectations in Game Design46:02 The Balance of Jank and Fun in Multiplayer Games51:26 The Impact of UI on Game Accessibility57:25 Launch Strategies and Market Timing for Indie GamesTo stay up to date with all of our buttery goodness subscribe to the podcast on Apple podcasts (apple.co/1LxNEnk) or wherever you get your audio goodness. If you want to get more involved in the Butterscotch community, hop into our DISCORD server at discord.gg/bscotch and say hello! Submit questions at https://www.bscotch.net/podcast, disclose all of your secrets to podcast@bscotch.net, and send letters, gifts, and tasty treats to https://bit.ly/bscotchmailbox. Finally, if you'd like to support the show and buy some coffee FOR Butterscotch, head over to https://moneygrab.bscotch.net. ★ Support this podcast ★

This is The Digital Story Podcast 1,040, Feb. 24, 2026. Today's theme is, "Snapseed Sprouts a New Camera, and It's Beautiful" I'm Derrick Story. Just when you think it's dead, Snapseed springs to life with additional editing tools, a refreshed UI, and a new camera app. And just like with some of our favorite mirrorless brands, we can capture images choosing from a variety of film simulations. And just like that Snapseed is relevant again. More about that, plus other interesting stories, on today's TDS Photography Podcast. thenimblephotographer.com, click the box next to Donating a Film Camera, and let me know what you have. In your note, be sure to include your shipping address. Affiliate Links - The links to some products in this podcast contain an affiliate code that credits The Digital Story for any purchases made from B&H Photo and Amazon via that click-through. Depending on the purchase, we may receive some financial compensation. Red River Paper - And finally, be sure to visit our friends at Red River Paper for all of your inkjet supply needs. See you next week! You can share your thoughts at the TDS Facebook page, where I'll post this story for discussion.

AI test automation is evolving fast — but most tools still generate brittle code that breaks with every UI change. See it for yourself now: https://links.testguild.com/Thunders In this episode of the TestGuild Podcast, Joe Colantonio sits down with Karim Jouini, founder of Thunders, to explore a radically different approach to AI testing: executing test automation in plain English without generating Selenium or Playwright code. Instead of "auto-healing selectors," Thunders interprets natural language directly — allowing teams to: Ship twice as fast Achieve 10x test coverage with the same resources Reduce regression cycles from weeks to days Eliminate massive automation maintenance overhead Karim shares real-world case studies, including: A European bank that reduced a 3-year core banking upgrade testing effort to 4 months A SaaS company that transitioned from a traditional QA team to AI-assisted product-led testing We also discuss: Whether AI test agents replace QA roles How QA managers must shift from individual contributors to AI managers The risks of adopting AI without a defined success metric The future of shift-left testing in the AI era If you're a software tester, automation engineer, QA lead, or DevOps leader trying to understand what's hype versus real ROI in AI testing — this episode breaks it down. Try it for yourself and see how AI testing fits into your pipeline. Get personal demo: https://links.testguild.com/Thunders  

The latest In Touch With iOS with Dave he is joined by Jill McKinley, Chuck Joiner, Jeff Gamet, Eric Bolden, Marty Jencius, Guy Serle. Apple teases a mysterious March 4 event as rumors swirl about colorful MacBooks and M5 updates. We break down VisionOS 26.4 beta, iOS 26.4 AI features, CarPlay updates, Rosetta 2 warnings, and Apple's expanding sports lineup — including MLS now free on Apple TV+. Plus, Emergency SOS via satellite saves skiers in Lake Tahoe. The show notes are at InTouchwithiOS.com  Direct Link to Audio  Links to our Show Give us a review on Apple Podcasts! CLICK HERE we would really appreciate it! Click this link Buy me a Coffee to support the show we would really appreciate it. intouchwithios.com/coffee  Another way to support the show is to become a Patreon member patreon.com/intouchwithios Website: In Touch With iOS YouTube Channel In Touch with iOS Magazine on Flipboard Facebook Page BlueSky Mastodon X Instagram Threads Summary In episode 409 of In Touch With iOS, Dave and the panel dive into Apple's newly announced "special experience" event scheduled for March 4 in New York, London, and Shanghai. With no official details revealed, speculation runs high. Could we see colorful, lower-cost MacBooks powered by A-series chips? M5 Pro and Max MacBook Pros? Updated iPads? The panel debates whether Apple may stage a staggered release week or unveil everything in a single coordinated announcement. The discussion shifts to Vision Pro, where rumors suggest Apple could demonstrate immersive Formula 1 experiences just days before the 2026 F1 season begins. With Apple's expanding sports footprint, including IMAX screenings of F1 races, the possibility of spatial sports broadcasting feels closer than ever. The panel also reviews VisionOS 26.4 beta updates, including refined UI elements, reorganized settings, early foveated streaming support for developers, and expanded 8K playback capabilities on newer hardware. iOS 26.4 beta brings one of the busiest update cycles in recent memory. Highlights include AI-powered playlist creation in Apple Music, enhanced podcast video playback directly inside the Apple Podcasts app, CarPlay integration with third-party AI tools like ChatGPT, Claude, and Gemini, improved hotspot usage visibility, battery charge limit automation through Shortcuts, and Stolen Device Protection becoming enabled by default. The panel weighs in on whether security features should be opt-in or automatically enforced. On the Mac side, Rosetta 2 warnings now alert users when launching Intel-based apps, signaling Apple's continued push toward full Apple Silicon adoption. The conversation explores legacy software challenges and developer responsibility during platform transitions. Additional stories include Toyota adding Apple Wallet car key support, Tesla's rumored CarPlay integration delays, and a powerful real-world example of Emergency SOS via satellite saving skiers in a Lake Tahoe avalanche. Finally, Apple's sports strategy takes center stage as MLS Season Pass becomes free for Apple TV+ subscribers, joining F1 and Friday Night Baseball in Apple's expanding live sports ecosystem. Breaking News Apple Announces Special Event in New York, London, and Shanghai on March 4 Apple Event on March 4: Here's What to Expect Upcoming Low-Cost MacBook May Come in Yellow, Green, Blue, and Pink F1 races to screen live in IMAX theatres in 2026 as Apple TV unveils new US viewing experience Topics and Links In Touch With Vision Pro this week.  Could Apple Demo Immersive F1 on Vision Pro at Its March 4 Event? visionOS 26.4 Beta Release Notes visionOS 26.4 unlocks new 'foveated streaming' feature for apps and games Beta this week. iOS 26.4 Beta 1 was released this week.  Apple Seeds First Betas of iOS 26.4 and iPadOS 26.4 to Developers Everything New in iOS 26.4 Beta 1 iOS 26.4 Adds Average Bedtime Metric and Restores Blood Oxygen to Health App Vitals Graph Apple Removes iTunes Movies and TV Shows Apps in tvOS 26.4 iOS 26.4 Brings CarPlay Support for ChatGPT, Claude and Gemini In Touch With Mac this week First macOS Tahoe 26.4 Beta Now Available for Developers Apple Releases First watchOS 26.4, tvOS 26.4 and visionOS 26.4 Betas  macOS Tahoe 26.4 Displays Warnings for Apps That Won't Work After Rosetta 2 Support Ends Other Topics Android-to-iPhone AirDrop Transfers Now Supported on Pixel 9 Tesla's CarPlay Plans Delayed by Apple Maps Compatibility Issue  Jeff met with Omni Group and reviews their 2026 road plan for OmniGraffle and OmniFocus for iPad and iPhone.  Omni Links News Toyota Rolling Out Apple Wallet Car Keys on iPhone  iPhone's Emergency SOS via Satellite Feature Helped Rescue Skiers Caught in Lake Tahoe Avalanche Apple TV Sports Content Including F1, MLS, and Friday Night Baseball Coming to Bars and Restaurants MLS 2026 Season Begins February 21 on Apple TV With Free Access for Subscribers Announcements Macstock X is here celebrating its 10th anniversary! With Three Full Days of expert-led Presentations and Workshops, Macstock's sessions are crammed full of productivity-enhancing content. NEW this year is a partnership with sponsor Ecamm. Ecamm Creator Camp: Mac Edition on July 9, 2026 there are only 100 tickets available for the bundle. There are 2 passes available: Macstock weekend pass July 10,11,12, 2026 or the Macstock Ecamm Bundle starting July 9 (only 100 tickets available)  Come join us. Register HERE Our Host Dave Ginsburg is an IT professional supporting Mac, iOS and Windows users and shares his wealth of knowledge of iPhone, iPad, Apple Watch, Apple TV and related technologies. Visit the YouTube channel https://youtube.com/intouchwithios follow him on Mastodon @daveg65, , BlueSky @daveg65  and the show @intouchwithios   Our Regular Contributors Jeff Gamet is a podcaster, technology blogger, artist, and author. Previously, he was The Mac Observer's managing editor, and Smile's TextExpander Evangelist. You can find him on Mastadon @jgamet Pixelfed @jgamet@pixelfed.social and Bluesky @jgamet.bsky.social‬ Podcasts The Context Machine Podcast  Retro Rewatch Retro Rewatch His YouTube channel https://youtube.com/jgamet Marty Jencius, Ph.D., is a professor of counselor education at Kent State University, where he researches, writes, and trains about using technology in teaching and mental health practice. His podcasts include Vision Pro Files, The Tech Savvy Professor and Circular Firing Squad Podcast. Find him at jencius@mastodon.social  https://thepodtalk.net  Eric Bolden is into macOS, plants, sci-fi, food, and is a rural internet supporter. You can connect with him by email at eabolden@mac.com, on Mastodon at @eabolden@techhub.social, on his blog, Trending At Work, and as co-host on The Vision ProFiles podcast.   Jill McKinley works in enterprise software, server administration, and IT A lifelong tech enthusiast, she started her career with Windows but is now an avid Apple fan. Beyond technology, she shares her insights on nature, faith, and personal growth through her podcasts—Buzz Blossom & Squeak, Start with Small Steps, and The Bible in Small Steps. Watch her content on YouTube at @startwithsmallsteps and follow her on X @schmern. Find all her work at http://jillfromthenorthwoods.com  Chuck Joiner is the host of MacVoices and hosts video podcasts with influential members of the Apple community. Make sure to visit macvoices.com and subscribe to his podcast. You can follow him on Twitter @chuckjoiner and join his MacVoices Facebook group. Guy Serle is one of the hosts of the new The Gmen Show along with GazMaz and email GMenshow@icloud.com  @MacParrot and @VertShark on X  Vertshark on YouTube, Google Voice +1 Area code  703-828-4677

EPISODE SYNOPSIS:Having picked up a job for some light body snatching and won their bar fight, it's time for the team to settle into some surveillance work.OUR LIVING CAMPAIGN MAPOUR SOCIAL MEDIA LINKS: EDITED BY:Rhydian Jones ARTWORK BY:Fnic SUBMITTING LOCATIONS AND DISTRICTS FORNEW YORK 2072 MAP:Any Submissions for new lore for existing districts or new locations, gangs or anything similar can be sent to b.team.shadowrun@gmail.com, with the subject “New Map Lore” or alternatively submitted to the dedicated channel on our discord found at: https://discord.com/invite/QB4FwXvrC4⁠  MUSIC CREDITS:Intro - More Human Than Human by Karl Cassey @Whitebat AudioOutro – Neon Thrills by LukHashBackground Music by Kharl Casey,Tabletop Audio & Aim to Head SOUND EFFECTS CREDITS:All Sounds from freesound.org unless otherwise noted.CREATOR - FILE NAMEmadcowzack - TEXT TYPE SEND RECEIVE iPhone.wavMATRIXXX_ - SciFi Inspect Sound, UI, or In-Game Notification 01.wavjar jar vince - sofa.wavvisualasylum - Unsheathing a Swordihitokage - Grab 2altfuture - Spilling Water On The FloorInspectorJ - Glass Smash, Bottle, E.wavredpanda69 - Snap stapler closeIridiuss - Magical focus energyGreub - Electroshock Weapon.wavPNMCarrieRailfan - Axe pacts Wood 04YleArkisto - Pullo, lasipullo rikki, lasi / A full bottle falls and breaks on the floor, water splashes, splinters of glass chink, mixmadcowzack - TEXT TYPE SEND RECEIVE iPhone.wavwordswords - SFX metal banging.wavCastIronCarousel - Chemistry table crash.wavissalcake - Chairs Break, Crash, pieces move.wavqubodup - Swipe WhooshTomasek1a - Tablesmack.wavabstraktgeneriert - Crumble #1.wavInspectorJ - Crunching, Wooden Fence, B.wavcourtneyeck - Falling body hits the floorTheMikirog - Bottle hitting a tableMBaer_Studios - Choking.wavtheshaggyfreak - Stabbing WatermelonSypherZent - Basic Melee Hitsandb1ns - Fluorescent Lightbulb Break.wavNatty23 - Glass Break 2EminYILDIRIM - Magic Impact Body HitEminYILDIRIM - Magic Impact Body Hitdeleted_user_7146007 - Metal Scraping on Concretebjelicvuk - frying pandavidh4976 - sizzle4-water-into-hot-pan.wav

Episode web page: https://bit.ly/4tH0nSl Leading without the title: The real power of the staff designer What does it take to grow your impact as a designer—without becoming a manager? In this episode of Insights Unlocked, host Jason Giles sits down with Catt Small, staff product designer, game maker, and author of The Staff Designer, to unpack the evolving role of senior individual contributors in design organizations. Catt shares her unconventional journey from creating digital dress-up dolls as a kid to shaping products at Etsy and Asana—and how those experiences shaped her perspective on leadership, influence, and creative confidence. At the heart of the conversation: a mindset shift. Moving from being told what to design to diagnosing what matters most. What you'll learn in this episode The misunderstood role of the staff designer: Catt explains why the staff-level IC role often feels ambiguous—and how influence, not authority, becomes your primary tool. She breaks down what “building influence” actually means in practice and why it's more intentional than mystical. Invisible work and strategic impact: From relationship building to cross-team alignment, much of a staff designer's impact happens behind the scenes. Catt explores how to prioritize the work that truly moves the business forward—and avoid getting stuck in “glue work” that doesn't drive career growth. From craft to communication: Design leadership at the IC level requires a shift from pixel perfection to clarity of thinking. Catt shares why low-fidelity diagrams and conceptual artifacts often create better alignment than polished UI—and how to coach teams away from jumping into high fidelity too soon. Navigating politics with integrity: If you've ever felt “allergic to politics,” this conversation reframes the idea. Catt explains how understanding motivations, fears, and power dynamics is less about manipulation—and more about empathy, curiosity, and emotional intelligence. Managing energy like a product: Influence takes energy. Catt shares practical strategies for auditing your calendar, designing your workweek intentionally, and partnering with your manager to balance short-term execution with long-term strategy. AI as a tool, not a replacement: AI is another tool in the designer's toolkit—but you're still the creative director. Catt discusses how to use AI to accelerate research and exploration without outsourcing your thinking or critical judgment. A key takeaway: Leadership is a mindset One of the most powerful themes in this episode is confidence. Staff-level designers aren't waiting for permission—they step into leadership by trusting their experience, sharing their perspective, and partnering across the organization. As Catt reflects, the transition is uncomfortable at first. But the shift from execution to influence starts with believing you belong in the room. Resources & links Catt Small on LinkedIn (https://www.linkedin.com/in/cattsmall/) Catt's website (https://cattsmall.com/) Catt's Maven page (https://maven.com/catt-small/staff-designer) The Staff Designer book page — 20% off with code UserTesting until Feb 28, 2026 (https://rosenfeldmedia.com/books/the-staff-designer/) Nathan Isaacs on LinkedIn (https://www.linkedin.com/in/nathanisaacs/) Learn more about Insights Unlocked: https://www.usertesting.com/podcast

On this week's show: Ring's Super Bowl ad fallout keeps getting worse as Search Party, Flock, Axon, and leaked emails raise bigger surveillance questions, Fire TV gets its biggest UI update ever, Eufy promises five-year motion sensors, and Third Reality drops new Zigbee gear. Ubiquiti goes industrial with a new Cloud Gateway, Shelly leaves garage doors wide open (literally), and OpenAI picks up the founder of OpenClaw. All of this, a pick of the week, project updates, and so much more!

How can you move your MCP tools beyond plain text? How do you add interactive UI components directly inside chat conversations? This week on the show, Den Delimarsky from Anthropic joins us to discuss MCP Apps and interactive UIs in MCP.

如果只推荐一本书给开发者和创作者，我几乎不会犹豫，答案是 Refactoring UI。不是因为它教你“什么风格好看”，而是因为它直接告诉你：什么地方一定是错的。很多人卡在 UI 上，并不是审美差，而是不知道问题出在哪。界面看着别扭，却只能凭感觉反复修改。这一期里，我聊的是《Refactoring UI》如何用大量前后对照，把 UI 从“玄学”拉回到可判断、可修正的层面。如果你做产品、写代码、独立开发，或者经常需要在没有设计师的情况下做界面，这一期会非常实用。 • 为什么我会把《Refactoring UI》推荐给开发者和创作者 • UI 为什么是产品设计里最容易被误解的一块 • “界面不对劲，但说不出来”的根源是什么 • 《Refactoring UI》的核心方法：错误示例 vs 正确示例 • 为什么它更像一次 UI 的 code review • 为什么我不建议从头到尾把这本书当小说读 • 这本书真正的价值在于反复翻和对照 • 当你带着书里的例子去看熟悉产品时，会发生什么 • 高级感背后，其实是非常朴素的原则 • 颜色、字体、间距和留白的真实作用 • UI 的基本功，为什么比风格更重要 • 从单个页面，转向系统性的思考 • 设计原则看起来简单，但执行并不容易 • 从功能出发，而不是从布局出发 • 限制选择，而不是不断增加选项 • 这些“默认决定”，为什么不是设计选择 • UI 改进为什么几乎每天都能用得上 • 把 60 分的界面，稳定拉到 80 分 • 最后的判断：少犯错，本身就是专业度Support this podcast at — https://redcircle.com/beartalk/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Vincent Heuschling reçoit Hayssam Saleh, créateur de **Starlake**, une plateforme data open source française née de la factorisation de projets clients depuis 2017-2018. L'épisode intervient dans un contexte de consolidation du marché (rachat de DBT et de SQLMesh par Fivetran), qui invite à challenger les solutions établies.Starlake se distingue par une approche **entièrement déclarative** (YAML + SQL natif, sans Jinja) couvrant toute la chaîne data engineering : ingestion, transformation, orchestration et qualité des données. L'outil s'appuie sur les moteurs sous-jacents des plateformes cibles (Snowflake, BigQuery, Spark) et génère automatiquement les DAGs pour les orchestrateurs du marché (Airflow, Dagster, Snowflake Tasks).Parmi les fonctionnalités marquantes : le **data branching** (branches de données à la manière de Git), l'inférence automatique de schémas YAML à partir de fichiers sources, un **transpiler SQL** multi-plateformes, et l'extraction du lineage depuis du SQL brut sans annotation. L'intégration récente de **DuckLake** ouvre la voie à des architectures on-premise souveraines à coût maîtrisé (sous 300 €/mois sur OVH, Scaleway, Clever Cloud).Le modèle économique repose sur le support, la formation, et le consulting : Starlake s'installe dans le cloud du client, avec mise à jour automatique gérée par l'équipe, sans accès aux données.**Chapitres****00:00:27** – Introduction : consolidation du marché data (rachat de DBT et SQLMesh par Fivetran) et présentation de l'épisode**00:03:13** – Hayssam et la genèse de Starlake : parcours Spark/Scala, POC à 4 000 formats de fichiers (2017-2018)**00:09:51** – Architecture et philosophie : load, transform, orchestration unifiés en déclaratif (YAML + SQL natif, pas de Jinja)**00:00:18:18** – Starlake vs DBT : différences philosophiques, composabilité, fonctionnalités 100 % open source**00:00:22:20** – Data branching, Starlake Labs (pipe syntax, transpiler SQL, lineage) et expérience développeur (DuckDB local, UI point-and-click)**00:36:35** – Modèle open source et économique : licence Apache, support, formation, marketplace cloud souveraine**00:43:42** – DuckLake : alternative on-premise/cloud souverain (OVH, Scaleway, Clever Cloud) et comment contribuer / démarrer**Le BigdataHebdo**Le BigdataHebdo est le podcast Francophone de la Data et de l'IA.Retrouvez plus de 200 épisodes https://bigdatahebdo.comRejoignez la communauté sur le Slack https://join.slack.com/t/bigdatahebdo/shared_invite/zt-a931fdhj-8ICbl9dbsZZbTcze61rr~Q

How AI Agents are Disrupting the AdTech Landscape Semantic content classification driven by AI agents is currently transforming digital advertising and B2B content monetization as we know it. When leveraged the right way, marketers can classify B2B content into actionable signals and find the most relevant content across the open web. This shift toward AI-native advertising allows for a more sophisticated approach to targeting that moves beyond traditional cookies. So, how can brands strategically implement these tools to generate impactful results, and what does the rise of autonomous agents mean for the future of your digital marketing strategy? That's why we're talking to Brendan Norman (Co-Founder and CEO, Classify), who shares his expertise and experience on how AI agents are disrupting the AdTech landscape. During our conversation, Brendan discussed the evolution of digital advertising and the critical integration of AI and cloud-based tools to automate manual tasks and improve campaign optimization. He also elaborated on the massive shift from human-centric to agent-centric traffic, predicting that agent traffic will surpass human traffic within 18-24 months. Brendan also explained why he believes that the future belongs to marketers who can blend audience and contextual signals to monetize human and agent attention. He highlighted how new AI-native tools are democratizing advanced ad tech, significantly reducing costs and improving efficiency for large and small advertisers. https://youtu.be/yVobWZTmwco Topics discussed in episode: [03:01] Beyond Keywords: How semantic understanding allows advertisers to target the nuance of a page (like “snow removal” vs. just “winter”) rather than broad categories.  [06:46] Optimizing for AI Agents: Why “Generative Engine Optimization” (GEO) complements traditional SEO, and how brands must prepare for agents retrieving information instead of humans.  [12:34] The Shift in Web Traffic: The prediction that agent traffic will surpass human traffic on the web in the next 6 to 24 months.  [15:50] The Power of Context + Audience: Why the best advertising strategy combines who the user is (audience) with what they are consuming in the moment (context).  [20:47] Democratizing Ad Tech: How AI agents and new frameworks will allow smaller brands with smaller budgets to access sophisticated programmatic advertising tools.  [26:54] High-Fidelity Curation at Scale: How AI reduces the cost of processing massive data sets, making real-time optimization and curation accessible and sustainable.  [33:44] The “Middleman Tax”: A look at the inefficiency of current ad tech where only 35 cents of every dollar reaches the publisher, and how AI can fix this.  Companies and links mentioned: Brendan Norman on LinkedIn  Classify  Bluefish AI Agentic Advertising Org  IAB Tech Lab Transcript Brendan Norman – Classify, Christian Klepp Brendan Norman – Classify  00:00 I think overall, jobs will change. I think that people will have to spend a lot less time doing a lot of the manual, rote tasks that they’re doing today. You know, kind of in parallel with what we’re seeing in terms of vibe coding and people’s ability to build product really quickly, design new web pages really quickly, like get ship things out quickly. I think a lot of the infrastructure layer tools, or just call them like, like, chatGPT style, cloud based tools, LLMs (Large Language Models), we’ll see a lot deeper integration into existing advertising product. And what that does is it helps democratize the whole ecosystem. So I think it frees up people’s time, you know, to not have to do a lot of the basic administrative, you know, reporting, manual, campaign, optimization type stuff, and it will help service a lot better insights. Ultimately, I think the industry grows, and I think it scales even faster and cautiously, optimistically. I think that we, we will have back to building on the curation piece, and, you know, the advertiser, outcomes piece, publisher monetization piece, user experience piece, I think that all those things will increase. Christian Klepp  01:07 When done the right way and leveraging the right approach and technology, you can classify B2B content into actionable insights and find the most similar content across the open web. So how can this be done the right way, and what role do B2B Marketers play? Welcome to this episode of the B2B Marketers in the Mission podcast, and I’m your host, Christian Klepp. Today, I’ll be talking to Brendan Norman about this. He’s the Co-Founder and CEO of Classify, a software that organizes the world’s digital content, making a privacy, safe, searchable and monetizable. Tune in to find out more about what this B2B Marketers Mission is, and off we go. I’m gonna say Mr. Brendan Norman, welcome to the show. Brendan Norman – Classify  01:49 Thanks for having me, Christian. Christian Klepp  01:51 Great to have you on. I’m really looking for this conversation because, man, like you know, in our previous discussion, besides talking about snow and bad weather, we did have, we did have we did have some interesting discussions around, I’m going to say, AI machine learning, and how that all has some kind of like strong correlation to content. So let’s just dive in. I’m going to start with the first question here. So you’re on a mission to help publishers increase monetization potential and advertisers target the most relevant, curated inventory. So for this conversation, I’m going to focus on the following topic, and we can unpack it from there. So how B2B brands can optimize their own content. And you know, let’s be honest. Brendan, who the heck doesn’t want to do that, right? So your company classify, if I remember correctly. It’s a software that organizes the world’s digital content, making it privacy, safe, searchable and monetizable. So here’s the two-pronged question I’m happy to repeat. So first one is, walk us through how your software does that and B, how does this approach benefit? B2B companies looking to optimize their own content? Brendan Norman – Classify  03:01 Historically, how a lot of content gets categorized, classified, organized, it’s fairly unsophisticated, and it’s been fairly unsophisticated for a long time, just because, you know, the technology is difficult to do, and we haven’t really had the foundational ability to understand it in a way like a human understands it until fairly recently, and do it at Deep scale. So good analogy for this question is like, if you were having a we were having a conversation just a minute ago about the snow, you know, happening in Canada, and how cold it was and how much snow you got, and, you know, also around the fact that, like you had to shovel your driveway, you have a snow blower you were putting the snow. There’s a lot of different nuance to that conversation. I as a human, and most humans, are able to interpret all of that nuance and kind of positively negatively, understand that there’s a snow blower involved in that snow blower was used to remove the snow historically that conversation, you know, if it was just a blob of text, or if it were a web page, the the basic technology to understand it would have reduced it down to a category like snow or maybe winter, and that’s it, and that’s all the targeting that would have happened to that page. So our conversation, you know, gets transcribed. It gets put on a blog, or it gets put on a news site. The only thing that a machine could understand about it was, you know, snow and then potentially a keyword, tagged snow blower. And that’s all so we took a very different one. One of the reasons why you know that that makes it challenging for advertisers and also for publishers. If you’re the publisher of that content, you’re not able to help advertisers really understand the nuance to like, what are we talking about here? Because maybe an advertiser wants to sell snow blowers for that specific site. Maybe they’re looking to sell ski and since we were talking about removing snow from a driveway, probably not the best application to go sell skis on. What is helpful is to deeply understand all the nuance to like we were talking about a driveway. We were talking about removing snow from that driveway. So we invented, you know, a much better, more sophisticated way to scrape content, classify it according to all of the different, you know, nuances semantic understanding much more like a human would, and then embed all of those different, you know, semantic understandings into, you know, this, this, this file, and then we organize that in a way that makes it searchable and kind of understands all the relationships very quickly. And what that does is it helps advertisers, like if you know, I’m Honda selling snow blowers, which they make, arguably the best snow blower in the market, if they’re looking to reach people that are talking about snow removal from the driveway, they can very quickly see the list of all the different URLs across the internet, and they can build, you know, a deal ID, or they can build a targeting, contextual targeting segment to specifically pinpoint those very specific web pages. And that’s kind of how the technology works, and then also, also why it’s relevant to advertisers. Christian Klepp  06:21 Thanks so much for sharing that Brendan that definitely helps us give, you know, some perspective into, like, what your software does. And you know, just, I’m asking you this from, from somebody who probably has learned to write one or two lines of code, and that’s as far as my dev skills go. But like, how, how is your software different from like GEO (Generative Engine Optimization), or is there some kind of overlap? Brendan Norman – Classify  06:46 It’s fairly complementary. I mean, the problem that GEO, you know, is trying to solve, and we’ve got good friends, advisors, you know, like at Blue Fish AI and like, a really cool company, Andre, I worked with him at live rail. He was the co-founder back then, before we got acquired by Facebook, you know. And I think that the problem that they’re trying to solve is going back to that it was just stay on Honda snowblowers. They’re trying to help Honda understand how they’re represented inside of, inside of an LLM or inside of a chat bot. And what they also do is they help these companies restructure their pages for, you know, better representation inside of the other end of like a chatGPT or a cloud answer. So it is kind of SEO (Search Engine Optimization), but for the generative world where we sit on is kind of on a different side of that. It’s very complimentary, though, and we’re deeply understanding content at scale, and that’s helping, you know, the advertiser understand where to position their ad. We’re also just, you know, very quickly, moving into this new space of, traditionally, advertising technology is focused on a human going to a web page, reading that content, reading the article, watching a video, you know, whatever that content looks like, and then helping the right advertisers show up in a contextually relevant way, so that the human will click on that ad, and they’ll go to another web page, they’ll buy the thing, whatever somebody wants to sell. A very recent development, so back up a year or so, you know, chatGPT Claude when they’re out and their agents and their bots are scraping like going out to the web and they’re retrieving information. They’re doing it to train their models to make their models better at answering questions. But now, you know, fast forward to today. They’re actually spending more time just going to content and then using that content to answer a specific question. So like, what’s the best recipe for, you know, creating soft shell craps. It’ll query a couple different web pages. It’ll find that, it’ll retrieve that information and bring it back that that is not being monetized today. And there’s a really interesting thing that we’re, you know, we’re starting to work on, which is monetizing the attention of an agent. And, you know, it’s, there’s a lot to figure out, but it’s kind of like the early days of a web browser, and like early days of search, when humans would go, you know, to a search engine, they would pop in some keywords, or, like, right out of search, and then, you know, Google would look at their entire index of the web, which was an algorithm that was weighted based on the number of different contextual relevancy plus the number of connections between web pages. So a web page that I might have published in geocities.com that nobody else would link to, Christian Klepp  09:50 wow, GeoCities like… Brendan Norman – Classify  09:54 Throwing way back remember the days of like writing like HTML and you know, creating that, you know, looping in some type of image because nobody else had linked to that, like personalized page that you built, it would never get shown up. And, you know, the top 20 or 30 or probably even couple 1000, or maybe even 100,000 search results. So their algorithm was about contextual relevancy, plus the number of links that other pages that had to your page. And then they started to include advertising in that. So early days of ads in search were literally anything, you know, it’s any advertiser that wanted to advertise to you, and they were just kind of choosing the highest price, trying to figure out, you know, how do we make money? And then it evolved into much more contextually relevant ads and sponsored post or sponsored advertisements. So now you know, if you’re searching for, like, what’s the best, you know, LLM or chat bot, you’re probably going to see a sponsored ad from, you know, Claude and Perplexity and chatGPT. Now you’re also going to see the search results underneath those. What’s changing about that kind of rapidly is how we’re influencing because humans are spending less time going there and doing that, and also within Google, Gemini is also surfacing some AI summary quickly and kind of superseding that, creating a chatGPT experience inside of Google, which is a brilliant way to do it also. But a lot of human interaction with the web now is humans going to chatGPT going to cloud asking questions and kind of treating it like we used to treat search back in the day. So influencing that, influencing that agent, going out to the web and sitting in between. That is another really interesting way that you can help an advertiser tell that story, not necessarily to a human but to the agent who’s retrieving the information and then bringing it back to the human, Christian Klepp  11:56 Right, right, right? And if we’re talking about content, it’s, you know, doing it in such a way that the content shows up in the AI search. Brendan Norman – Classify  12:04 Exactly. Christian Klepp  12:05 Because everybody, everybody’s got those now, right, like Google or Bing, or whatever, they’ve got the, they’ve got the AI summary at the at the very top of the page, right when you, when you, when you key in something. Brendan Norman – Classify  12:17 Yeah. Christian Klepp  12:18 Okay, fantastic. I’m gonna move us on to the next question about because we’re on the topic of optimizing content. So what are some of the key pitfalls that like B2B Marketers and their content teams? What should they be mindful of, and what should they be doing instead? Brendan Norman – Classify  12:34 That would be actually a better question for some of the GEO companies and something like more SEO focused companies about how to specifically optimize like your content. It’s a great question. I haven’t spent as much time, you know, deeply thinking through that. And the problem that we’re trying to solve is more of, you know, at scale, what is the semantic understanding of like, how somebody has built their page and or construct the video, as opposed to advising them on what they should do? You know, to think about it in a way that’s either more engaging. I would pivot that question more to the Geo and SEO focused folks, yeah, but super high level. I mean realizing that now web has two primary users of traffic. There’s humans who are bouncing or reading a, you know, web page or watching a video. But there’s also agents. And now the scale is like, changing very, very quickly. So you know, in the next year, two years, everybody will have lots of agents, kind of doing things on the back end for them. And, you know, we believe that, you know, in the next what, 6,12,18,24 months, Agent traffic will surpass human traffic on the web. So realizing that there’s these kind of two layers that one, humans see a web page and nice pretty pictures, and, you know, they see the layout great, but also having a web page that’s optimized in HTML, markdown, JSON, in ways that agents consume that, and then also knowing the different types of agents. So the cool thing that we’re building right now, in addition to this content graph of all the content, which is effectively like a understanding all the context between the content. It’s a mouthful, an agent graph that helps to inform this is an agent coming to my site. So in a lot of ways, it’s very similar to the folks who over the last decade or so, have built these identity graphs or audience graphs, and they know that like you, Christian versus me, Brendan, they’ve got some profiling on us. They understand our search history, our retargeting, our purchase intent, a lot of things that they’re appending to like you as a specific profile or an IP address. The rapid evolution of all this is mapping out the land. Landscape of different agents, where they come from, and then the personalization of these agents, and basically applying a lot of the similar logic that we’ve used for identity graphs and for audience graphs towards agents to help understand, how do you modify the content on the back end that humans never see, so that when they’re retrieving information, interacting with the content they’re doing it, you’re presenting in a really thoughtful way that drives like the answers and the results that you want to Christian Klepp  15:33 right, right? No, absolutely, absolutely. And in our previous conversation, you talked a little bit about contextual versus audience targeting. So and I mean, I’ve asked you this back then, but do you think one is better than the other, or do you think that they can work together? Brendan Norman – Classify  15:50 They should absolutely work together. Christian Klepp  15:52 And why? Brendan Norman – Classify  15:54 The reason, the reason is, you know, knowing who you are is a very important piece to the puzzle. Like, and if you even take a step back, like, what’s the whole point of advertising? Like, the whole point of advertising is storytelling, so that a brand or a service or a company can help market their brand service to the right person they’re trying to sell them something. The cool thing about the internet is we all now have this, you know, basic shared awareness that, like, there are certain things that are paid for on the internet, certain types of content that are gated. I might buy a subscription to The Economist, you know, I pay Claude a certain amount of money, a lot to be able to use it, you know, a lot and chatGPT, and then a lot of the web is free. Facebook is free, Tiktok is free, Instagram is free, LinkedIn is free. But the economics, it’s very expensive to run these businesses, so they have to, you know, support it through advertising. Ideally, you know, there’s a couple of ways to think about it, and there’s one camp of people on the internet who think that advertising is a necessary evil or a last resort, you know, we just cram it in there and make some money. There’s another camper of folks who actually think that it can be additive to the experience. And one of the reasons why, you know, it’s kind of a meme, and you always hear people talking about, you know, I didn’t need this thing, but I saw an ad for it on Instagram, and just had to buy it because it was really cool. The reason why that exists is that their advertising is phenomenal, and the targeting and optimization is phenomenal. And why it’s phenomenal on the back end is it knows a lot about you know me, who I am, what I’m interested in, based on my history, what I’ve been engaging with, where I’m spending time, you know, what I’m looking at, but it also knows specifically when I’m looking at that thing, you know, it might have a framework of saying, Brendan, really, you know, likes these types of skis, you know, he’s interested in, You know, a couple other, couple other interesting products, but the best time to serve each one of those products might be different, and it’s different depending on what I’m looking at, what I’m thinking about in that exact moment. And to kind of align these, these different graphs, graphs of intent, contextual understanding, and then audience, you know, the best time to serve me an ad for a new pair of skis is when I’m reading an article about skiing or something about the mountains. You know, it’s not necessarily when I’m reading about the Warriors, because I’m not really thinking about skiing when I’m reading about basketball. So to your point, the most effective ads are when you’re combining those two sets. It’s great for the advertiser, because I’m much more likely to click on it and go check out the skis. It’s also giving me a better experience, because it feels more native to the overall content that I’m reading. And that’s why it’s so important. It shouldn’t be an afterthought or a necessary evil or a last resort. It should be something that is intentionally thought about the entire design, because it can, it can actually be a cool experience. Christian Klepp  19:06 Absolutely, absolutely. I mean, you know, you’re talking to somebody that started his career in the in the advertising industry, so, yeah, I’ve heard that one before, and what you’ve been describing in the past couple of minutes sounds to me a little bit like time of day marketing too, right? Because you’re you know, are you the had a guest on, like, a year ago who talked about this? Right? Is, is Brendan, the same guy at eight in the morning and one one in the afternoon and seven in the evening? Right? There’s different different times of the day, different mindset, different motivation, different reason for being on your device or looking at, looking at specific type of content, right? But it is interesting, right? And it’s interesting and sometimes a little bit scary, how, um, how quickly the algorithm picks, picks this stuff up, right? Like, for example, last year, I was researching a lot on Japan, because we went there, right? Family trip and whatnot. And. And that’s what I kept seeing on Instagram, right? Like, because I was looking up specific temples and whatnot and and today I got another push. Like, would you like to invest in a temple that’s an on island in the Sea of Japan, right? Brendan Norman – Classify  20:12 Like, sorry, did you invest? Christian Klepp  20:17 No, I did not. But it was just, it was just funny that I got that ad right, like, it’s, like, Okay, interesting, but like, it’s so like it not, was not on my radar at all, right, Brendan Norman – Classify  20:29 Yeah, Christian Klepp  20:29 Okay, great. From your experience, and you talked a little bit about it now in the past couple of minutes, but like, from your experience, how can leveraging AI agents improve efficiency and save marketing leaders time? Brendan Norman – Classify  20:47 Ooh, there’s a couple different ways to think about that. So you know, part of it is this new agentic framework for how existing tools, you know, advertising and marketing tools, will communicate with each other today. You know, it’s fairly complex. You know, if I wanted to go build a contextual targeting segment to help one of our brands that we work with find the right contextual or inventory to target contextually, I would have to work with them. We build a targeting segment. We would upload that into our one of our SSPs, we would build a deal ID, you know, they would connect it back. And there’s a lot of different pieces that happen along the way. And each one of those pieces you have to go to, you know, a UI, I’ve got to go to a dashboard, I’ve got to push that thing in. Some of it happens through an API, but a lot of it happens like going to a whole bunch of different web pages to make sure this stuff all works. So stuff all works. What’s cool about agents? And I’ll unpack this, and then I’ll go to the more of the consumer focus side too. But what’s really cool about agents using, you know, things like the ACP framework from the Agentic Advertising Org., the ARTF (Agentic Real Time Framework) from IAB Tech Lab is they’re kind of built on some of the existing frameworks that allow humans to use natural language to communicate between these different systems. So there’s still the back end pipes of API pushing data or pulling data from one system to another. But on top of that is more of an agentic framework that allows, you know, a human just to use some prompting, like in chatGPT, to make a request, you know, that talks to a back end system. So that’s one part of the agentic framework for like, you know, how to think about this through the lens of advertising and marketing. And then the other side is, you know, more of the consumer focused. There are so many interesting and very quickly growing tools you know, that you can start to plug in, into Cloud, into Cloud code, and to building things that just rapidly accelerate development of different products and your ability to analyze data quickly. I think in the next, you know, 6 to 12 months, we’re going to have a totally different landscape for how people are buying like trading media also, you know, one more final thought about all of this is that a lot of the sophisticated tooling and pipes that we have are only accessible towards the largest advertisers today. And I think that you’ll pretty quickly see a democratization of the ability for anybody to just buy programmatic ads, whether you’ve got a $20 a month budget or a $20 million a month budget. Now, the ability to similar types of tools to access the right content across the web will start to be available towards a lot more folks outside of the existing, you know, kind of ad tech ecosystem. Christian Klepp  23:55 And I might be stating the obvious when I say this here, but that’s a good thing, isn’t it, because, I mean, I, again, I came out of this industry, and I know that, like, you know, if you wanted to advertise in the New York Times, for example, right? Like, how expensive that would be, or, or anything that was print, right? And then they migrated all that to digital, and then it still wasn’t, it still wasn’t affordable. It was, it was cheaper than print, but still not like, exactly like, you know, yeah, I wonder, wonder if they’ll be worth the investment or not. And then now you have this, this push towards the democratization of all of this through AI and machine learning and, and I do think that you know, for all the the scare mongering that you know people are doing now with, with, oh, you know, all this stuff around AI, I do think that that part certainly will be advantageous to to B2B companies and to marketing in general. Brendan Norman – Classify  24:49 Great. I mean, yeah, optimistically, I think I’m excited about the entire landscape changing because it does a couple things. It allows for much more contextually relevant ads. I know right now there’s only, let’s call it to the magnitude of like, 1000s, 10s of 1000s, maybe hundreds of 1000s, of campaigns and or brands that are able to use these pipes to reach the largest publishers. And all of a sudden you expand that out. You know, I think between meta and Google, they each have somewhere between 15 to 20 million unique advertisers on their platforms, and what that means is, you get really hyper specific ads. And it also means that, like, I might get a local ad for my hometown here for some restaurant that’s launching a promotion that I might only get here, and I might only get to your point, maybe not in the morning, but I’ll get in the evening. There’s a lot of different data sets around my identity, you know, the psychographic profile, contextual understanding of what I’m reading at that exact moment. And what it does a lot of things. It helps smaller brands get more traction, get more visibility. It also just helps improve the publisher experience, and like publishers, make more money. And then the user who’s consuming that content, reading the web page, watching a video, also has just a better experience. And then the other layer of that will continue to just go on, this narrative of agentic, tension, but the agents who are reading that content, watching that video for an end user. On the other side, are also able to interact with advertising content that’s very contextually relevant to the content that they’re consuming again, and it’s good for the storytelling of the advertiser and good for monetization of that publisher too. Christian Klepp  26:38 Absolutely, absolutely. Okay. So how can high fidelity curation? This is the next question, right? How can high fidelity curation make B2B companies more sustainable? And if you can just provide an example, Brendan Norman – Classify  26:54 Curations like, it’s such an interesting term, but you know, effectively, it’s just, it’s helping to use the word and the definition, the definition in the word, curate the right inventory to run an ad campaign on, and curate the right inventory and audiences. So it’s a really important part of the business. I think it involves a couple things. It involves front end targeting, of knowing who’s the back to that question, who’s the audience, and then what’s the right content, and then it also involves a lot of ongoing optimization. And I’ll say that there are some some interesting companies that that are really good at curation, who are building out the right automatic tools to think about more real time optimization, and it’s something that the really big social media companies do very well, like they’re constantly looking at lots and lots of signals when they’re running a campaign, and they’re looking at inventory and stitching together based on the signals that they’re acquiring around. Why certain campaigns do well, to your point, you know, when we’re testing that, selling that pair of skis to Christian, we’re testing a lot of things. We’re testing what he’s reading, you know, we’re testing maybe time of day. We’re testing, you know, where he is. There’s a lot of different elements on the back end that they will ingest and understand and then refeed into that targeting and optimization algorithm. And I think that that is one of the cool things that AI to use, like the air quotes, AI will help enable the processing of a lot of this data to just be a lot faster, be a lot more cost effective, and a lot of these systems that you know previously have been not accessible to the ad tech ecosystem, just because we we operate at such a crazy scale of 10s, hundreds of billions of requests and impressions and transactions that happen every single day. It’s very cost expensive if you’re processing all of that data and all these different signals, with the advancement of how the model cost is getting a lot less expensive, very quickly, not just from an LLM perspective, but then the foundational layers and the infrastructure layers, like we’re doing contextual intelligence as an infrastructure layer. There are inference layers that all kind of sit underneath the LLM and help inform an LLM understanding of that content. As those costs start to decrease, you’ll start to see a lot better performance from curation, just because, you know, it’s not as cost prohibitive, and we’ll be able to find that balance in terms of economics. Christian Klepp  29:45 Yeah, yeah, you hit the nail on the head there. Because, you know, I was just writing this down. You said faster, more cost effective and in my head, and you said it, it’s like, and at scale, like, you can scale this stuff faster, like, when I when I think back, like, years ago, when we, when we launched an ad campaign, and, you know, just the amount of effort, like, for the print and then the cost into, you know, the media placements and all of that and and just alone for like, one city, just just the amount of investment that was involved in all of that, right? Just think, thinking about that. It’s like, gosh, and then now you can scale all of that, like, even faster, because it’s because it’s digital, right? So it’s just such an incredible evolution. Like, I’m getting just as excited as you are man, I’m like, for this next question. Brendan, I’m not sure if you’re the type that likes to do this, but I need you to look into the crystal ball for a second here, right? Because we’re looking at, like, stuff that is, you know, the events that are yet to come, if I’m gonna that, make it sound a little bit suspenseful, but, um, the future of digital advertising, like, how do you think that could become less fragmented and more optimized with everything that we’ve talked about in this conversation. Brendan Norman – Classify  31:04 Yeah, I caution against, like, having any, any specific predictions, and more of, like, a framework for, I mean, for me, at least, yeah, more of a framework for how I think overall, jobs will change. I think that people will have to spend a lot less time doing a lot of the manual, rote tasks that they’re doing today. And, you know, kind of in parallel with what we’re seeing in terms of vibe coding and people’s ability to build product really quickly, design new web pages really quickly. Like, get ship things out quickly. I think a lot of the the infrastructure layer tools, or just call them like, you know, the like, chatGPT style, cloud-based tools, LLMs, we’ll see a lot deeper integration into existing advertising product. And what that does is it helps democratize the whole ecosystem. So I think it frees up people’s time to not have to do a lot of the basic administrative, reporting, manual, campaign, optimization type stuff, and it will help service a lot better insights. Ultimately, I think the industry grows, and I think it scales even faster. And, you know, cautiously, optimistically, I think that we, we will have back to building on the curation piece, and, you know, the advertiser, outcomes piece, publisher, monetization piece, user experience piece, I think that all those things will increase, and I I’m hopeful that with the integration of just better technology, embedding AI into a lot of these systems, it’s going to help steer us towards having better experiences across any type of Publisher content. I think that the advertisers will see better outcomes. I think that the people that are in this industry will get to think more creatively about how they’re, you know, building better creative storytelling, better reaching the right people with those stories. And my hope is that it just continues to expedite and grow the overall industry. Brendan Norman – Classify  33:17 That will be my hope as well. All right, get up on your soapbox here for a little bit. What is a status quo in your area of expertise? So anything that we’ve talked about now in this conversation, what’s the status quo that you passionately disagree with and why? Oh, you must have a ton. Brendan Norman – Classify  33:44 I definitely do. I mean, you know, Christian Klepp  33:48 just name one, just one, Brendan Norman – Classify  33:50 Like in any industry, you know, there’s always, there’s always the early adopters, you know, there’s always the kind of like the middle stack, you know, there’s always, like, the laggards. There’s definitely, you know, a smaller, but growing quickly, minority of folks who are really leaning into, you know, I’ll just call it AI, and then the agentic web, and there’s a lot of discussion right now in ad tech around like, what that means? I’m still hearing that. There’s a lot of skeptics who are kind of making fun of it, or, you know, trash talking about different protocols. Fine, like those are the folks that are absolutely going to get left behind. And I think a lot of those folks on the soapbox in the next 6 to 12 months will look back at, you know what they said, and we’ll all kind of say that didn’t age well, and you were not building this stuff. You weren’t fingers on keyboard or hands on keyboard. Vibe marketing, vibe targeting, building stuff like shipping new product and testing and iterating. What I what I don’t think, is that the really big platforms are just able to be super nimble and adapt to a lot of these new frameworks quickly, totally like the pipes will continue to stay there. I think that there will be startups that are more nimble, that can build and ship things, you know, proof of concepts, prototypes, get things out, learn from them, fail, iterate, and then start to scale meaningful businesses without having to rely on a lot of the existing infrastructure that exists today. Do I think the trade desk is, you know, going anywhere? No, do I think that they will, like, continue to be a valuable piece in this ecosystem, absolutely. And I think that they will ship things. I think that they’ll enable the industry like to build on top of of the pipes that they’ve already built. And at the same time, I think a lot of that rapid advancement will come from startups who are kind of proving that, like they don’t necessarily need the existing pipes and channels to be able to at the end of the day, you know, this whole ecosystem is about helping an advertiser surface their ad against the right content for a human or for an agent. And there have been a lot of folks kind of sitting in the middle for that space for a long time. One of my favorite stats, soapboxy stats, is that if an advertiser puts $1 in to the open web with a programmatic web, 35 cents comes out to a publisher, so 65 cents is being taken by some combination of middlemen, you know, who are collecting a margin for, you know, different services, also some version of fraud. There’s a lot of things that happen in between that and what I’m again, cautiously optimistic about, you know, like the big picture, AI, of facilitating, is the ability to reduce that margin so that, you know, advertiser puts $1 in. A lot more of that dollar comes out towards the publisher, I think big social media, you know, it’s around 70 cents comes out. So they take, you know, somewhere between 25 to 30 cents, which is kind of the value exchange of providing the services, all the targeting, all the technology that goes into supporting that, you know, as a more fair exchange. So I think what a lot of the folks on more of the startup on more of like the front end of the frontier tech in the space we’re excited about is getting to reduce a lot of that inefficiency and a lot of that margin in the middle, and helping more of that dollar show up towards the publisher where it should. Christian Klepp  37:34 Boom and there you have it. Man Brendan, this has been awesome conversation, so thanks again for your time, please. Quick intro to yourself and how folks out there can get in touch with you. Brendan Norman – Classify  37:45 Yeah. Brendan Norman, CEO co-founder at Classify, please. You know, hit me up on LinkedIn or shoot me an email. Check out our website, which is, you know, www.tryclassify.com. I’m happy to connect. You know, if you have questions about advertising from a publisher side, from an advertiser side. Love to chat about it. Christian Klepp  38:06 Sounds good. Sounds good once again. Brendan, thanks for your time. Take care, stay safe and talk to you soon. Brendan Norman – Classify  38:13 Cool. Thanks, Christian. Christian Klepp  38:14 All right. Bye for now.

This episode is a full “build a business in 40 minutes” demo showing how AI collapses what used to take teams (creative production + sales ops + support) into a handful of prompts. Samruddhi generates a high-production video ad in Google AI Studio using a JSON-style prompt framework, then spins up a working voice sales/support agent in Vapi via Claude Desktop + MCP—so the agent is created from a single prompt instead of clicking through the UI. The conversation also covers why “interfaces matter less” in an agent-first world, why workflow tools (like n8n) still have a role, and how memory layers like Mem0 unify context across channels (email/WhatsApp/etc.) so you can take actions without hunting.Timestamps0:00 — “Single person billion-dollar company” belief + AI driving 10x execution speed1:57 — Plan: create the ad in Google AI Studio (Veo 3.1) + build a voice agent using Vapi MCP via Claude Desktop2:42 — Smithery: marketplace for MCP servers3:39 — MCP for non-technical listeners: “like an API, but agents use it to talk to external services”4:22 — Inside Vapi MCP: tool list = APIs the agent can choose from5:06 — AI Studio setup: video generation playground + select Veo 3.16:16 — JSON prompting framework begins (structure → production-level output)6:28 — Keys: description, style, camera, lighting, environment, elements, motion, ending, text9:05 — Prompts/scripts can be AI-generated (humans provide guardrails)10:41 — Need an API key to generate videos in AI Studio10:54 — Ad review: strong realism; last segment looks AI-ish → iterate prompt13:05 — Install Vapi MCP via npx from Smithery + add Vapi API key13:46 — Claude Desktop: Vapi MCP appears under Connectors/Tools (not Claude web)14:05 — Prompt the agent build: “Fresh Pause” + role, tasks, FAQs, call flows18:23 — Testing: “Talk to assistant” starts a live call simulation19:20 — Deployment: assign a phone number; Vapi provides free/test numbers (up to a limit)21:57 — Mem0 / Supermemory: memory layer across apps/agents to keep context24:13 — Why memory layers help: fewer MCPs → less slowdown/hallucination; no need to specify where to search26:36 — MCPs + slide decks: mention of Gamma MCP via Claude27:34 — Future of n8n/Zapier: they persist, but prompting increasingly generates workflows31:38 — Prediction market trading algos (Kalshi/Polymarket) + AI improves speed/decision-making36:02 — Closing vision: help orgs 10x execution speed, especially non-technical leaders (40+) with domain expertiseTools & technologies mentionedGoogle AI Studio (Video Generation Playground) — Generate an 8-second video ad.Veo 3.1 — Google video model used for “production-level” output.JSON Prompting Framework — Structured key/value prompts for story, visuals, camera, lighting, motion, ending frame.Claude Desktop — Runs connectors/tools (including MCP servers).MCP (Model Context Protocol) — Lets agents call external services/tools based on intent.Smithery — Directory/marketplace for MCP servers.Vapi — Voice agent platform; create agents + assign phone numbers.Vapi MCP Server — Enables Claude to operate Vapi via prompts (create/list/configure).npx — Installs MCP server quickly from the terminal.API Keys — Required for AI Studio generation + Vapi authentication.Mem0 / Supermemory — Cross-channel memory layer to retrieve context automatically.Knowledge Graph — Underlying structure for semantic retrieval across interactions.Glean — Referenced as a comparison point for search/context retrieval.Gamma MCP — Example of generating slide decks via MCP.n8n / Zapier — Workflow automation tools discussed in an MCP-first future.OpenClaw — Mentioned as agent tooling that can help with steps like obtaining API keys.Kalshi / Polymarket — Prediction markets referenced in the trading/AI speed discussion.Subscribe at⁠ thisnewway.com⁠ to get the step-by-step playbooks, tools, and workflows.

Este episodio cuenta con el apoyo de Softwariza3Softwariza3 acompaña a las empresas en su día a día, ayudándolas a simplificar la gestión, automatizar tareas y ahorrar tiempo, con soluciones digitales siempre adaptadas a la normativa vigente.Implantación profesional, soporte experto y una forma honesta de hacer las cosas.Conoce más sobre Softwariza3: https://softwariza3.es/podcast-itnig/En esta tertulia nos sentamos con Eloy Montaña (CEO de Softwariza3 y Clavei) para hablar de cómo la regulación (Ley Antifraude / Verifactu) puede reventar, para bien o para mal, la operativa de miles de empresas: facturación “casi en tiempo real”, trazabilidad tipo “cadena” y el fin de los trucos de las facturas intermedias. También comentamos el coste oculto de los retrasos y cómo eso frena producto e innovación justo cuando el mercado va a mil. Luego entramos a lo que de verdad viene fuerte en 2026–2027: IA en asesorías y backoffice.Desde la idea de la “mega gestoría” (consolidación + procesos + IA) hasta por qué hay partes que van a caer antes que otras (y por qué nóminas en España es tan complejo). Y por el camino: cómo modernizas un ERP de décadas para llevarlo a cloud, y por qué el futuro de SaaS apunta más a cobrar por valor que por “asientos”.Cerramos con la parte incómoda: agentes que hacen cosas de verdad… y los riesgos reales. Hablamos de controles, seguridad, prompt injection y qué pasa cuando un agente “lee” internet y alguien intenta colarle instrucciones maliciosas (sí, incluyendo pagos). Y cómo la regulación europea puede ser a la vez freno y ventaja en esta carrera.

Wie hat dir die Folge gefallen?Gut

In episode 31 of Recsperts, I sit down with Elisabeth Lex, Full Professor of Human-Computer Interfaces and Inclusive Technologies at Graz University of Technology and a leading researcher at the intersection of recommender systems, psychology, and human–computer interaction. Together, we explore how recommender systems can become truly human-centric by integrating cognitive, emotional, and personality-aware models into their design.Elisabeth begins by addressing a common reductionism in the field: treating users primarily as data points rather than as humans with goals, emotions, memories, and cognitive boundaries. We revisit the origins of psychology-informed recommendation, including the Grundy system -the first recommender system, built nearly 50 years ago - which framed book recommendation through stereotype modeling. From there, we discuss how the community's focus shifted toward solving recommendation mainly as an algorithmic optimization problem, often sidelining richer models of human decision-making.We then map out the three major branches of psychology-informed RecSys - cognition-inspired, affect-aware, and personality-aware - and dive into practical examples. Elisabeth walks us through her work on modeling music re-listening behavior using cognitive architectures such as ACT-R (Adaptive Control of Thought–Rational) and shows how cognitive constructs like memory decay, attention, and familiarity can meaningfully augment standard approaches like collaborative filtering. We also explore how hybrid systems that combine cognitive models with collaborative filtering can yield not just higher accuracy but also more novelty, diversity, and clearer explanations.Our conversation also turns to user-centric evaluation. Elisabeth argues that accuracy metrics alone cannot tell us whether a system is genuinely helpful. Instead, we must measure attitudes, perceptions, motivations, and emotional responses - while carefully accounting for cognitive biases, UI effects, and users' lived experiences.Towards the end, Elisabeth discusses emerging research directions such as hybrid AI (symbolic + sub-symbolic methods), the role of LLMs and agents, the risks of replacing human studies with automated evaluations, and the responsibility our community has to understand users beyond their clicks.Enjoy this enriching episode of RECSPERTS – Recommender Systems Experts.Don't forget to follow the podcast and please leave a review.(00:00) - Introduction (03:15) - About Elisabeth Lex (07:55) - Grundy, the first Recommender System (09:03) - Bridging the Gap between Psychology and Modern RecSys (17:21) - On how and when Elisabeth became a Researcher (21:39) - Survey on Psychology-Informed RecSys (39:29) - Personality-Aware Recommendation (49:43) - Affect- and Emotion-Aware Recommendation (01:01:37) - Cognition-Inspired Recommendation and the ACT-R Framework (01:14:39) - Combining Collaborative Filtering and ACT-R for Explainability (01:21:26) - Human-Centered Design (01:26:15) - Further Challenges and Closing Remarks Links from the Episode:Elisabeth Lex on LinkedInWebsite of ElisabethAI for Society LabFirst International Workshop on Recommender Systems for Sustainability and Social Good | co-located with RecSys 2024Second International Workshop on Recommender Systems for Sustainability and Social Good | co-located with RecSys 2025HyPer Workshop: Hybrid AI for Human-Centric PersonalizationTutorial on Psychology-Informed RecSysACT-R: Adaptive Control of Thought-RationalPOPROX: Platform for OPen Recommendation and Online eXperimentationPapers:Elaine Rich (1979): User Modeling via StereotypesLex et al. (2021): Psychology-informed Recommender SystemsReiter-Haas et al. (2021): Predicting Music Relistening Behavior Using the ACT-R FrameworkMoscati et al. (2023): Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music RecommendationTran et al. (2024): Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session RecommendationGeneral Links:Follow me on LinkedInFollow me on XSend me your comments, questions and suggestions to marcel.kurovski@gmail.comRecsperts Website

Synopsis Iron Lung is about Markiplier in a submarine. That's the one thing that I can confidently say. there are a lot of other details, but none of them seem as salient. Sure all of the galaxy's, universe’s(?) suns have gone out, and there are factions of the remaining humans fighting for resources but that is really window dressing on Markiplier being in a submarine. Also, he's exploring an ocean of blood on some moon. Now you may ask me Bryce, how could they possibly functions as a society without a sun? How are they making new oxygen? Doesn't blood congeal or separate or something? how did they fine this moon without light? shut up nerd, Markilpier’s in a sub, now sit back and be scared. Review or Iron Lung before i go further, let me answer the main question first, yes this is better than Shelby Oaks. I will say that it's not as bad as I expected, but i wasn't blow. away either. The movie is basically all shot in one room, so that limitation let all the energy go into the story and the performance. It dod hold my attention for the most part, but it didn't deserve a two hour runtime. They could have edited out 40 mins and lose almost nothing. Markipliers performance was better than expected, but definitely leaned heavily into the melodramatic verging on overacting. The production design was well done except for the fact that it faithfully replicated the cartoonish UI of the video game, which I felt was a lazy choice. As far as delivering on suspense it did well. the movie was atmospheric and moody throughout. The ending goes full cosmic horror and feels like a good payoff. Score 4/10

Linktree: ⁠⁠https://linktr.ee/Analytic⁠⁠Join The Normandy For Additional Bonus Audio And Visual Content For All Things Nme+! Join Here: ⁠⁠https://ow.ly/msoH50WCu0K⁠⁠In this segment of Notorious Mass Effect, Analytic Dreamz delivers a concise analytical breakdown of Overwatch Season 1 (2026), Blizzard Entertainment's relaunch dropping "Overwatch 2" for a unified title with annual Season-1 cycles. Available on PC (Battle.net, Steam), PS4/5, Xbox One/Series X|S, and Nintendo Switch—with Switch 2 upgrade planned—it launched February 10, 2026, at 11 a.m. PST (2 p.m. EST, 7 p.m. GMT, etc.).The free-to-play title exploded with a Steam peak of 165,651 concurrent players—over 2x the prior 75,608 record—averaging 30,000+ post-launch, ranking #17 on Newzoo (Jan 2026), surpassing Call of Duty, Battlefield 6, and Marvel Rivals.Steam reviews shifted from "Overwhelmingly Negative" (27% positive) toward "Mixed," praising hero influx, content refresh, and resurgence amid minor UI/balance bugs (e.g., Domina laser) fixed by Feb 13.Core 5v5 PvP features payload/control objectives in the "Reign of Talon" year-long arc (6 seasons). Season 1 adds Conquest meta-event (5 weeks: Overwatch vs. Talon factions, 75+ loot boxes, exclusive Echo skins); 5 new heroes (Tank: Domina—photon beam, shield regen; Damage: Emre—burst rifle, Emre—fire fans, burn amp; Support: Mizuki—ricochet blade, Jetpack Cat—permanent flight, biotic projectiles); sub-role passives (e.g., Tank Bruiser crit reduction); Stadium 6v6 mode; 3D UI/lobby; Mythics (Mercy Celestial, Juno Star Shooter, Mei); Hello Kitty crossover (Feb 10–23).Analytic Dreamz unpacks competitive meta disruption from 5-hero drop (10 planned for 2026), rapid dev pipeline (4–5 months/hero), story integration (map damage, cinematics), and roadmap (Season 2: 10th anniversary; Season 3: Japan Night map). This ecosystem reset boosts engagement, narrative immersion, and counters rivals via content velocity.Tune in for strategic takeaways on player retention and genre dominance.Support this podcast at — https://redcircle.com/analytic-dreamz-notorious-mass-effect/exclusive-contentPrivacy & Opt-Out: https://redcircle.com/privacy

In our latest Open Source Startup Podcast episode, co-hosts Robby and Tim talk with Magnus Müller, the Co-Founder & CEO of Browser Use - the platform that makes web agents come to life. Their open source, browser-use, has almost 80K stars on GitHub and is widely adopted. This episode dives into the unexpected rise of an open-source browser automation project that took off during Y Combinator - while many similar projects before and after it never gained traction. The founder reflects on why: delivering a “magical moment” fast. Early demos showing AI controlling a browser, inspired by trends like OpenAI's Operator, and immediately clicked with people. What began as a developer-only Python library evolved into a hosted product as non-technical users - from sales teams to startups - wanted access. Along the way, the team leaned into controversial but compelling use cases, like AI applying for jobs on your behalf, which sparked conversation and accelerated growth. The core challenge they focused on solving was reliability: unlike deterministic automation scripts, AI agents can behave unpredictably, making trust and repeatability central problems to overcome.The long-term vision goes beyond UI automation toward agents that can skip the browser entirely and interact directly with website servers through structured actions. But the conversation isn't just about infrastructure. The founder admits that early growth came mostly from building and talking to users, while recent months have been dedicated to storytelling and marketing rather than coding. A personal through-line emerges as well: learning to replace defensiveness with curiosity - questioning assumptions, staying open to feedback, and continuously refining both the technology and the narrative around it.

We're keeping the AI Tools series rolling with Adir Traitel, entrepreneur, product leader, and early adopter of just about every vibe coding tool out there. Adir joins Matt and Moshe to share hard‑won lessons from building real apps with v0, Bolt, Replit, Figma Make, and more, all while running his own startup and consulting on product builds across industries.From his early days in project management and mobile app startups, through work with companies like Moovit and across FinTech, AgTech, and credit scoring, Adir has consistently been the “try it first” person for new build tools. In this episode, he breaks down what these platforms actually do well, where they fall short, and how product managers can use them responsibly for experiments, prototypes, and beyond.Join Matt, Moshe, and Adir as they explore:Adir's journey from PM and founder to heavy user of vibe coding tools in his current startupHis 3-layer view of the ecosystem: AI dev assistants (Cursor, Antigravity, Claude Code), front-end mockup tools (v0, Figma Make), and full‑product builders (Lovable, Base44, Bolt, Replit)V0: where it shines for quickly building functional UIs (like his electricity consumption app) and where it starts to crackLovable: great for sites and simple flows, but not ideal for complex SaaS or CRM‑like productsBolt: fun and fast for concepts, but why it never got him close to productionReplit: stronger agents and capabilities, but weaker UI output and surprising backend defaults that can get very expensive very quicklyFigma Make and Google Stitch: when design quality trumps everything else, especially for SaaS interfacesThe real costs of vibe coding: AI token spend, hosting/pricing traps, and why production economics matter as much as build speedWhat his “dream product” would look like, including multi‑agent environments, better security/privacy, and built‑in QA and CI/CDHow all this is reshaping the product management role, and why curiosity and tool fluency are becoming must‑have skillsAnd much more!Want to connect with Adir or learn more?LinkedIn: https://www.linkedin.com/in/adirtraitel/ Website: https://adirtraitel.com/You can also connect with us and find more episodes:Product for Product Podcast: http://linkedin.com/company/product-for-product-podcastMatt Green: https://www.linkedin.com/in/mattgreenproduct/Moshe Mikanovsky: http://www.linkedin.com/in/mikanovskyNote: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️

Watch the full video interview here Annual sustainability and ESG reporting is now becoming a necessity for many businesses, whether driven by region specific regulations and legislation, industry expectations or client demand.  However, doing so is definitely easier said than done. It requires a complex network of data being gathered from multiple sources which then needs to be collated, analysed and summarised in a cohesive report for leadership and possible public publication. Thankfully, there have been developments in new AI driven technology that can help ease this annual burden, allowing you to focus on utilising the results to make meaningful sustainability impacts. In this episode Mel Blackmore is joined by Darayush Mistry, Head of Product at Pulsora, to discuss how AI can make a difference in ESG and sustainability reporting, including its benefits, pitfalls and the balance of utilising AI while considering its environmental impact. You'll learn ·      Who is Darayush? ·      Who are Pulsora? ·      When did Darayush realise how AI could be utilised for ESG and sustainability reporting? ·      What are the positives of AI in this space? ·      Why is AI for ESG and sustainability reporting becoming more necessary? ·      What are the risks involved in using AI for ESG and sustainability reporting? ·      Where is AI making a real difference in reporting? ·      What parts of ESG and sustainability reporting need human judgement? ·      How does AI help collate data from multiple sources? ·      How might regulators react to AI being utilised in reporting? ·      How can businesses utilise AI while still considering it's environmental impact? ·      Darayush's advise to sustainability leaders looking to explore AI solutions   Resources ·      Pulsora ·      Darayush Mistry ·      Carbonology   In this episode, we talk about: [00:25] Episode Summary – Mel is joined by Darayush Mistry, Head of Product at Pulsora to discuss the use of AI tools in ESG and Sustainability reporting, how you can leverage this technology and what risks you need to be aware of before doing so. [02:40] Who is Darayush Mistry? Darayush has been working with enterpirise software for the past 2 decades. This technology is used by companies to help operationalise their business. He began his career at a company called Siebel Systems, which operated in the CRM space, spending 10 years there before moving onto the world of sustainability. Darayush recalls how everyone was so used to working from a set of spreadsheets just 20 years ago, whereas now most will use a central CRM for business operations. This is an area that sustainbilty reporting seems to have lagged behind, with many still trying to collate their data from multiple spreadsheets and other external sources rather than having a dedicated central system. This is why he was eager to work with Pulsora, to bring similar solutions to businesses as he once had with CRM's in the past. [05:25] Who are Pulsora? Pulsora are an AI-forward SaaS (software as a service) platform. The Pulsora platform helps businesses to operationalise their sustainability initiatives, which includes data collation, calculation and reporting features. This is set up for scope 1, 2 and 3 level reporting, with considerations for climate related goals, waste water monitoring, biodiversity and policy oriented information. Darayush's role as Head of Product means he sits at the intersection between customers and Pulsora's engineering and design teams. His job is to ensure that whatever Pulsora created ultimately provides value to their customers in the form of successful sustainability outputs. [07:50] When did Darayush realise how AI could be utilised for ESG and sustainability reporting? Darayush can pinpoint a time four years prior when he first stepped into a more sustainability focused role, speaking to the co-founders of Pulsora back in 2021 they were sharing experiences of using the then early versions of AI tools such as ChatGPT and Gemini. It clicked for them then that they could do something similar for sustainability reporting, making it as easy as possible while still being accurate. It wasn't until 2 years later that they had a product to launch with Pulsora AI in late 2024. This initial product allowed users to write long from narrative responses for carbon disclosures. Regulations like CSRD require a comprehensive disclosure, but not everyone is an expert in parsing the data to write that, so Pulsora AI helped get past that writers block, to give people the building blocks for that professional disclosure. [11:55] What are the positives and negatives of AI in this space? The biggest benefits include: ·      Giving professionals and sustainability teams more time back to achieve their desired outcomes. ·      Cutting down on spending time in spreadsheets and on calculations on an annual basis. ·      Reduction of repetitive tasks ·      Ease of data collection from multiple sources and locations ·      Ease of data calculation ·      Allowing for pre-audit of data using AI tools ·      Highlighting data gaps when rationalizing the data [17:20] Why is AI for ESG and sustainability reporting becoming more necessary? People are starting to move on from the mindset of 'Let's try AI' to 'Let's use AI'. Time is one of the most precious resources we have, and any tool that can help accelerate more mundane tasks so that people can focus on making results happen should be a priority. Sustainability teams are under increasing pressure to produce tangible results, something that can be made easier with the help of AI tools. [20:06] What are the risks of using AI in ESG and Sustainability reporting? Don't treat AI as this magic wand, it's a tool you can leverage. At the moment, it's good at certain tasks, but it cannot act on its own.    In order to progress, sustainability teams need to push on the initiatives to produce results. People know their business best, and though AI can infer certain information and produce a result, it may not always be the best solution for you. You still need that human input into areas such as strategy and action planning. Darayush reminds us of Amara's Law: "We as humans severely overestimate technology outcomes in the short-term, and severely underestimate that in the long-term" Don't fall into the trap of thinking AI can do everything. [22:30] Where is AI making a real difference in reporting? Data collection, ad-hoc sustainability reporting and providing insights into the data provided. It can also help with providing a starting point for carbon disclosures or options for various strategies that you could explore. Currently, the biggest one is data collection, as it can help do this efficiently and consistently, allowing for improved accuracy in your overall sustainability data. [25:20] What parts of ESG and sustainability reporting need human judgement? Darayush states that these are complementary to each other, it should never be all of one and none of the other. There will be elements that need more human in the loop and areas where it's required less. It's applicable in degrees. One example of where the human input will be higher is in completing a materiality assessment and figuring out how to execute your decarbonisation strategy, which will require your knowledge and experience of how the business operates, it's core values and what your ultimate goals are. AI can do the heavy lifting in areas such as sustainability reporting, as it can collate all the data and create initial reports very fast. But, at the end of the day, humans still need to understand these outputs and provide their own judgement. 'AI' today isn't true AI, they're LLM's with a great capacity to collect data, analyse it and provide outputs that can be starting points. It cannot replace human judgement, as we provide the nuance in context and experience needed to apply those results effectively. AI responses operate in a perfect world where everything is an easy step by step process, which we all know does not reflect reality. [29:40] How does AI help collate data from multiple sources? Older technologies like OCR (optical Character Recognition) was the go to years ago when scanning various different documents like spreadsheets, PDF's, receipts etc. This required specific code to be written to read these docs accurately, this would then feed into pipelines to bring this data together. This code was quite rigid, so any changes to document layouts would cause things to break. AI in comparison is much more adaptable, it's capable of reading much more natural language and extracting what's required for its designated task. It also provides a much more friendly UI (user interface), meaning you don't need an IT specialist to utilise the technology. [33:15] How might regulators react to AI being utilised in reporting? Based on Darayush's previous experience in the finance sector when people were using dedicated platforms for financial reporting, the regulators didn't care where the data came from or how it was collated, they just card if it was accurate.  Regulators want transparency, accuracy and a big part of this is providing an audit trail so they can see where the data came from. They simply want businesses to follow their guidelines, the how you get from A to B is of little importance so long as the result is accurate. If anything, the existence of these tools will raise the bar of expectations from regulators, as businesses should be able to provide the required information with these tools readily available. [36:30] How can businesses utilise AI while still considering it's environmental impact? – AI can certainly aid the sustainability industry in certain areas, such as reporting, but it's a resource intensive tool. It consumes a lot of energy and water. Like with most emerging technology, the sustainability impact usually isn't addressed until much later. Much like with mobile phones, which create tonnes of E-waste every year, not to mention the mined material required to make them. It's factors like this which eventually get regulators involved to help reduce the overall harm caused. AI is yet to go through this evolution, but both regulator and consumer pressure is building to reduce the impact of AI. This will inevitably lead to innovation as companies seek to find more sustainable ways to cool data centres and reduce the resource burden. On the flip side, AI can help save energy in other ways, such as time taken to complete the tasks for a human, which will include travelling to an office and amount of time they use a device for the task. This also has its own carbon footprint, which can comparatively be reduced by using AI to complete the tasks in minutes as opposed to hours or days. The bottom line as of the start of 2026 is, we know there is a resource issue when it comes to AI, and companies are looking at better ways to address it as the technology develops. [42:20] Darayush's advise to sustainability leaders looking to explore AI solutions – Identify a problem space where you can apply AI in a measured way an start using it. The only way you can find out how it impacts you is to use the technology.   Currently, AI shines is areas such as collating data from multiple sources and locations, so if that's an issue you're tackling where sustainability reporting is concerned, that's a good place to start with utilising AI.   If you'd like to learn more about Pulsora, check out their website.   We'd love to hear your views and comments about the ISO Show, here's how: ●     Share the ISO Show on Twitter or Linkedin ●     Leave an honest review on iTunes or Soundcloud. Your ratings and reviews really help and we read each one. Subscribe to keep up-to-date with our latest episodes: Stitcher | Spotify | YouTube |iTunes | Soundcloud | Mailing List

Shashank Bijapur, co-founder and CEO of Spotdraft, explores the transition from the archaic, manual world of legal practice to the high-velocity domain of B2B SaaS. In this episode, we strip away the jargon surrounding "LegalTech" to reveal how Spotdraft powers the invisible infrastructure of global commerce - from airport leases to ride-sharing agreements. Shashank provides a masterclass on finding product-market fit in the mid-market, the reality of AI's role in high-stakes legal workflows, and the strategic pivot from technical perfection to market-driven iteration.Key Takeaways1. The "Aha Moment": Identifying Stagnation in Essential Industries- Digital Lag: While photography (Adobe) and accounting (Intuit) underwent digital revolutions decades ago, legal innovation peaked in 1993 with Microsoft Word's "Track Changes."- The Opportunity Gap: Identifying ubiquitous, paper-heavy processes that remain manual despite technological advancements is the strongest signal for a SaaS disruption.- Democratic Software: The goal isn't just to replace a lawyer; it's to turn complex legal processes into software that is as accessible and intuitive as a consumer app.2. GTM Strategy: The Power of Mid-Market Focus- Avoid the "Gambler's Fallacy": Shashank emphasizes the importance of trashing unusable early products rather than doubling down on a failing idea.- Homogeneity Matters: The US is the primary target for Indian SaaS due to its massive, homogeneous market, which allows for a repeatable ecosystem and faster flywheels.- The Mid-Market Sweet Spot: Avoiding the high-churn "small business" trap and the "unobtainable enterprise" early on leads to a focused GTM where legal teams (the true buyer persona) have decision-making power.3. The Founder's Dilemma: Accuracy vs. Speed- Legal Training vs. Startup Reality: Lawyers are trained for 100% accuracy; founders must embrace "fail fast." Overcoming the urge to pursue a "perfect product" is essential to gathering user feedback.- Technical Maturity: In 2017, the promise of AI exceeded the technology's capability. Spotdraft pivoted to building robust workflows first, capturing the data needed to make today's LLM integrations effective.- The Talent Moat: When a founder lacks specific functional knowledge (like GTM or engineering), the solution is "talent density"—hiring highly motivated experts who believe in the mission.4. The Future of AI in High-Stakes Legal- The End of "Form Filling": UI is shifting from manual data entry to conversational interfaces where users describe an outcome, and the AI configures the workflow.- Context is King: General LLMs lack company-specific context. AI's value in SaaS comes from mapping global laws against a company's specific historical data and standards.- Humans in the Loop: AI will handle "grunt work" and pattern recognition, but $1M+ deals will still require a human handshake and strategic negotiation for at least the next decade.About Spotdraft:Spotdraft is an AI-driven, end-to-end contract automation platform designed to clear the "madness from quote to cash." It helps businesses of all sizes—from startups to giants like Uber and Airbnb—create, manage, and analyze contracts seamlessly.Chapters:00:10 - Introduction00:50 - Journey from Lawyer to SaaS CEO03:34 - The "Aha Moment" for LegalTech07:09 - Spotdraft's Hidden Role in Everyday Life11:34 - GTM Strategy: Building from India for the US18:24 - Balancing Legal Risk with Founder Speed22:56 - How LLMs are Changing Legal Workflows30:22 - Lightning Round: Lessons Learned & AI ToolsVisit our website - https://saassessions.com/Connect with me on LinkedIn - https://www.linkedin.com/in/sunilneurgaonkar/

Thanks Pressable for supporting the show! Get your special hosting deal at https://pressable.com/wpminuteBecome a WP Minute Supporter & Slack member at https://thewpminute.com/supportOn this episode of The WP Minute+ podcast, Eric is joined by Rachel Berry, Head of Client Services at Filter. Rachel fills us in on the role of WordPress as an enterprise solution. The discussion also looks at the importance of client relationships, the benefits of WordPress in the enterprise space, and the challenges of changing perceptions about the platform. Rachel shares insights on leveraging AI in client services and offers advice for agencies working in the enterprise market. Takeaways:Filter is a digital-first agency focusing on UX, UI design, and WordPress development.Rachel's role bridges the gap between client needs and solution delivery.AI is transforming client servicing by simplifying communication and project management.WordPress offers flexibility and cost-effectiveness for enterprise clients compared to proprietary solutions.Changing perceptions about WordPress is crucial for its adoption in enterprise environments.Clients often prioritize outcomes over technical features in their solutions.Building strong client relationships is essential for long-term success.Effective communication and trust are key to client retention.Agencies should focus on understanding client pain points holistically.The future of AI in client services is promising but requires careful implementation.Important Links:Filter's websiteConnect with Filter: LinkedIn | YouTubeFilter AI PluginThe WP Minute+ Podcast: thewpminute.com/subscribe ★ Support this podcast ★

Vincent Warmerdam is a Founding Engineer at marimo, working on reinventing Python notebooks as reactive, reproducible, interactive, and Git-friendly environments for data workflows and AI prototyping. He helps build the core marimo notebook platform, pushing its reactive execution model, UI interactivity, and integration with modern development and AI tooling so that notebooks behave like dependable, shareable programs and apps rather than error-prone scratchpads.Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletterMLOps GPU Guide: https://go.mlops.community/gpuguide// AbstractVincent Warmerdam joins Demetrios fresh off marimo's acquisition by Weights & Biases—and makes a bold claim: notebooks as we know them are outdated.They talk Molab (GPU-backed, cloud-hosted notebooks), LLMs that don't just chat but actually fix your SQL and debug your code, and why most data folks are consuming tools instead of experimenting. Vincent argues we should stop treating notebooks like static scratchpads and start treating them like dynamic apps powered by AI.It's a conversation about rethinking workflows, reclaiming creativity, and not outsourcing your brain to the model.// BioVincent is a senior data professional who worked as an engineer, researcher, team lead, and educator in the past. You might know him from tech talks with an attempt to defend common sense over hype in the data space. He is especially interested in understanding algorithmic systems so that one may prevent failure. As such, he has always had a preference to keep calm and check the dataset before flowing tonnes of tensors. He currently works at marimo, where he spends his time rethinking everything related to Python notebooks.// Related LinksWebsite: https://marimo.io/Coding Agent Conference: https://luma.com/codingagentsHyperbolic GPU Cloud: app.hyperbolic.ai~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]MLOps GPU Guide: https://go.mlops.community/gpuguideConnect with Demetrios on LinkedIn: /dpbrinkmConnect with Vincent on LinkedIn: /vincentwarmerdam/Timestamps:[00:00] Context in Notebooks[00:24] Acquisition and Team Continuity[04:43] Coding Agent Conference Announcement![05:56] Hyperbolic GPU Cloud Ad[06:54] marimo and W&B Synergies[09:31] marimo Cloud Code Support[12:59] Hardest Code to Generate[16:22] Trough of Disillusionment[20:38] Agent Interaction in Notebooks[25:41] Wrap up

On Iowa Politics is a weekly news and analysis podcast that aims to recreate the kinds of conversations that happen when you get political reporters from across Iowa together after the day's deadlines have been met. Tackling anything from local to state to national, On Iowa Politics is your weekly dose of analysis and insight into the issues affecting Iowa.This week on the On Iowa Politics podcast, we discuss multiple pieces of controversial legislation at the Iowa Capitol, federal immigration enforcement, and a bill to lure the Chicago Bears to Iowa.This episode was hosted by the Gazette Des Moines Bureau Chief Erin Murphy. It features Gazette Deputy Bureau Chief Tom Barton, Lee Des Moines Bureau Chief Maya Marchel Hoff, Jared McNett of the Sioux City Journal and Gazette columnists Althea Cole and Todd Dorman.Read the articles mentioned in this episode: FIRST FROM THE JOURNAL: Sioux City Rep. J.D. Scholten not seeking reelectionhttps://siouxcityjournal.com/news/state-regional/government-politics/article_c7ffa366-f7bc-4e71-8b27-a567e7993c44.htmlBill advanced by Iowa lawmakers would require in-person doctor visits for medication abortionhttps://qctimes.com/news/state-regional/government-politics/article_72d193d4-bb21-4d62-ae33-d140dacf9538.htmlTransgender Iowans protest GOP billshttps://www.thegazette.com/state-government/love-not-hate-iowans-protest-gop-bills-targeting-transgender-protections-and-parenting-rules/UI student accused of ‘trying to assault' lawmaker as she was escorted out of DEI hearing: https://www.thegazette.com/higher-education/ui-student-accused-of-trying-to-assault-lawmaker-as-she-was-escorted-out-of-dei-hearing/ Opinion: Reynolds can't stop kicking trans Iowanshttps://www.thegazette.com/staff-columnists/reynolds-cant-stop-kicking-trans-iowans/Could Iowa become Chicago Bears' next home? Bill aims to lure NFL teamhttps://www.thegazette.com/state-government/iowa-lawmakers-pitch-stadium-incentives-to-lure-chicago-bears-across-state-lines/This episode was produced by Gazette Social Video Producer Bailey Cichon.

In this episode from 20VC, Harry Stebbings talks with Anish Acharya, general partner at a16z, about the future of SaaS in an AI world. Anish argues that software is completely oversold and that the general story about vibe coding everything is flat wrong. They discuss why SaaS switching costs are actually going down thanks to coding agents, where startups versus incumbents will win, and whether the apps layer or foundation models will capture more value. They also cover agent overhype, the changing UI paradigm, what defensibility looks like now, and why boring wins versus weird wins in this product cycle. Resources:Follow Anish Acharya on X:  https://twitter.com/illscienceFollow Harry Stebbings on X:  https://twitter.com/HarryStebbings Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

Portnox is an enterprise access control platform that eliminates passwords and enforces zero trust security. The company was bootstrapped for over a decade, plateauing at a few million in ARR before investors brought in Denny LeCompte as CEO four years ago. Since then, Portnox has grown 8x. But this episode isn't about that growth story. Denny, a former cognitive scientist and professor who taught psychometrics, uses his scientific background to systematically dismantle Net Promoter Score—explaining why it's methodologically flawed, how it misleads organizations, and which metrics actually correlate with business performance. This is a contrarian take grounded in measurement science, not marketing opinion. Topics Discussed: The fundamental psychometric flaws in NPS: why single-item questionnaires are unreliable and why throwing out 7s and 8s violates basic statistical principles How NPS scores fluctuate based on survey UI presentation independent of actual customer sentiment Why NPS creates incentive structures that encourage gaming rather than improving customer outcomes The case for gross revenue retention and net revenue retention as the only ungameable metrics that matter How measuring human behavior changes that behavior (the Heisenberg principle applied to business metrics) Why investors care about retention rates above 90% but don't ask about NPS scores GTM Lessons For B2B Founders: Single-item questionnaires violate measurement principles: Denny's background in psychometrics immediately flagged NPS as unreliable. One-item measures lack the redundancy needed for reliability, and the methodology of throwing out middle responses (7s and 8s) then subtracting detractors from promoters is statistically nonsensical. At a previous company with thousands of data points, he observed NPS scores drop and rise based solely on how the survey rendered on the page—no business changes, just UI differences. When presentation affects your metric independent of the underlying construct, your instrument is broken. Founders with technical backgrounds should trust their instincts when measurement methodology feels scientifically unsound. Compensation drives behavior more than metric accuracy: Portnox structures customer success compensation as 50% gross revenue retention and 50% net revenue retention. These are determined by finance and can't be manipulated. Denny had to rein in his CS team when they became overly focused on time-to-value because any number you give a team becomes their obsession. With NPS, teams game survey timing, cherry-pick recipients, and optimize for score rather than outcome. This is the Heisenberg principle applied to business: measuring changes the behavior. Choose metrics where gaming the number aligns with improving actual business outcomes. Investors evaluate retention rates, not satisfaction surveys: When Denny presents gross retention above 90%, investors don't ask about NPS. Renewal behavior reveals actual satisfaction—customers voting with budget rather than survey responses. The test for any metric: "What are we doing differently if this number is up versus down?" If it doesn't drive distinct actions or reveal information not already visible in financials, eliminate it. NPS often becomes a number that exists because "we've always measured it," inherited from previous leadership without questioning its utility. Question inherited practices ruthlessly: NPS gained adoption through Harvard Business Review credibility in 2003 and consulting firms building practices around it. The promise of "one number you need" appeals to executives wanting simple solutions. But herd behavior—"everyone else measures it"—perpetuates bad methodology. Denny's advice to founders stuck with NPS: give your team something else to focus on (gross retention is straightforward: don't let customers churn), then stop doing it. Sometimes you need to point to external validation to break internal momentum. The question isn't whether NPS correlates somewhat with growth—it's whether better alternatives exist that can't be gamed. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPLSMFimtv0riPyM

This Day in Legal History: Nelson Mandela ReleasedOn February 11, 1990, Nelson Mandela was released from Victor Verster Prison in South Africa after 27 years of incarceration, marking a seismic shift in the country's legal and political landscape. Mandela's release followed a period of secret negotiations between the apartheid government and the African National Congress (ANC), and it signaled the beginning of the end of apartheid—a system of institutionalized racial segregation and oppression upheld by law. His imprisonment had become a global symbol of the fight against racial injustice and was frequently challenged by international human rights organizations and legal scholars as a violation of fundamental human rights.Mandela had been convicted in 1964 of sabotage and other charges under South Africa's Suppression of Communism Act, following the infamous Rivonia Trial. He was sentenced to life imprisonment, spending much of his sentence on Robben Island under harsh conditions. Over the decades, growing international sanctions and internal unrest made apartheid increasingly untenable.Then-President F.W. de Klerk's government began rolling back apartheid legislation in the late 1980s, and on February 2, 1990, de Klerk announced the unbanning of the ANC and his intention to release Mandela. Just nine days later, Mandela walked free, delivering a speech in Cape Town that emphasized reconciliation, peace, and the continuation of the struggle for full democratic rights.Mandela's release was not just a political milestone—it was a legal one, too. It reflected a move away from laws based on racial supremacy and toward a constitutional order grounded in human rights. This transformation would culminate in South Africa's 1996 Constitution, often lauded for its rights-based framework and independent judiciary.The Trump administration's plan to repeal the EPA's 2009 endangerment finding—the scientific basis for regulating greenhouse gases under the Clean Air Act—could reignite legal efforts to hold polluters accountable through public nuisance lawsuits. That finding enabled the EPA to regulate emissions from vehicles and power plants, but its reversal removes the legal framework that had previously shielded companies from such claims under a 2011 Supreme Court ruling. In that decision, the Court held that the EPA's authority under the Clean Air Act displaced common-law nuisance suits against emitters. Without that EPA oversight, legal scholars believe plaintiffs may now argue that the courts are once again an appropriate venue for these claims.Public nuisance lawsuits, typically filed by states or municipalities, seek to hold companies accountable for harms caused to community health and safety. These cases have been historically difficult to win due to challenges in proving direct causation, but experts say the new regulatory gap could encourage a wave of litigation. Industry groups like the Edison Electric Institute have warned that repealing the endangerment finding could expose utilities to costly legal battles. While federal courts had largely blocked such claims, state courts have shown more openness, and the shift in federal policy may strengthen these legal efforts. Environmental advocates may now have renewed leverage to push power companies and other emitters into court.Trump's repeal of climate rule opens a ‘new front' for litigation | ReutersAttorney General Pam Bondi is scheduled to testify before the House Judiciary Committee this week amid intensifying legal scrutiny over the Justice Department's management of the Jeffrey Epstein files. Lawmakers are expected to question Bondi about what they view as excessive redactions and the DOJ's withholding of key documents, actions that may conflict with a bipartisan federal law passed in 2025 mandating the broad release of Epstein-related materials. Legal analysts suggest the DOJ's reliance on legal privileges—such as investigatory and deliberative process exemptions—to justify redactions could face stiff challenges in court or through congressional oversight powers.The situation raises constitutional tensions between legislative oversight and executive privilege, particularly as the House panel, now under Republican control, examines whether the DOJ is shielding politically sensitive information. Some members of Congress have accused the Department of undermining transparency and potentially violating the statutory intent of the Epstein Disclosure Act, which narrowed the DOJ's discretion in withholding records tied to convicted sex offenders or deceased suspects like Epstein.Bondi's DOJ has been accused of prioritizing partisan enforcement over institutional neutrality, illustrated by failed prosecutions of Trump critics and an aggressive posture on immigration and protest-related cases. The sidelining of the DOJ's civil rights division and the refusal to investigate federal shootings has further fueled concerns over selective enforcement and erosion of prosecutorial independence. Bondi's testimony will serve as a key moment to defend the Department's use of legal redactions and its broader approach to politically charged prosecutions.Bondi to face questions on Epstein files in House testimony | ReutersInstagram chief Adam Mosseri is set to testify in a Los Angeles courtroom this week in a groundbreaking lawsuit that could reshape how U.S. law approaches the intersection of product design and youth mental health. The case centers on a 20-year-old plaintiff who alleges she became addicted to Instagram as a child due to its deliberately addictive interface—particularly the “endless scroll” feature that loads content continuously to hold user attention. Her lawyers argue that Instagram's design choices amount to a form of negligent product engineering that failed to account for known risks to children.This case raises novel legal questions: Can user interface (UI) design be treated as a defective product under tort law? Can tech companies be held liable not just for content but for the architecture of the platforms themselves? If the court accepts these arguments, it could establish precedent for treating addictive design as a public health harm similar to tobacco or opioid marketing practices.Mosseri is expected to face questioning over internal documents that, according to the plaintiff, show Meta was aware of the app's mental health impact on vulnerable teens. Meta counters that these documents reflect efforts to mitigate harm, not evidence of negligence. Still, the case may test the limits of Section 230 immunity, as it focuses not on third-party content, but the platform's own design—potentially sidestepping the traditional legal shield for tech companies.Hundreds of similar cases are pending, and this trial may serve as a bellwether for litigation nationwide. International developments, including Australia's ban on social media for children under 16, suggest this is a growing legal frontier.Instagram's leader to testify in court on app design, youth mental health | ReutersNovo Nordisk's recent patent infringement lawsuit against Hims & Hers marks a pivotal legal development in the pharmaceutical industry's battle with telehealth providers distributing compounded drugs. The suit, filed in Delaware federal court, targets Hims' sales of compounded semaglutide—the active ingredient in Wegovy and Ozempic—claiming these formulations infringe Novo's patents. While compounding is allowed under certain FDA exemptions, those exemptions do not shield pharmacies or telehealth platforms from patent liability. This case challenges the assumption that FDA compliance protects against infringement claims, exposing a gray area where regulatory and intellectual property regimes collide.Historically, brand-name drugmakers focused on trademark challenges over how compounded drugs were marketed. Novo's move into patent litigation signals a strategic escalation: it's not about branding anymore—it's about the act of making and selling the compound itself. Experts highlight that this is likely the first time a brand drug company has pursued patent claims directly against a compounding pharmacy or telehealth distributor, suggesting the industry now sees these entities as substantial commercial threats.The case also underscores a novel enforcement strategy: suing the telehealth platform facilitating sales rather than the dispersed network of compounding pharmacies, streamlining legal action and potentially setting precedent for centralized liability. Hims, already under regulatory scrutiny, had just halted plans to sell compounded semaglutide pills but remains a target due to its involvement in injectable forms.The outcome of this case may clarify how FDA-sanctioned compounding intersects with patent protections and could define the boundaries for how far telehealth companies can go in offering customized versions of patented drugs.Novo's GLP-1 Patent Suit Against Hims Takes Aim at Compounding This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.minimumcomp.com/subscribe

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss managing AI agent teams with Project Management 101. You will learn how to translate scope, timeline, and budget into the world of autonomous AI agents. You will discover how the 5P framework helps you craft prompts that keep agents focused and cost‑effective. You will see how to balance human oversight with agent autonomy to prevent token overrun and project drift. You will gain practical steps for building a lean team of virtual specialists without over‑engineering. Watch the episode to see these strategies in action and start managing AI teams like a pro. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-project-management-for-ai-agents.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In‑Ear Insights, one of the big changes announced very recently in Claude code—by the way, if you have not seen our Claude series on the Trust Insights live stream, you can find it at trustinsights. Christopher S. Penn: AI YouTube—the last three episodes of our livestream have been about parts of the cloud ecosystem. Christopher S. Penn: They made a big change—what was it? Christopher S. Penn: Thursday, February 5, along with a new Opus model, which is fine. Christopher S. Penn: This thing called agent teams. Christopher S. Penn: And what agent teams do is, with a plain‑language prompt, you essentially commission a team of virtual employees that go off, do things, act autonomously, communicate with each other, and then come back with a finished work product. Christopher S. Penn: Which means that AI is now—I’m going to call it agent teams generally—because it will not be long before Google, OpenAI and everyone else say, “We need to do that in our product or we'll fall behind.” Christopher S. Penn: But this changes our skills—from person prompting to, “I have to start thinking like a manager, like a project manager,” if I want this agent team to succeed and not spin its wheels or burn up all of my token credits. Christopher S. Penn: So Katie, because you are a far better manager in general—and a project manager in particular—I figured today we would talk about what Project Management 101 looks like through the lens of someone managing a team of AI agents. Christopher S. Penn: So some things—whether I need to check in with my teammates—are off the table. Christopher S. Penn: Right. Christopher S. Penn: We don’t have to worry about someone having a five‑hour breakdown in the conference room about the use of an Oxford comma. Katie Robbert: Thank goodness. Christopher S. Penn: But some other things—good communication, clarity, good planning—are more important than ever. Christopher S. Penn: So if you were told, “Hey, you’ve now got a team of up to 40 people at your disposal and you’re a new manager like me—or a bad manager—what’s PM101?” Christopher S. Penn: What’s PM101? Katie Robbert: Scope, timeline, budget. Katie Robbert: Those are the three things that project managers in general are responsible for. Katie Robbert: Scope—what are you doing? Katie Robbert: What are you not doing? Katie Robbert: Timeline—how long is it going to take? Katie Robbert: Budget—what’s it going to cost? Katie Robbert: Those are the three tenets of Project Management 101. Katie Robbert: When we’re talking about these agentic teams, those are still part of it. Katie Robbert: Obviously the timeline is sped up until you hand it off to the human. Katie Robbert: So let me take a step back and break these apart. Katie Robbert: Scope is what you’re doing, what you’re not doing. Katie Robbert: You still have to define that. Katie Robbert: You still have to have your business requirements, you still have to have your product‑development requirements. Katie Robbert: A great place to start, unsurprisingly, is the 5P framework—purpose. Katie Robbert: What are you doing? Katie Robbert: What is the question you’re trying to answer? Katie Robbert: What’s the problem you’re trying to solve? Katie Robbert: People—who is the audience internally and externally? Katie Robbert: Who’s involved in this case? Katie Robbert: Which agents do you want to use? Katie Robbert: What are the different disciplines? Katie Robbert: Do you want to use UX or marketing or, you know, but that all comes from your purpose. Katie Robbert: What are you doing in the first place? Katie Robbert: Process. Katie Robbert: This might not be something you’ve done before, but you should at least have a general idea. First, I should probably have my requirements done. Next, I should probably choose my team. Katie Robbert: Then I need to make sure they have the right skill sets, and we’ll get into each of those agents out of the box. Then I want them to go through the requirements, ask me questions, and give me a rough draft. Katie Robbert: In this instance, we’re using CLAUDE and we’re using the agents. Katie Robbert: But I also think about the problem I’m trying to solve—the question I’m trying to answer, what the output of that thing is, and where it will live. Katie Robbert: Is it just going to be a document? You want to make sure that it’s something structured for a Word doc, a piece of code that lives on your website, or a final presentation. So that’s your platform—in addition to Claude, what else? Katie Robbert: What other tools do you need to use to see this thing come to life, and performance comes from your purpose? Katie Robbert: What is the problem we’re trying to solve? Did we solve the problem? Katie Robbert: How do we measure success? Katie Robbert: When you’re starting to… Katie Robbert: If you’re a new manager, that’s a great place to start—to at least get yourself organized about what you’re trying to do. That helps define your scope and your budget. Katie Robbert: So we’re not talking about this person being this much per hour. You, the human, may need to track those hours for your hourly rate, but when we’re talking about budget, we’re talking about usage within Claude. Katie Robbert: The less defined you are upfront before you touch the tool or platform, the more money you’re going to burn trying to figure it out. That’s how budget transforms in this instance—phase one of the budget. Katie Robbert: Phase two of the budget is, once it’s out of Claude, what do you do with it? Who needs to polish it up, use it, etc.? Those are the phase‑two and phase‑three roadmap items. Katie Robbert: And then your timeline. Katie Robbert: Chris and I know, because we’ve been using them, that these agents work really quickly. Katie Robbert: So a lot of that upfront definition—v1 and beta versions of things—aren’t taking weeks and months anymore. Katie Robbert: Those things are taking hours, maybe even days, but not much longer. Katie Robbert: So your timeline is drastically shortened. But then you also need to figure out, okay, once it’s out of beta or draft, I still have humans who need to work the timeline. Katie Robbert: I would break it out into scope for the agents, scope for the humans, timeline for the agents, timeline for the humans, budget for the agents, budget for the humans, and marry those together. That becomes your entire ecosystem of project management. Katie Robbert: Specificity is key. Christopher S. Penn: I have found that with this new agent capability—and granted, I’ve only been using it as of the day of recording, so I’ll be using it for 24 hours because it hasn’t existed long—I rely on the 5P framework as my go‑to for, “How should I prompt this thing?” Christopher S. Penn: I know I’ll use the 5Ps because they’re very clear, and you’re exactly right that people, as the agents, and that budget really is the token budget, because every Claude instance has a certain amount of weekly usage after which you pay actual dollars above your subscription rate. Christopher S. Penn: So that really does matter. Christopher S. Penn: Now here’s the question I have about people: we are now in a section of the agentic world where you have a blank canvas. Christopher S. Penn: You could commission a project with up to a hundred agents. How do you, as a new manager, avoid what I call Avid syndrome? Christopher S. Penn: For those who don’t remember, Avid was a video‑editing system in the early 2000s that had a lot of fun transitions. Christopher S. Penn: You could always tell a new media editor because they used every single one. Katie Robbert: Star, wipe and star. Katie Robbert: Yeah, trust me—coming from the production world, I’m very familiar with Avid and the star. Christopher S. Penn: Exactly. Christopher S. Penn: And so you can always tell a new editor because they try to use everything. Christopher S. Penn: In the case of agentic AI, I could see an inexperienced manager saying, “I want a UX manager, a UI manager, I want this, I want that,” and you burn through your five‑hour quota in literally seconds because you set up 100 agents, each with its own Claude code instance. Christopher S. Penn: So you have 100 versions of this thing running at the same time. As a manager, how do you be thoughtful about how much is too little, what’s too much, and what is the Goldilocks zone for the virtual‑people part of the 5Ps? Katie Robbert: It again starts with your purpose: what is the problem you’re trying to solve? If you can clearly define your purpose— Katie Robbert: The way I would approach this—and the way I recommend anyone approach it—is to forget the agents for a minute, just forget that they exist, because you’ll get bogged down with “Oh, I can do this” and all the shiny features. Katie Robbert: Forget it. Just put it out of your mind for a second. Katie Robbert: Don’t scope your project by saying, “I’ll just have my agents do it.” Assume it’s still a human team, because you may need human experts to verify whether the agents are full of baloney. Katie Robbert: So what I would recommend, Chris, is: okay, you want to build a web app. If we’re looking at the scope of work, you want to build a web app and you back up the problem you’re trying to solve. Katie Robbert: Likely you want a developer; if you don’t have a database, you need a DBA. You probably want a QA tester. Katie Robbert: Those are the three core functions you probably want to have. What are you going to do with it? Katie Robbert: Is it going to live internally or externally? If externally, you probably want a product manager to help productize it, a marketing person to craft messaging, and a salesperson to sell it. Katie Robbert: So that’s six roles—not a hundred. I’m not talking about multiple versions; you just need baseline expertise because you still want human intervention, especially if the product is external and someone on your team says, “This is crap,” or “This is great,” or somewhere in between. Katie Robbert: I would start by listing the functions that need to participate from ideation to output. Then you can say, “Okay, I need a UX designer.” Do I need a front‑end and a back‑end developer? Then you get into the nitty‑gritty. Katie Robbert: But start with the baseline: what functions do I need? Do those come out of the box? Do I need to build them? Do I know someone who can gut‑check these things? Because then you’re talking about human pay scales and everything. Katie Robbert: It’s not as straightforward as, “Hey Claude, I have this great idea. Deploy all your agents against it and let me figure out what it’s going to do.” Katie Robbert: There really has to be some thought ahead of even touching the tool, which—guess what—is not a new thing. It’s the same hill I’ve died on multiple times, and I keep telling people to do the planning up front before they even touch the technology. Christopher S. Penn: Yep. Christopher S. Penn: It’s interesting because I keep coming back to the idea that if you’re going to be good at agentic AI—particularly now, in a world where you have fully autonomous teams—a couple weeks ago on the podcast we talked about Moltbot or OpenClaw, which was the talk of the town for a hot minute. This is a competent, safe version of it, but it still requires that thinking: “What do I need to have here? What kind of expertise?” Christopher S. Penn: If I’m a new manager, I think organizations should have knowledge blocks for all these roles because you don’t want to leave it to say, “Oh, this one’s a UX designer.” What does that mean? Christopher S. Penn: You should probably have a knowledge box. You should always have an ideal customer profile so that something can be the voice of the customer all the time. Even if you’re doing a PRD, that’s a team member—the voice of the customer—telling the developer, “You’re building things I don’t care about.” Christopher S. Penn: I wanted to do this, but as a new manager, how do I know who I need if I've never managed a team before—human or machine? Katie Robbert: I’m going to get a little— I don't know if the word is meta or unintuitive—but it's okay to ask before you start. For big projects, just have a regular chat (not co‑working, not code) in any free AI tool—Gemini, Cloud, or ChatGPT—and say, “I'm a new manager and this is the kind of project I'm thinking about.” Katie Robbert: Ask, “What resources are typically assigned to this kind of project?” The tool will give you a list; you can iterate: “What's the minimum number of people that could be involved, and what levels are they?” Katie Robbert: Or, the world is your oyster—you could have up to 100 people. Who are they? Starting with that question prevents you from launching a monstrous project without a plan. Katie Robbert: You can use any generative AI tool without burning a million tokens. Just say, “I want to build an app and I have agents who can help me.” Katie Robbert: Who are the typical resources assigned to this project? What do they do? Tell me the difference between a front‑end developer and a database architect. Why do I need both? Christopher S. Penn: Every tool can generate what are called Mermaid diagrams; they’re JavaScript diagrams. So you could ask, “Who's involved?” “What does the org chart look like, and in what order do people act?” Christopher S. Penn: Right, because you might not need the UX person right away. Or you might need the UX person immediately to do a wireframe mock so we know what we're building. Christopher S. Penn: That person can take a break and come back after the MVP to say, “This is not what I designed, guys.” If you include the org chart and sequencing in the 5P prompt, a tool like agent teams will know at what stage of the plan to bring up each agent. Christopher S. Penn: So you don't run all 50 agents at once. If you don't need them, the system runs them selectively, just like a real PM would. Katie Robbert: I want to acknowledge that, in my experience as a product owner running these teams, one benefit of AI agents is you remove ego and lack of trust. Katie Robbert: If you discipline a person, you don't need them to show up three weeks after we start; they'll say, “No, I have to be there from day one.” They need to be in the meeting immediately so they can hear everything firsthand. Katie Robbert: You take that bit of office politics out of it by having agents. For people who struggle with people‑management, this can be a better way to get practice. Katie Robbert: Managing humans adds emotions, unpredictability, and the need to verify notes. Agents don't have those issues. Christopher S. Penn: Right. Katie Robbert: The agent's like, “Okay, great, here's your thing.” Christopher S. Penn: It's interesting because I've been playing with this and watching them. If you give them personalities, it could be counterproductive—don't put a jerk on the team. Christopher S. Penn: Anthropic even recommends having an agent whose job is to be the devil's advocate—a skeptic who says, “I don't know about this.” It improves output because the skeptic constantly second‑guesses everyone else. Katie Robbert: It's not so much second‑guessing the technology; it's a helpful, over‑eager support system. Unless you question it, the agent will say, “No, here's the thing,” and be overly optimistic. That's why you need a skeptic saying, “Are you sure that's the best way?” That's usually my role. Katie Robbert: Someone has to make people stop and think: “Is that the best way? Am I over‑developing this? Am I overthinking the output? Have I considered security risks or copyright infringement? Whatever it is, you need that gut check.” Christopher S. Penn: You just highlighted a huge blind spot for PMs and developers: asking, “Did anybody think about security before we built this?” Being aware of that question is essential for a manager. Christopher S. Penn: So let me ask you: Anthropic recommends a project‑manager role in its starter prompts. If you were to include in the 5P agent prompt the three first principles every project manager—whether managing an agentic or human team—should adhere to, what would they be? Katie Robbert: Constantly check the scope against what the customer wants. Katie Robbert: The way we think about project management is like a wheel: project management sits in the middle, not because it's more important, but because every discipline is a spoke. Without the middle person, everything falls apart. Katie Robbert: The project manager is the connection point. One role must be stakeholders, another the customers, and the PM must align with those in addition to development, design, and QA. It's not just internal functions; it's also who cares about the product. Katie Robbert: The PM must be the hub that ensures roles don't conflict. If development says three days and QA says five, the PM must know both. Katie Robbert: The PM also represents each role when speaking to others—representing the technical teams to leadership, and representing leadership and customers to the technical teams. They must be a good representative of each discipline. Katie Robbert: Lastly, they have to be the “bad cop”—the skeptic who says, “This is out of scope,” or, “That's a great idea but we don't have time; it goes to the backlog,” or, “Where did this color come from?” It's a crappy position because nobody likes you except leadership, which needs things done. Christopher S. Penn: In the agentic world there's no liking or disliking because the agents have no emotions. It's easier to tell the virtual PM, “Your job is to be Mr. No.” Katie Robbert: Exactly. Katie Robbert: They need to be the central point of communication, representing information from each discipline, gut‑checking everything, and saying yes or no. Christopher S. Penn: It aligns because these agents can communicate with each other. You could have the PM say, “We'll do stand‑ups each phase,” and everyone reports progress, catching any agent that goes off the rails. Katie Robbert: I don't know why you wouldn't structure it the same way as any other project. Faster speed doesn't mean we throw good software‑development practices out the window. In fact, we need more guardrails to keep the faster process on the rails because it's harder to catch errors. Christopher S. Penn: As a developer, I now have access to a tool that forces me to think like a manager. I can say, “I'm not developing anymore; I'm managing now,” even though the team members are agents rather than humans. Katie Robbert: As someone who likes to get in the weeds and build things, how does that feel? Do you feel your capabilities are being taken away? I'm often asked that because I'm more of a people manager. Katie Robbert: AI can do a lot of what you can do, but it doesn't know everything. Christopher S. Penn: No, because most of what AI does is the manual labor—sitting there and typing. I'm slow, sloppy, and make a lot of mistakes. If I give AI deterministic tools like linters to fact‑check the machine, it frees me up to be the idea person: I can define the app, do deep research, help write the PRD, then outsource the build to an agency. Christopher S. Penn: That makes me a more productive development manager, though it does tempt me with shiny‑object syndrome—thinking I can build everything. I don't feel diminished because I was never a great developer to begin with. Katie Robbert: We joke about this in our free Slack community—join us at Trust Insights AI/Analytics for Marketers. Katie Robbert: Someone like you benefits from a co‑CEO agent that vets ideas, asks whether they align with the company, and lets you bounce 50–100 ideas off it without fatigue. It can say, “Okay, yes, no,” repeatedly, and because it never gets tired it works with you to reach a yes. Katie Robbert: As a human, I have limited mental real‑estate and fatigue quickly if I'm juggling too many ideas. Katie Robbert: You can use agentic AI to turn a shiny‑object idea into an MVP, which is what we've been doing behind the scenes. Christopher S. Penn: Exactly. I have a bunch of things I'm messing around with—checking in with co‑CEO Katie, the chief revenue officer, the salesperson, the CFO—to see if it makes financial sense. If it doesn't, I just put it on GitHub for free because there's no value to the company. Christopher S. Penn: Co‑CEO reminds me not to do that during work hours. Christopher S. Penn: Other things—maybe it's time to think this through more carefully. Christopher S. Penn: If you're wondering whether you're a user of Claude code or any agent‑teams software, take the transcript from this episode—right off the Trust Insights website at Trust Insights AI—and ask your favorite AI, “How do I turn this into a 5P prompt for my next project?” Christopher S. Penn: You will get better results. Christopher S. Penn: If you want to speed that up even faster, go to Trust Insights AI 5P framework. Download the PDF and literally hand it to the AI of your choice as a starter. Christopher S. Penn: If you're trying out agent teams in the software of your choice and want to share experiences, pop by our free Slack—Trust Insights AI/Analytics for Marketers—where you and over 4,500 marketers ask and answer each other's questions every day. Christopher S. Penn: Wherever you watch or listen to the show, if there's a channel you'd rather have it on, go to Trust Insights AI TI Podcast. You can find us wherever podcasts are served. Christopher S. Penn: Thanks for tuning in. Christopher S. Penn: I'll talk to you on the next one. Katie Robbert: Want to know more about Trust Insights? Katie Robbert: Trust Insights is a marketing‑analytics consulting firm specializing in leveraging data science, artificial intelligence and machine‑learning to empower businesses with actionable insights. Katie Robbert: Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data‑driven approach. Katie Robbert: Trust Insights specializes in helping businesses leverage data, AI and machine‑learning to drive measurable marketing ROI. Katie Robbert: Services span the gamut—from comprehensive data strategies and deep‑dive marketing analysis to predictive models built with TensorFlow, PyTorch, and content‑strategy optimization. Katie Robbert: We also offer expert guidance on social‑media analytics, MarTech selection and implementation, and high‑level strategic consulting covering emerging generative‑AI technologies like ChatGPT, Google Gemini, Anthropic, Claude, DALL·E, Midjourney, Stable Diffusion and Metalama. Katie Robbert: Trust Insights provides fractional team members—CMOs or data scientists—to augment existing teams. Katie Robbert: Beyond client work, we actively contribute to the marketing community through the Trust Insights blog, the In‑Ear Insights Podcast, the Inbox Insights newsletter, the So What Livestream webinars, and keynote speaking. Katie Robbert: What distinguishes us? Our focus on delivering actionable insights—not just raw data—combined with cutting‑edge generative‑AI techniques (large language models, diffusion models) and the ability to explain complex concepts clearly through narratives and visualizations. Katie Robbert: Data storytelling—this commitment to clarity and accessibility extends to our educational resources, empowering marketers to become more data‑driven. Katie Robbert: We champion ethical data practices and AI transparency. Katie Robbert: Sharing knowledge widely—whether you're a Fortune 500 company, a midsize business, or a marketing agency seeking measurable results—Trust Insights offers a unique blend of technical experience, strategic guidance and educational resources to help you navigate the ever‑evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

With excitement, we are announcing the birth of AIXD.World, a subsidiary of 10PlusBrand.com. Learn what AIXD (AI experience design) is all about (hint: it is not anti-AI, but pro-human.) AIXD is not the same as UI and UX. UI (user interface) and UX (user experience) are terms often associated with app design. As Google famously explains: if a digital product were a house, UX is the structure and wiring (how it works), while UI is the paint and furniture (how it looks). AIXD goes further. It is both the architectural blueprint and the interior design—but custom-built around real human needs. AIXD precedes UI and UX by grounding AI development in what end users actually want, not what technologists assume they want. By anchoring AI to human psychology, emotions, and lived experience, AIXD helps organizations avoid waste, reduce friction, and design AI that truly serves people. In an era of AI over-enthusiasm, rushed adoption, and “white elephant” AI projects, AIXD fills the critical gap between human end users and AI developers. It reframes success away from hype and toward outcomes that matter: usefulness, satisfaction, dignity, and trust. AIXD asks leaders the most important question before building any AI system: What human experience are we creating—and for whom? AIXD is not anti-AI. It is pro-human. AIXD is user experience. User experience is brand experience. What is AIXD (AI Experience Design)? How is it Related to User Centered Design and Brand Experience? AIXD (“AI Experience Design”) is the design of AI-assisted, AI-enabled, and AI-led user journeys—created explicitly for the convenience, satisfaction, and wellbeing of human end users. It is the human-centered design of AI models, applications, workflows, products, and services. Ultimately, AIXD is the be-all and end-all of human user experience.

Google will release the first beta of Android 17, bypassing Developer Preview builds and moving directly to public beta testing. The beta is expected around February 18, coinciding with the Pixel 10a pre-order date. The stable version is anticipated by June 2026. Android 17 may introduce UI blur, separate the notification shade from Quick Settings, and add app support on the always-on display. Pixel users should avoid downgrading to Android 16's previous stable build to prevent data wipes and can exit the Android Beta Program in June 2026.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Agents will soon retrieve data from MCP servers and offer formatting options you can interact with. The Copilot "preview pane" opens Word, Excel, and PowerPoint alongside your M365 Chat results. Viva Engage introduces a way to hide your colleagues messages from your feed. What else landed this week? 0:00 Welcome 1:55 Open Word, Excel, and PowerPoint Files in Microsoft 365 Copilot Chat - MC1225199 4:03 Microsoft Teams: Teams Live Events is retiring - MC1226495 8:17 Enhancing Model Context Protocol (MCP) based agents with rich interactive UI widgets support - MC1227627 14:00 Viva Engage: New option to hide a user's messages - MC1226225 21:11 Drawn electronic signatures with eSignature for Microsoft 365 - MC1225195 24:09 Change meeting organizer via PowerShell cmdlet in Exchange Online - MC1227623

OPEN HEAVENSMATALA LE LAGI MO LE ASO LULU 11 FEPUARI 2026(tusia e Pastor EA Adeboye) Manatu Autu: Fua mai ni fua mo Keriso 1 (Bear fruits for Christ 1)Tauloto Tusi Paia: Mareko 16:15 “Ua fetalai atu fo‘i o ia ‘iā te i latou, “Ō atu ia ‘outou i le lalolagi uma, ‘inā tala‘i atu ai le tala lelei i tagata uma lava.”Faitauga - Tusi Paia: Ioane 15:14-16I le faitauga mai le Tusi Paia o le asō, na fetalai Iesu na ia filifilia lona au soo e fua mai ni fua ma ia tumau ai. O le upu ‘fua' o lona uiga o agaga. O loo faamanino mai e Iesu e na o i latou e usitai i ana poloaiga e taua o ana uo ma o soo se mea e latou te ole atu ai i le Tamā i lona suafa, e na te foai atu iai latou. O i latou e latalata ia te a'u, e filiga e manumalo agaga mo Keriso aua ou te iloa o le finagalo lea o le Atua. Ou te naunau e manumaloina agaga seia oo ina ou oti. Tusa pe leai ni avanoa, ou te faia avanoa ina ia ou talai atu Iesu i tagata. Na fai mai se tagata ia te au e leai se mea o faamauina ai na faailoga le aso fanau o Iesu, ona ia faaseā mai lea i le faailoga o lo'u aso fanau. Na ou faafetaia o ia ma ou fai iai e faaaoga e le Atua lo'u aso fanau e manumaloina ai agaga mo lona malo. Na filifilia i tatou e Iesu e fua mai ni fua ma tumau i lona malo, ma e tatau ona tatou faaaogaina soo se avanoa e tuliloaina ai. O se faanoanoaga tele ona o le toatele, e lē faatauaina le manumaloina o agaga. Na poloai Iesu iai tatou e o atu i le lalolagi ma talai atu le talalelei i soo se tagata, e pei ona tatou iloa i le tauloto mai le Tusi Paia o le asō. E lei valaau mai iai tatou e o atu i nofoaga e talia ai tatou e tagata pe faigofie ona faia ai le galuega. Na ia valaau mai iai tatou e o atu i le lalolagi atoa, pe lelei pe leaga. A aumai se galuega e le Atua ia tei tatou e o atu e faia, e tatau ona tatou o atu ma faia. I le Galuega 16:6-40, na maua se faaaliga e Paulo na ia vaaia se tamaloa o valaau mai ia te ia e alu atu i Maketonia e fesoasoani iai latou. I luma atu o le taimi lea, sa ia taumafai atu e talai le talalelei i ni nuu i Asia, peitai na faasāina e Agaga Paia. E te ono manatu ona na valaau le Atua ia te ia e alu i Maketonia o le a leai nisi e tetee mai poo ni puapuaga. Peitai, e lei leva ona taunuu Paulo ma Sila i lea nuu, ae fasia i laua ma lafo i le falepuipui. Ui i lea, e lei taofia ai mai le talai atu o le talalelei. O tagata e lei sauni e fetaiai ma mea faigata, e lē mamao le tulaga e oo iai i le usitaia o poloaiga a Iesu, e o atu i le lalolagi uma ma talai atu le talalelei.Le au pele e, e iai taimi, ao e talai atu le talalelei, e teena pe ulagia oe e tagata. Peitai, ia outou loto tetele pe a tupu lea tulaga. Na fetalai Iesu o soo se tagata e sauaina ona o ia e maua e ia le taui e tele (Mataio 5:11-12). Ia e talai pea le talalelei i taimi uma pe talafeagai pe lē talafeagai, ona e susulu pea lea e pei o se fetu e faavavau (Tanielu 12:3). Ia e faamaoni e talai le talalelei  i soo se avanoa e te maua, i le suafa o Iesu, Amene. 

New @greenpillnet pod out today!

Sumbits is back. MBA experts Sean Cawby, Eric Schaitel, and Ryan Cockrem sit down (this time with coffee-instead-of-whiskey energy) and catch up on what's been happening while the microphones were off, then get into what's new in PowerSchool since they last joined us. They talk AI (from skepticism to daily tool), the new UI and navigation, security and SSO, data dictionary changes, page permissions, development workflows - along with a few opinions on what's genuinely helpful versus what's just different. The beards might be a little more gray, but the commits are still green.Sumbits is brought to you by MBA. At MBA, we enhance the power of #PowerSchool with plugins, customizations and professional development, transforming your PowerSchool #SIS experience without creating more administrative overhead. Learn more at MBA-link.com

Lazar Jovanovic is a full-time professional vibe coder at Lovable. His job is to build both internal tools and customer-facing products purely using AI, while not having a coding background. In this conversation, he breaks down the tactics, workflows, and framework that let him ship production-quality products using only AI.We discuss:1. Why having no coding background can be an advantage when building with AI2. Why most of your time should go to planning and chat mode, not prompting3. What to do when you get stuck: his 4x4 debugging workflow4. The PRD and Markdown file system that keeps AI agents aligned across complex builds5. Why kicking off four or five parallel prototypes is the best way to clarify your thinking6. Why design skills and taste are going to be the most important skills in the future7. His “genie and three wishes” mental model for making the most of AI's limitations8. How product, engineering, and design roles are converging—and what that means for your career—Brought to you by:Strella—The AI-powered customer research platform: https://strella.io/lennySamsara—Saving lives with AI built for physical operations: https://samsara.com/lennyWorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs: https://workos.com/lenny—Episode transcript: https://www.lennysnewsletter.com/p/getting-paid-to-vibe-code—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Lazar Jovanovic:• X: https://x.com/lakikentaki• LinkedIn: https://www.linkedin.com/in/lazar-jovanovic• YouTube: https://www.youtube.com/@50in50challenge• Starter Story course: https://build.starterstory.com/build/ai-build-accelerator?via=lazar (code LAZAR15 for 15% off)—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Lazar and professional vibe coding(04:53) What a professional vibe coder actually does day-to-day(09:26) Why non-technical backgrounds can be an advantage(12:24) The importance of self-awareness(14:42) His “genie and three wishes” mental model(17:43) Developing taste and judgment in the age of AI(21:46) The parallel project approach for better outcomes(29:30) Creating dynamic context windows with PRDs(36:56) Why elite vibe coders focus on planning, not coding(44:43) Creating MD files to guide AI development(50:57) Why prototyping still matters(56:50) Why “good enough” is no longer good enough(01:00:53) The future of engineering in an AI world(01:05:14) What to do when you get stuck: his 4x4 debugging workflow(01:14:27) Helping agents learn from their mistakes(01:15:35) Why watching agent output is more important than code(01:19:08) The incredible pace of AI development(01:22:55) Why emotional intelligence will become more valuable(01:28:30) How to become a professional vibe coder(01:30:10) Why building in public is the fastest path to opportunities(01:37:03) Final thoughts on focusing on quality over tech stack—Referenced:• The new AI growth playbook for 2026: How Lovable hit $200M ARR in one year | Elena Verna (Head of Growth): https://www.lennysnewsletter.com/p/the-new-ai-growth-playbook-for-2026-elena-verna• Elena Verna on how B2B growth is changing, product-led growth, product-led sales, why you should go freemium not trial, what features to make free, and much more: https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company• The ultimate guide to product-led sales | Elena Verna: https://www.lennysnewsletter.com/p/the-ultimate-guide-to-product-led• 10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey): https://www.lennysnewsletter.com/p/10-growth-tactics-that-never-work-elena-verna• Lovable: https://lovable.dev• Lovable + Shopify: https://lovable.dev/shopify• Everyone's an engineer now: Inside v0's mission to create a hundred million builders | Guillermo Rauch (founder and CEO of Vercel, creators of v0 and Next.js): https://www.lennysnewsletter.com/p/everyones-an-engineer-now-guillermo-rauch• Mobbin: https://mobbin.com• Dribbble: https://dribbble.com• 21st.dev: https://21st.dev• Lovable base prompt generator: https://chatgpt.com/g/g-67e1da2c9c988191b52b61084438e8ee-lovable-base-prompt• Lovable PRD generator: https://chatgpt.com/g/g-67e1e85fbeac8191a69b95c6d5c42ef6-lovable-prd-generator• Felix Haas's newsletter: https://designplusai.com• Bauhaus: https://en.wikipedia.org/wiki/Bauhaus• Glassmorphism: https://www.figma.com/community/plugin/1197106608665398190/glassmorphism• UI style guide: http://uistyle.lovable.app• Cloudflare: https://www.cloudflare.com• Ben Tossell on X: https://x.com/bentossell• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Peter Thiel says AI will be ‘worse' for math nerds than for writers: https://www.businessinsider.com/peter-thiel-ai-worse-for-math-professionals-than-writers-2024-4• Andrej Karpathy on X: https://x.com/karpathy• The 100-person AI lab that became Anthropic and Google's secret weapon | Edwin Chen (Surge AI): https://www.lennysnewsletter.com/p/surge-ai-edwin-chen• Why experts writing AI evals is creating the fastest-growing companies in history | Brendan Foody (CEO of Mercor): https://www.lennysnewsletter.com/p/experts-writing-ai-evals-brendan-foody• Slumdog Millionaire: https://www.imdb.com/title/tt1010048—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Thank you to our sponsors! Fuse: The Energy Network MultiChain Advisors Vitalik Buterin just dropped a bombshell: the L2 vision no longer makes sense. Meanwhile, AI coding agents are going parabolic. In this monster episode of Uneasy Money, Ethereum Foundation Head of Developer Growth Austin Griffith and Optimism co-founder Karl Floersch join hosts Kain Warwick and Taylor Monahan to unpack the reasoning behind Vitalik's remarks and debate whether Ethereum needs L2s to pull institutions. They also take a deep dive into the OpenClaw and Moltbook craze and Austin shares how he has different agents running on different machines, including one that texts his wife good morning everyday. Is “AI the new UI?” Hosts: Kain Warwick, Founder of Infinex and Synthetix Taylor Monahan, Security Expert, Metamask Guests: Austin Griffith, AI Lead at Ethereum Foundation Karl Floersh, CTO of OP Labs Links: Vitalik Rethinks Ethereum's L2 Playbook, Calls for Shift Toward Native Rollups How the x402 Standard Is Enabling AI Agents to Pay Each Other Learn more about your ad choices. Visit megaphone.fm/adchoices

Ep 277Western governments BUILT the backdoors China walked through. They are called "lawful intercept" systems.Apple's new iPhone and iPad security feature limits cell networks from collecting precise location data | TechCrunchFlorian Roth: Notepad++ hacked. This is bad. Putty level bad.iPhone 5s Gets New Software Update 13 Years After LaunchWindows 11 ima 1 milijardu aktivnih korisnika.Announcing msgvault: lightning fast private email archive and search system, with terminal UI and MCP server, powered by DuckDB – Wes McKinneyMake Finder Window Columns Resize to Fit Filenames - TidBITSApple Propelled to Record Q1 2026 Financials by iPhone and Services - TidBITSSdW (re-)joins Apple.Steve Moser: I'm not sure which is better news: Alan Dye leaving Apple or Sebastiaan joiningBasic Apple Guy: Nature is healing.Renaud Lienhart: Sounds like one of Steve Lemay's first task after Dye's departure is to try to hire back all the designers who were alienated & departed over the past decade. This is great.Shipping at Inference-Speed | Peter SteinbergerClawdbot / Moltbot / OpenClaw — Personal AI AssistantClawdbot Showed Me What the Future of Personal AI Assistants Looks LikeMoltbookI Spent 40 Hours Researching Clawdbot.Clawd disaster incomingAndrej Karpathy: A few random notes from claude coding quite a bit last few weeks.This white hat is providing over-eager AI builders a much-needed wake up call.ClawCon ?!2 nedelje za C compiler koji radi.i've made a tragic discovery using clawdbot. there simply aren't that many tasks in my personal life that are worth automatingDušan Dž.: Tim robota mi programira u Claude Code. OpenClaw mi radi istraživanje tržišta. Robot-usisivač pere pod. A ja? Ja slažem veš. Nisam se nadao ovakvoj budućnosti.Apple WINS AI because INTEL and MICROSOFT got it wrong.Apple Just Made Its Second-Biggest Acquisition Ever After BeatsXcode 26.3 unlocks the power of agentic codingApple introduces new AirTag with expanded range and improved findability10+ Things to Know About the New AirTag 2The chime has changed from the note "F" to the note "G".Oliur / ASUS just beat Apple to it.ROG Strix 5K XG27JCG 5K-GPU Supported Refresh Rate ListApple has landed the rights to turn ‘MISTBORN' into a film franchise & ‘THE STORMLIGHT ARCHIVE' into a TV series.Researcher builds bizarre 128-byte USB drive the size of a dinner plate using ancient pre-semiconductor magnetic core memory technology — data disappears once it is read, requiring special handlinghollywood.computerZahvalniceSnimano 6.2.2026.Uvodna muzika by Vladimir Tošić, stari sajt je ovde.Logotip by Aleksandra Ilić.Artwork epizode by Saša Montiljo, njegov kutak na Devianartu

I sit down with Morgan Linton, Cofounder/CTO of Bold Metrics, to break down the same-day release of Claude Opus 4.6 and GPT-5.3 Codex. We walk through exactly how to set up Opus 4.6 in Claude Code, explore the philosophical split between autonomous agent teams and interactive pair-programming, and then put both models to the test by having each one build a Polymarket competitor from scratch, live and unscripted. By the end, you'll know how to configure each model, when to reach for one over the other, and what happened when we let them race head-to-head. Timestamps 00:00 – Intro 03:26 – Setting Up Opus 4.6 in Claude Code 05:16 – Enabling Agent Teams 08:32 – The Philosophical Divergence between Codex and Opus 11:11 – Core Feature Comparison (Context Window, Benchmarks, Agentic Behavior) 15:27 – Live Demo Setup: Polymarket Build Prompt Design 18:26 – Race Begins 21:02 – Best Model for Vibe Coders 22:12 – Codex Finishes in Under 4 Minutes 26:38 – Opus Agents Still Running, Token Usage Climbing 31:41 – Testing and Reviewing the Codex Build 40:25 – Opus Build Completes, First Look at Results 42:47 – Opus Final Build Reveal 44:22 – Side-by-Side Comparison: Opus Takes This Round 45:40 – Final Takeaways and Recommendations Key Points Opus 4.6 and GPT-5.3 Codex dropped within 18 minutes of each other and represent two fundamentally different engineering philosophies — autonomous agents vs. interactive collaboration. To use Opus 4.6 properly, you must update Claude Code to version 2.1.32+, set the model in settings.json, and explicitly enable the experimental Agent Teams feature. Opus 4.6's standout feature is multi-agent orchestration: you can spin up parallel agents for research, architecture, UX, and testing — all working simultaneously. GPT-5.3 Codex's standout feature is mid-task steering: you can interrupt, redirect, and course-correct the model while it's actively building. In the live head-to-head, Codex finished a Polymarket competitor in under 4 minutes; Opus took significantly longer but produced a more polished UI, richer feature set, and 96 tests vs. Codex's 10. Agent teams multiply token usage substantially — a single Opus build can consume 150,000–250,000 tokens across all agents. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ Morgan Linton X/Twitter: https://x.com/morganlinton Bold Metrics: https://boldmetrics.com Personal Website: https://linton.ai

AI agents aren't “coming” to Ethereum—they're already here, spinning up on dedicated machines, clicking through wallets, deploying contracts, and even building apps for themselves. In this episode, Ryan and David sit down with Davide Crapis and Austin Griffith to map the emerging agent stack: ERC-8004 as a decentralized identity + reputation layer, x402 as payment rails for agent-to-agent commerce, and the real-world “Clawdbot” experiments that show what happens when an agent gets a wallet, a codebase, and a mandate. Along the way: prompt-injection risks, why agents read calldata like it's their native language, and why it may be the best time in history to be a solo builder—even as it gets harder to be a junior dev. ---

In this episode of the Official Xbox Podcast, we're so excited to have the Overwatch team in studio with us! We're talking about game's 10-year anniversary, diving deep into the new heroes, and getting information on the story. We're also looking ahead to what will be a massive year for both the franchise and Blizzard as a whole.00:00 Introduction01:09 Overwatch is having its 10th anniversary this year. How is the Overwatch team there at Blizzard feeling about the last ten years and about this big year to come? 02:47 There's a name change with Overwatch 2 going back to Overwatch? What was the thought behind it? How's this going to work? 03:28 Along with that change, there are some other changes as well when it comes to how you're handling seasons moving forward, right? 03:55 Season 1 is launching on February 10th, and that's starting off something huge for Overwatch, right? 04:17 Talon is seeing some leadership changes at the top. Doomfist is no longer in control, right? 04:57 New heroes coming to Overwatch. This year we're getting 10 new heroes overall, which is more than triple what we get normally in the year. Five of those are dropping during season 1? 06:53 Domina deep dive 09:07 Two new DPS, let's start with Emre. 10:43 Anran deep dive 12:34 Mizuki deep dive 14:54 Jet Pack Cat deep dive 17:51 We know our roles of DPS, Tank, and Support, but you guys are rolling out Sub-Roles and Passives as well? 20:37 Visually, the game is getting a bit of an update with the UI and some engine advancements. 21:43 Plus new Post-Match Accolades? 22:48 What can you share about the new cosmetics, and if you can, which one is your favorite? 23:36 For you two, personally, what's the thing you're most excited for players to get to experience over this upcoming year? 26:15 Final thoughts? 28:00 Do you have favorite heroes or villains? 29:12 Outro FOLLOW XBOXFacebook: https://www.facebook.com/Xbox​​​ Twitter: https://www.twitter.com/Xbox​​​ Instagram: https://www.instagram.com/Xbox

With Ron gone (in Portland for pinball, don't worry), Jason Howell, Florence Ion, and Huyen Tue Dao do their best to hold down the fort. From Android desktop UI leaks to Nvidia's masterful 10 year (and counting) run with the Shield, to a live unboxing of the new Motorola Moto Watch, everything you need for Android bliss is right here.Note: Time codes subject to change depending on dynamic ad insertion by the distributor00:02:33 - NEWSAndroid's full desktop interface leaks: New status bar, Chrome ExtensionsChromeOS will be ‘phased out' in 2034 as Android PCs arrive late, court docs suggestGoogle releases ‘Desktop Camera' app that's seemingly for Android PCsAndroid 16 is off to a strong start in Google's latest usage breakdownGoogle Pixel expected to see ‘strongest growth' in 2026, report saysPatron Pick: Motorola is getting away with zero OS updates thanks to regulatory loophole00:36:11 - HARDWAREMotorola Moto Watch first lookThere won't be a Nothing Phone 4 this yearNothing's next over-ear headphones reportedly cost around $150, launching in MarchNothing Phone 4a series leak reveals launch date, and it's just weeks awayInside Nvidia's 10-year effort to make the Shield TV the most updated Android device ever00:58:12 - APPSGemini in navigation is now available for walking and cycling in Google Maps.Fitbit's co-founders are back with a new app, and you can sign up for the limited betaThis free app turns your Android phone into an iPod Hosted on Acast. See acast.com/privacy for more information.

Anfi and Ioana explore the future of design jobs, questioning whether design roles will remain the same as the tech industry transforms. They discuss how design roles are evolving right now, especially as AI begins to generate UI and we communicate through prompts. In this new landscape, what exactly are we designing? Can we still call ourselves UX designers, or will our roles shift into something new? Anfi and Ioana discuss why designers need to improve our technological skills to stay relevant in this changing field.This episode was recorded in partnership with ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Wix Studio.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Mentioned in this episode:"Dogma, tribe and truth" from the Making Sense with Sam Harris podcast"The Adolescence of Technology. Confronting and Overcoming the Risks of Powerful AI" - article by Dario Amodei Check out these links:Preorder Ioana's upcoming book ⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠. The first 100 copies will be hand-signed.Ioana's co-working spaceJoin Anfi's ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Job Search community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠The community includes 3 courses, 12 live events and workshops, and a variety of templates to support you in your job search journey.Ioana's AI project: ⁠⁠⁠⁠⁠⁠aidesign-os.com⁠⁠⁠⁠⁠⁠Ioana's ⁠⁠⁠⁠⁠⁠WhatsApp group⁠⁠⁠⁠⁠⁠Ioana's ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AI Goodies Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ioana's Domestika course ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Create a Learning Strategy⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Enroll in Ioana's AI course ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠"**AI-Powered UX Design: How to Elevate Your UX Career"**⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ on Interaction Design Foundation with a 25% discount.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Into UX design⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ online course by Anfisa⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠❓Next topic ideas:Submit your questions or feedback anonymously ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Follow us on Instagram to stay tuned for the next episodes.

Scott and Wes run through their wishlist for the web platform, digging into the UI primitives, DOM APIs, and browser features they wish existed (or didn't suck). From better form controls and drag-and-drop to native reactivity, CSS ideas, and future-facing APIs, it's a big-picture chat on what the web could be. Show Notes 00:00 Welcome to Syntax! Wes Tweet 00:39 Exploring What's Missing from the Web Platform 02:26 Enhancing DOM Primitives for Better User Experience 03:59 Multi-select + Combobox. Open-UI 04:49 Date Picker. Thibault Denis Tweet 07:18 Tabs. 08:01 Image + File Upload. 09:08 Toggles. 10:23 Native Drag and Drop that doesn't suck. 12:03 Syntax wishlist. 12:06 Type Annotations. 15:07 Pipe Operator. 16:33 APIs We Wish to See on the Web 18:31 Brought to you by Sentry.io 19:51 Identity. 21:33 getElementByText() 24:09 Native Reactive DOM. Templating in JavaScript. 24:48 Sync Protocol. 25:52 Virtualization that doesn't suck. 27:40 Put, Patch, and Delete on forms. Ollie Williams Tweet SnorklTV Tweet 28:55 Text metrics: get bounding box of individual characters. 29:42 Lower Level Connections. 29:50 Bluetooth API. 30:47 Sockets. 31:29 NFC + RFID. 34:34 Things we want in CSS. 34:40 Specify transition speed. 35:24 CSS Strict Mode. 36:25 Safari moving to Chromium. 36:37 The Need for Diverse Browser Engines 37:48 AI Access. 44:49 Other APIs 46:59 Qwen TTS 48:07 Sick Picks + Shameless Plugs Sick Picks Scott: Monarch Wes: Slonik Headlamp Shameless Plugs Scott: Syntax on YouTube Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

On our 500th episode James and Frank celebrate the milestone, reminisce about their mobile‑dev roots, and dig into how AI, the Copilot CLI/SDK and the Model Context Protocol (MCP) are reshaping workflows. Frank demos an MCP‑powered tool that turns app reviews into prioritized GitHub issues and automations — a real example of AI-as-glue — with practical takeaways on prompt engineering, UI extensions, and when to automate versus curate manually. Follow Us Frank: Twitter, Blog, GitHub James: Twitter, Blog, GitHub Merge Conflict: Twitter, Facebook, Website, Chat on Discord Music : Amethyst Seer - Citrine by Adventureface ⭐⭐ Review Us ⭐⭐ Machine transcription available on http://mergeconflict.fm

Thu, 29 Jan 2026 20:30:00 GMT http://relay.fm/connected/588 http://relay.fm/connected/588 DO NOT Put an AirTag on a Lobster 588 Federico Viticci, Stephen Hackett, and Myke Hurley Clawdbot has a new (worse) name, AirTags got an update, Camo served Apple with a lawsuit, and Sebastiaan de With has a new job. This week, the guys talk about all of these stories and more. Clawdbot has a new (worse) name, AirTags got an update, Camo served Apple with a lawsuit, and Sebastiaan de With has a new job. This week, the guys talk about all of these stories and more. clean 3886 Clawdbot has a new (worse) name, AirTags got an update, Camo served Apple with a lawsuit, and Sebastiaan de With has a new job. This week, the guys talk about all of these stories and more. This episode of Connected is sponsored by: Surfshark: Use this link or use code CONNECTED at checkout to get 4 extra months of Surfshark VPN! Sentry: Mobile crash reporting and app monitoring. New users get $100 in Sentry credits with code connected26. Links and Show Notes: Get Connected Pro: Preshow, postshow, no ads. Submit Feedback Moltbot (Formerly Clawdbot) Showed Me What the Future of Personal AI Assistants Looks Like - MacStories Moltbot — Personal AI Assistant Apple Introduces New and Improved AirTag - MacStories Introducing Moltworker: a self-hosted personal AI agent, minus the minis Apple Introduces New and Improved AirTag - MacStories An app developer is suing Apple for Sherlocking it with Continuity Camera | The Verge Camo and Apple – Aidan Fitzpatrick Cupertino restarts photocopiers, but indie devs stay optimistic - Ars Technica Halide co-founder Sebastiaan de With is joining Apple's design team | The Verge Sebastiaan's Threads Post Inside Looks: A Mark III Preview – Lux Physicality: the new age of UISDW's article about "a big impending UI redesign" before Liquid Glass was announced. iPhone 17 Pro Camera Review: Rule of Three - SDW Welcome to the New, Unified MacStories and Club MacStories - MacStories The New Club MacStories: Re-Subscribing to Your RSS

Theresa Duringer is the owner and CEO of Temple Gates Games, a San Francisco–based digital board game studio known for best-in-class adaptations of modern tabletop games. Her team has brought Ascension to VR and developed acclaimed digital versions of Dominion, Race for the Galaxy, Shards of Infinity, and more, with a relentless focus on speed, clarity, and intuitive UI. Theresa works closely with designers and publishers to translate complex tabletop systems into digital experiences that feel natural, responsive, and faithful to the original games, helping players around the world connect and play together online. In this episode, she shares insights on what makes a great digital adaptation, why performance and UX are inseparable from game design, and how to bridge the gap between physical and digital play without losing what makes tabletop special. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit justingarydesign.substack.com/subscribe

Scott and Wes sit down with Kent C. Dodds to break down MCP, context engineering, and what it really takes to build effective AI-powered tools. They dig into practical examples, UI patterns, performance tradeoffs, and whether the future of the web lives in chat or the browser. Show Notes 00:00 Welcome to Syntax! 00:44 Introduction to Kent C. Dodds 02:44 What is MCP? 03:28 Context Engineering in AI 04:49 Practical Examples of MCP 06:33 Challenges with Context Bloat 08:08 Brought to you by Sentry.io 09:37 Why not give AI API access directly? 12:28 How is an MCP different from Skills 14:58 MCP optimizations and efficiency levers 16:24 MCP UI and Its Importance 19:18 Where are we at today with MCP 24:06 What is the development flow for building MCP servers? 27:17 Building out an MCP UI. 29:29 Returning HTML, when to render. 36:17 Calling tools from your UI 37:25 What is Goose? 38:42 Are browsers cooked? Is everything via chat? 43:25 Remix3 47:21 Sick Picks & Shameless Plugs Sick Picks Kent: OneWheel Shameless Plugs Kent: http://EpicAI.pro,http://EpicWeb.dev,http://EpicReact.dev Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Nilay owns a Sony TV. He loves his Sony TV, and he's a little sad that it appears this era of Sony TVs is ending. He and David talk through the news of a new joint venture between Sony and TCL, before digging into OpenAI's new-fangled plan to make money (spoiler alert: it's ads!), and some new news about an AI gadget Apple may or may not be working on. Then it's time for the lightning round: Brendan Carr, Netflix, the Trump Phone, and much more. Further reading: The TikTok deal could finally close this week. Epic and Google have a secret $800 million Unreal Engine and services deal Sony's TV business is being taken over by TCL  What a Sony and TCL partnership means for the future of TVs OpenAI's 2026 ‘focus' is ‘practical adoption'  OpenAI releases a cheaper ChatGPT subscription  Ads are coming soon to ChatGPT, starting with shopping links  Opinion | A.I. Is Real. But OpenAI Might Still Fail.Apple is reportedly working on an AirTag-sized AI wearable  Apple is turning Siri into an AI bot that's more like ChatGPT  FCC Targets Colbert and Kimmel in New Crackdown on Late-Night TV - The New York Times Bureau Provides Guidance on Political Equal Opportunities Requirement | Federal Communications Commission Free TV startup Telly only had 35,000 units in people's homes last fall Microsoft wants to build 15 data centers in Mount Pleasant, Wisconsin  OpenAI says its data centers will pay for their own energy and limit water usage Netflix will revamp its mobile UI this year  600,000 Trump Mobile phones sold? There's no proof. YouTubers will be able to make Shorts with their own AI likenesses  Subscribe to The Verge for unlimited access to theverge.com, subscriber-exclusive newsletters, and our ad-free podcast feed.We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. Learn more about your ad choices. Visit podcastchoices.com/adchoices