Podcasts about UX

Pankaj Kumar: Product Owners Who Prepare the Ground for Agile Teams The Great Product Owner: Proactive Preparation Before the Team Asks Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.   "Before the team asks, you know that this is the prerequisite for this user story." - Pankaj Kumar   Pankaj's example of a great Product Owner is someone who did the homework before meeting the team. This PO understood what the team needed to deliver, prepared the acceptance criteria, talked to UX early, and made sure design inputs were available before developers were blocked. Vasco describes this as "preparing the ground" for the team. The PO was not just pushing features into a sprint. He was listening to what the team could achieve, translating stakeholder needs into clearer work, and reducing waiting time so the team could focus on execution. For Scrum Masters, this is a useful pattern to notice: great Product Owners protect the team's flow by handling communication and dependency work early.   Self-reflection Question: What does your Product Owner prepare before the team discovers it needs help? The Bad Product Owner: Pushing Urgent Work Into an Already Full Sprint Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.   "The team was already having everything on the plate, and they can't take any more." - Pankaj Kumar   The anti-pattern Pankaj highlights is a Product Owner who treats urgency as permission to ignore capacity. In one sprint, a PO arrived with an important customer case and insisted that it had to enter the current sprint backlog. The team was already overloaded, but the PO was not ready to hear the tradeoff. Pankaj's advice is to slow the conversation enough to make the real decision visible. Why is this urgent? Who is the stakeholder? What is the risk? What is the impact? Can it wait until the next sprint? If it cannot wait, what will be adjusted? Agile welcomes change, but change still has a cost. A Product Owner who says yes without making the tradeoff visible risks damaging trust with the team.   In this segment, we refer to root cause analysis, capacity planning, and Product Owner anti-patterns.   Self-reflection Question: When urgent work enters the sprint, what does your team explicitly remove or renegotiate?   [The Scrum Master Toolbox Podcast Recommends]

Why does Microsoft keep stumbling with new devices and software design... and what happens when enthusiasts start favoring AI over old-school Office? Paul, Richard, and Leo dig into shifting loyalties and what that means for the future of the desktop. And with the release of 26H2, it's time to update your install and recovery media. The build number is 26300.9457 Windows Windows 11, version 26H2 is here New features, none unique: Start, Taskbar, Search, etc New defaults for some features: Windows settings backup for commercial customers, etc. The time a UX legend interviewed for a job at Microsoft running Windows UX WSL Containers are generally available in Windows 11 Four new Insider builds, but only Experimental gets anything interesting: Phone companion improvements and Energy recommendations directly in Quick Settings Software Microsoft retires the Microsoft 365 Companion apps for Windows 11 that never made sense in the first place Mozilla ships the new Nova UI in Firefox 157 Hardware Copilot+ PC brand could be on the way out, but it was all over IFA, including on RTX Spark-based PCs Microsoft launches new base Surface Pro and Laptop models with Snapdragon X2 - And a new mouse, for some reason Qualcomm brought (first-gen) Snapdragon X to Googlebook, but it's also bringing X2 to Linux Google quietly reveals that, yes, Chromebooks are on the way out AI Microsoft unveils the Copilot "super app" and the notion that this is basically an OS Open releases about 1000 new updates for ChatGPT Claude Sonnet 5.5 joins Opus 5.5 The AI money pit, part 127: Report details Anthropic losses, spending, and revenues. Report also does a bad job at visualizing that information XBOX and gaming Disc to digital program is live Minecraft Dungeons is here with a new dimension, and it's coming to Minecraft next year too Microsoft has a new casual mobile game for some reason XBOX now has mythic achievements Tips and picks Tip of the week: Get the Windows 11 version 26H2 ISO App pick of the week: Opera on desktop and on mobile RunAs Radio this week: AI and Penetration Testing with Paula Januszkiewicz Brown liquor pick of the week: Kilchoman Loch Gorm Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: PaloAltoNetworks.com/Idira bitwarden.com/twit

Why does Microsoft keep stumbling with new devices and software design... and what happens when enthusiasts start favoring AI over old-school Office? Paul, Richard, and Leo dig into shifting loyalties and what that means for the future of the desktop. And with the release of 26H2, it's time to update your install and recovery media. The build number is 26300.9457 Windows Windows 11, version 26H2 is here New features, none unique: Start, Taskbar, Search, etc New defaults for some features: Windows settings backup for commercial customers, etc. The time a UX legend interviewed for a job at Microsoft running Windows UX WSL Containers are generally available in Windows 11 Four new Insider builds, but only Experimental gets anything interesting: Phone companion improvements and Energy recommendations directly in Quick Settings Software Microsoft retires the Microsoft 365 Companion apps for Windows 11 that never made sense in the first place Mozilla ships the new Nova UI in Firefox 157 Hardware Copilot+ PC brand could be on the way out, but it was all over IFA, including on RTX Spark-based PCs Microsoft launches new base Surface Pro and Laptop models with Snapdragon X2 - And a new mouse, for some reason Qualcomm brought (first-gen) Snapdragon X to Googlebook, but it's also bringing X2 to Linux Google quietly reveals that, yes, Chromebooks are on the way out AI Microsoft unveils the Copilot "super app" and the notion that this is basically an OS Open releases about 1000 new updates for ChatGPT Claude Sonnet 5.5 joins Opus 5.5 The AI money pit, part 127: Report details Anthropic losses, spending, and revenues. Report also does a bad job at visualizing that information XBOX and gaming Disc to digital program is live Minecraft Dungeons is here with a new dimension, and it's coming to Minecraft next year too Microsoft has a new casual mobile game for some reason XBOX now has mythic achievements Tips and picks Tip of the week: Get the Windows 11 version 26H2 ISO App pick of the week: Opera on desktop and on mobile RunAs Radio this week: AI and Penetration Testing with Paula Januszkiewicz Brown liquor pick of the week: Kilchoman Loch Gorm Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: PaloAltoNetworks.com/Idira bitwarden.com/twit

Why does Microsoft keep stumbling with new devices and software design... and what happens when enthusiasts start favoring AI over old-school Office? Paul, Richard, and Leo dig into shifting loyalties and what that means for the future of the desktop. And with the release of 26H2, it's time to update your install and recovery media. The build number is 26300.9457 Windows Windows 11, version 26H2 is here New features, none unique: Start, Taskbar, Search, etc New defaults for some features: Windows settings backup for commercial customers, etc. The time a UX legend interviewed for a job at Microsoft running Windows UX WSL Containers are generally available in Windows 11 Four new Insider builds, but only Experimental gets anything interesting: Phone companion improvements and Energy recommendations directly in Quick Settings Software Microsoft retires the Microsoft 365 Companion apps for Windows 11 that never made sense in the first place Mozilla ships the new Nova UI in Firefox 157 Hardware Copilot+ PC brand could be on the way out, but it was all over IFA, including on RTX Spark-based PCs Microsoft launches new base Surface Pro and Laptop models with Snapdragon X2 - And a new mouse, for some reason Qualcomm brought (first-gen) Snapdragon X to Googlebook, but it's also bringing X2 to Linux Google quietly reveals that, yes, Chromebooks are on the way out AI Microsoft unveils the Copilot "super app" and the notion that this is basically an OS Open releases about 1000 new updates for ChatGPT Claude Sonnet 5.5 joins Opus 5.5 The AI money pit, part 127: Report details Anthropic losses, spending, and revenues. Report also does a bad job at visualizing that information XBOX and gaming Disc to digital program is live Minecraft Dungeons is here with a new dimension, and it's coming to Minecraft next year too Microsoft has a new casual mobile game for some reason XBOX now has mythic achievements Tips and picks Tip of the week: Get the Windows 11 version 26H2 ISO App pick of the week: Opera on desktop and on mobile RunAs Radio this week: AI and Penetration Testing with Paula Januszkiewicz Brown liquor pick of the week: Kilchoman Loch Gorm Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: PaloAltoNetworks.com/Idira bitwarden.com/twit

Kate and Kristin speak with Frances Abrantes Baca about her wide ranging design experience in the food media space. The founding design director of the award winning magazine Gastronimica, Frances shares her thoughts about the design elements that go into a cookbook—color, contrast, titles, page numbers, typeface, layout, covers, spines and, don't forget the table of contents. She discusses what catches her eye, what the differences in specific design roles are and where she finds inspiration before revealing what design choices draw her in and which drive her nuts. She talks about her newsletter, her process and leaves us with a few ideas and tips for authors and aspiring designers both.Hosts: Kate Leahy + Molly Stevens + Kristin Donnelly + Andrea NguyenEditor: Abby Cerquitella MentionsFrances Abrantes BacaNewsletterInstagram Episode 18: Pro Tips from Cookbook Critic Paula ForbesEpisode 11: Understanding Cookbook Design with Alice ChauEpisode 91: The UX of Cookbook Design The Elements of Typographic Style by Robert BringhurstBlue Star PressStained Page NewsWhat Makes a Cookbook Cover Successful?Don't Sleep on the TOCBay Area Women in Publishing Mentorship Subscribe to our weekly Substack newsletter for more information about each episode and a peek at what's coming up.Visit the Everything Cookbooks Bookshop to purchase a copy of the books mentioned in the showWine Simple by Aldo Sohm and Christine MuhlkeTasting Rome by Katie Parla and Kristina GillCookbooking: a Fan's Guide by Debbie BerneThe Design of Books by Debbie Berne

Why does Microsoft keep stumbling with new devices and software design... and what happens when enthusiasts start favoring AI over old-school Office? Paul, Richard, and Leo dig into shifting loyalties and what that means for the future of the desktop. And with the release of 26H2, it's time to update your install and recovery media. The build number is 26300.9457 Windows Windows 11, version 26H2 is here New features, none unique: Start, Taskbar, Search, etc New defaults for some features: Windows settings backup for commercial customers, etc. The time a UX legend interviewed for a job at Microsoft running Windows UX WSL Containers are generally available in Windows 11 Four new Insider builds, but only Experimental gets anything interesting: Phone companion improvements and Energy recommendations directly in Quick Settings Software Microsoft retires the Microsoft 365 Companion apps for Windows 11 that never made sense in the first place Mozilla ships the new Nova UI in Firefox 157 Hardware Copilot+ PC brand could be on the way out, but it was all over IFA, including on RTX Spark-based PCs Microsoft launches new base Surface Pro and Laptop models with Snapdragon X2 - And a new mouse, for some reason Qualcomm brought (first-gen) Snapdragon X to Googlebook, but it's also bringing X2 to Linux Google quietly reveals that, yes, Chromebooks are on the way out AI Microsoft unveils the Copilot "super app" and the notion that this is basically an OS Open releases about 1000 new updates for ChatGPT Claude Sonnet 5.5 joins Opus 5.5 The AI money pit, part 127: Report details Anthropic losses, spending, and revenues. Report also does a bad job at visualizing that information XBOX and gaming Disc to digital program is live Minecraft Dungeons is here with a new dimension, and it's coming to Minecraft next year too Microsoft has a new casual mobile game for some reason XBOX now has mythic achievements Tips and picks Tip of the week: Get the Windows 11 version 26H2 ISO App pick of the week: Opera on desktop and on mobile RunAs Radio this week: AI and Penetration Testing with Paula Januszkiewicz Brown liquor pick of the week: Kilchoman Loch Gorm Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Download or subscribe to Windows Weekly at https://twit.tv/shows/windows-weekly Check out Paul's blog at thurrott.com The Windows Weekly theme music is courtesy of Carl Franklin. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: PaloAltoNetworks.com/Idira bitwarden.com/twit

Get 25% off workshops and courses at UX Content Collective this week, until Monday October 5! AND get 50% off if you add a second enrollment.  Content design is changing quickly - and so is the bar for getting hired. Jody Allard is the head of content design at Roblox, with a long and experienced history at companies like Microsoft and Meta. She's been writing articles on the future of the industry, and authored a new book "Writing Was Never the Job" about where content design is going. She's also spent the past year hiring content designers. What impressed a hiring manager eight months ago, she says, may barely register today. In this episode, Jody joins me to talk about what actually separates strong candidates now, why simply "using AI" is no longer enough, and why product thinking, technical confidence, and a willingness to build are becoming essential parts of the job. We also discuss the changing value of big-tech experience, the two paths emerging within content design, and why people entering the field need a much clearer picture of what the job now requires. What we talked about: ✅ What hiring managers are actually looking for from content designers right now ✅ Why simply using AI has gone from a differentiator to table stakes ✅ What separates people who use AI from people who actually identify problems and build solutions ✅ Why product thinking and proactivity are becoming essential, not optional, content design skills ✅ How content design is splitting between content engineering and traditional UX work, and what both paths now require ✅ Why experience at large companies no longer automatically carries the weight it once did ✅ What content designers can start doing today to stay competitive as the role continues to change Where to find Jody: Website "Writing Was Never the Job" on Amazon LinkedIn Get 25% off workshops and courses at UX Content Collective this week, until Monday October 5! AND get 50% off if you add a second enrollment.

On this episode of the WGU Alumni Podcast, we sit down with two-time WGU graduate Laura Wing, an author, business coach, workplace communication expert, and entrepreneur who decided to return to school at age 59. Laura shares what prompted that decision, why WGU was the right fit, and how she went on to earn both a bachelor's degree in UX Design and a master's degree in Management and Leadership by age 60.As we kick off Season 3 and our theme of Momentum, Laura shares what happened after earning those degrees and how she immediately began putting what she learned into practice. From applying UX principles to her businesses to gaining new confidence and leadership skills, Laura reflects on how education created new possibilities at an already accomplished stage of her career. She also shares lessons from writing 11 books, why she believes a book can be a powerful career and networking tool, and her advice for anyone ready to turn an idea into something real. It's a conversation about lifelong learning, betting on yourself, and creating momentum at any stage of life.To learn more about the WGU Alumni experience, including events, benefits, and ways to stay connected, visit wgu.edu/alumni.

This week on Science Faction, we tackle the important questions: Is bread actually good? Can a Disney Channel movie sneak science fiction past Steven? Can Star Trek: Strange New Worlds finally make Ben stop complaining? And, most importantly, how did a movie about sheep detectives turn out to be legitimately great? Then, in Future or Now, things get a little more serious as Devon brings us a massive Kelvin wave moving toward California, while Ben explores a fake version of Windows 11 that leads us into a much bigger question: why are we still using computers basically the same way we have for decades? Real Life We begin, naturally, with bread. Ben is against it. Devon, who apparently becomes increasingly French whenever bread is involved, is very much in favor of it. Steven loves sourdough and becomes increasingly concerned about what exactly Ben considers the staple crops of his diet. Devon settles the matter with perhaps the strongest argument anyone can make: fresh French bread is amazing, and he will eat all of it. From there, Steven has two movies to discuss, neither of which would normally be considered science fiction. Or would they? First up is Big City Greens: Spacecation, which gets a thumbs-up from Steven. It's a Big City Greens adventure that sends the family into space, which certainly sounds suspiciously like science fiction. There's also a familiar name in the writing credits: Mike Trapp. Then there's The Love Hypothesis. If you've listened to Steven talk about romantic comedies before, you probably already know where this conversation is headed. If you don't, well…he's going to tell you. Meanwhile, Ben has reached the end of another season of Star Trek: Strange New Worlds, which raises an important question: is there a limit to how long Ben can complain about Star Trek? Season four is over, and somehow the show may have accidentally made a good Star Trek episode. That sends us into a bigger discussion about what some fans want from the franchise: an aspirational future where science, discovery, truth, and people working together matter more than enormous space armadas and the latest galaxy-ending artificial intelligence. Could this finally make Ben stop complaining about Star Trek? Maybe for five minutes. But the biggest surprise of Ben's week was The Sheep Detectives. And apparently it was EPIC. Ben calls it a genuinely smart movie and, perhaps most impressively, says it succeeds where Cars 2 failed. Beneath the absurd premise of sheep solving a mystery, there's a story dealing with grief and trauma that worked far better than Ben expected. Ben recommends it. Strongly. Future or Now Devon brings us something considerably larger than sheep detectives: a slow-moving, planetary-scale Kelvin wave heading toward California. The Guardian: Concern mounts over planetary-scale Kelvin wave heading for California The enormous mass of warm water is moving across the Pacific and could affect sea levels along the California coast, with changes potentially approaching a foot in some areas. That leads us into a conversation about what these enormous ocean systems actually mean when they finally reach land—and, for Devon, a much more personal connection involving his son and the devastating flooding in Texas. Then Ben takes us somewhere considerably sillier. Definitely Not Windows is exactly what it sounds like: a parody of Windows 11 that gives us an excuse to complain about modern operating systems. But the joke leads into a much more interesting question. Why do computers still work like this? Windows, icons, folders, desktops, applications—we've been iterating on essentially the same basic ideas for decades. Ben brings in Scott Jenson's exploration of what could come next and asks whether we're really going to use the same desktop UX forever. Scott Jenson: Are we really going to use the same Desktop UX forever? If artificial intelligence changes how we interact with computers, maybe the future isn't simply an AI assistant sitting inside the same old operating system. Maybe the entire idea of what an operating system looks like—and how we tell a computer what we want—needs to change. So this week we've got bread, romantic comedies, surprisingly good Star Trek, emotionally complicated sheep, planetary waves, fake Windows, and the possible death of the desktop. You know. Science Faction.

Agile does not have to undermine UX—but low maturity, rushed delivery, and “Agile in name only” often do. This week, Dr. Darren explores how UX maturity impacts our ability to excel in Agile and how UX professionals can thrive in such environments without compromising research, strategy, quality, or meaningful outcomes.#ux#podcasts#cxofmradio#cxofm#realuxtalk#worldofuxDon't forget to like, subscribe, and share!Bookmark the new World of UX website at https://www.theworldofux.com. Visit the UX Uncensored blog at https://uxuncensored.medium.com. Get your specialized UX merchandise at https://www.kaizentees.com.

Episode web page: https://bit.ly/4hjhw0g Episode summary In this special episode of Insights Unlocked, host Nathan Isaacs sits down with CEO Eric Johnson and Chief Marketing Officer Johann Wrede to introduce Auros, the new parent brand for UserTesting, and explore why the company is expanding its focus as AI reshapes how products and experiences are built. Eric and Johann explain why the UserTesting name no longer captured the full scope of the company's ambitions. While UserTesting and User Interviews will continue serving the research, UX, and insights communities, Auros reflects a broader mission: bringing human intelligence into the development, training, testing, and evaluation of AI. They also share the story behind the Auros name and why the company believes its network of more than 7.5 million people can play a larger role in an AI-enabled world. You'll learn: Why UserTesting is becoming part of a new parent brand, Auros What the Auros name represents and why the company chose a broader identity Why UserTesting and User Interviews will remain important parts of the business How AI is shifting the experience question from usability to trust Why human judgment and expertise matter when training and evaluating AI How a global network of more than 7.5 million people could support AI development What the evolution means for UX researchers, designers, product teams, and other enterprise leaders Why Eric and Johann see human intelligence as an essential part of building safer, more trustworthy AI Resources & links:  Auros Eric Johnson on LinkedIn (https://www.linkedin.com/in/ebjohnson1/) Johann Wrede on LinkedIn (https://www.linkedin.com/in/johannwrede/) Nathan Isaacs on LinkedIn (https://www.linkedin.com/in/nathanisaacs/) Learn more about Insights Unlocked (https://www.usertesting.com/resources/podcast)

By 2035, banking may no longer be something customers actively use. It may increasingly operate around them — intelligently, predictively and autonomously.This episode explores how artificial intelligence could fundamentally reshape financial UX over the next decade. As banking moves beyond screen-based interfaces toward autonomous financial systems, the role of design will shift from creating interactions to governing trust, permissions, transparency and control.We dive into what this means for financial institutions, from adaptive relationships and real-time personalization to AI-driven decision-making that operates on behalf of the customer. The challenge will no longer be simply making banking easier, but ensuring that greater automation still preserves human agency, confidence and meaningful choice.The future of financial UX will not be defined by how much AI can automate — but by how intelligently banks balance automation with human control.Find out: How AI could transform financial UX and digital banking by 2035 Why trust, transparency and permissions will become central design challenges How banks can balance autonomous experiences with human agency and controlRead the full article on UXDA's blog:https://theuxda.com/blog/12-ux-forecasts-next-decade-ai-revolution-digital-banking-2035* AI podcast on UXDA article powered by Google NotebookLM

What's up everyone, today we have the pleasure of sitting down with Christina Garnett, Fractional Chief Customer and Communications Officer at Neuemotion.(00:00) - audio-Christina (01:19) - In This Episode (05:37) - Why Your Tech Stack Cannot Fix Siloed Customer Teams (11:10) - Why Getting the Name Right No Longer Counts as Personalization (18:08) - Why A/B Testing Programs Ignore Qualitative Customer Data (24:14) - How Brands Build Core Memories Customers Cannot Buy (29:55) - How B2B Brands Build Core Memories Without a Budget (35:46) - What to Do When Transactional, Behavioral and Feedback Data Disagree (42:12) - Why Spotify Wrapped Works and What Gen AI Took Away (49:17) - Why Personalization Feels Predatory Even When You Have Consent (53:58) - Where Influence Ends and Manipulation Starts in Marketing (58:57) - Why Audiences Are Going Back to the Humanities in the AI Era Summary: Christina spent 5 years teaching math before she ever wrote a subject line, and it shows in how ruthlessly she takes apart the personalization playbook. Getting someone's first name right used to be impressive and now it just proves you didn't get fired, which leaves marketers chasing hooks and tricks while customers quietly learn to read the pattern. She makes the case for core memories a brand cannot buy, walks through what actually happened when Spotify Wrapped got the AI treatment, and introduces earned context, the idea that having someone's data and having the right to use it are 2 completely different things. Along the way she calls CX and brand the same job, refuses to bet her life on NPS, and explains why the B2B move nobody is making costs almost nothing. If you have ever shipped a perfectly segmented campaign that landed with a thud, this one will tell you why.About Christina GarnettChristina Garnett is a fractional Chief Customer and Communications Officer at Neuemotion and the founder and principal at Pocket CCO, where she builds customer trust systems for agencies and SaaS companies. She's the author of Transforming Customer-Brand Relationships, which won the Independent Press Award and the International Impact Book Award and was a Foreword INDIES finalist, and she's currently writing a second book built on the Customer Trust Equation she created.Before going independent she built customer advocacy at HubSpot, growing the HubFans program from 0 to 37,000 members and more than 4,000 active advocates, and she led social and customer strategy at global scale through ICUC. She's a contributing writer at Campaign US, was previously a byline at Adweek, and she started her career as a teacher.Why Your Tech Stack Cannot Fix Siloed Customer TeamsEvery few years a software category shows up promising to end the silo problem. The CRM was going to do it. Then the customer data platform. Then the customer 360, the composable stack, and whatever the warehouse-native vendors are calling it this quarter. Marketing, support, social, and CX all get wired into the same tables, and everyone assumes shared data will produce shared behavior. Buy the integration, get the alignment.Christina has been on enough of those teams to know how that ends.She came into marketing sideways, an English major who taught math for 5 years, including a stretch at a school built for kids with learning differences where she trained in the Orton-Gillingham method. That method exists to move information from short-term into long-term memory. Marketers have a word for the same thing and call it brand recall. Her husband told her early on that she should go into advertising because she couldn't watch an ad without rebuilding it in her head, tweaking the copy, cutting the music, fixing whatever was broken. She went and took every course, read every book, and kept the teacher's instinct the whole way through: make the hard thing make sense, find the right words for the room you're in.Then she sat at basically every table that claims part of the customer, working as a social listening strategist, a social media creator and a social media strategist, and putting time into support, CX, community and advocacy. What she kept finding was the same organizational failure repeating under different logos, and no amount of shared tooling moved it.Here's the argument that runs under her whole book. Nobody is solving customer experience alone, because nobody is the only person reaching the customer. Support talks to them, so support gets blamed for the relationship. But your UX team is shaping what the customer can and can't do, whether they think of that as CX work or not. Your legal team is writing the fine print the customer reads at the worst possible moment. Finance sets the refund window. All of it builds the relationship, and most of it never appears on anyone's CX dashboard.The result is a company that's genuinely excellent in one place and quietly hostile in another, and it shows.That's why the material became a book rather than a framework or a course. A framework asks you to already agree with its premise before you'll use it. Christina wanted the thing she'd been hunting for on shelves and never found, written for the people at all of those different tables at once. Her words on it: "I think we all wind up writing the books that we wish already existed." The structure follows the problem in 3 moves:Every moving piece that actually builds a brand relationship, most of which sits outside the CX team., Community as the silo killer, the thing that finally forces those teams into the same room., What happens when something lands that you had no way of seeing. A brand crisis, an acquisition by a company your customers hate, or AI arriving and throwing the whole operating model in the air.That third one is where most companies are living right now, and it's the least rehearsed. The martech buying cycle keeps funding integration projects because integration is something you can purchase, and cross-functional accountability is something you have to negotiate. Until one person owns the customer relationship end to end on paper, a new platform just gives 4 teams a shared place to file work they still refuse to coordinate.Key takeaway: List every team that shapes your customer's experience, including the ones that never speak to a customer. Legal writes the cancellation language, UX decides how buried the downgrade button is, finance sets the refund window, and none of them get counted. Put all of them on one page and mark who currently owns the customer's experience of each moment. The blanks are your real CX roadmap, and no platform purchase will fill them in for you.Why Getting the Name Right No Longer Counts as PersonalizationAccuracy in a lifecycle program has never been cheaper. You can segment by behavior, by lifecycle stage, by predicted churn, by what someone hovered over last Tuesday, and the tooling will do all of it on a mid-tier plan. Ask a marketer whether their email is personalized and they'll show you the merge fields and the branching logic and say yes.Ask the person receiving it and you get a different answer. Christina's position is that martech made accuracy so cheap that creativity started to look like a nice-to-have, and customers noticed long before marketers did.Her frame for the whole industry is the Jeff Goldblum line from Jurassic Park, the one about scientists so preoccupied with whether they could that they never stopped to ask whether they should. We're living inside that meme, she says, running on a lot of we could so we can. And the cost shows up somewhere marketers rarely look, which is in what we've trained people to expect.Conditioning is real, and it doesn't require a marketing degree. Somebody w...

UX design burnout doesn't come from working too many hours. That's one of Marc Aquino's hot takes in this episode, and once it sits with you, it's hard to keep blaming the hours.Marc is senior product design manager at Deel, a global HR and payroll platform helping companies hire and pay employees and contractors in over 150 countries. He's spent 15 years in the industry, seven of them building and scaling design teams across startups, scale-ups, and global companies.His take started with a LinkedIn post responding to a Nielsen Norman Group article on psychological ownership, research originally from Lynn Van Dyne and John Pierce. Burnout builds from a cycle. You get excited about a UX project. You pour research and effort into it. Then stakeholders get involved, and it gets scoped down or reshaped until it barely resembles what you built. Repeat that for months, years, and it wears you down more than the hours ever could.Marc talks through how he tries to protect his own team from that cycle, including a framework for pitching big ideas without getting shut down, and a line from his LinkedIn post that stuck with Sarah: the UX designers who last the longest hold the shipped thing loosely and their own judgment tightly.Then the conversation shifts into hiring. What Marc actually wants to see when a UX project never launched. Why team metrics tell him more than individual ones ever could. What happens when a candidate claims credit for a huge revenue number all on their own. Deel gets an enormous volume of applications, and Marc is candid about how little time he actually gets to review each one.Topics Discussed✅ Why UX design burnout might have less to do with hours worked and more to do with losing authority over something you feel deep ownership over✅ The repeating cycle that quietly wears UX designers down over months and years, and why fast-paced companies make it worse✅ A framework for pitching big, ambitious ideas at work without getting shut down before you even present them✅ Why you should include projects that never launched in your UX portfolio, and what to talk about instead of the outcome✅ Why leading with a huge dollar figure or revenue percentage in an UX interview can backfire, and what to say instead✅ Why team metrics tell a UX hiring manager more about a candidate than an individual one ever could✅ What actually happens to your application in the days after you hit submit at a company receiving millions of applications a year✅ Two team rituals that help a UX design team avoid burnout and stay aligned every monthLinks & Resources

Pawana Burlakoti on AI, Empowered Teams, and Product Sense Everyone says their team is "using AI." Pawana Burlakoti, Product Management Executive, can show you what hers has built. In this episode of Productside Stories, she tells host Nicole Tieche how her teams make AI in product management part of the job, not a side hobby. She also covers the hiring bar she won't lower and why her UX team reports into product. Her verdict on this year? It's "the applying stage." And no, product sense isn't going anywhere. Key Topics Discussed in This Episode Learn and play: how Pawana's teams use AI Every team in Pawana's org has a goal to learn something with AI and share it. One teammate built a best-practice repository in a single evening. That used to take months. Hiring product managers in the AI era Looking for an AI-mature product manager? Pawana says they don't exist yet. She hires for strong product sense, then looks for candidates who've built something with AI because they couldn't help themselves. How to build an empowered product team Pawana's UX and research teams report into product, so clients hear one voice instead of three competing agendas. She also explains how she grows empowered product teams, where even early-career PMs lead.  Why Listen to This Episode? In this thought-provoking episode, you'll gain: A model for AI adoption your team can copy by Monday A straight answer to "Will AI replace product managers?" (Short version: not the ones with product sense.) What an SVP of Product looks for when she hires PMs today The case for putting product and UX under one roof and building shared capabilities once across your portfolio Hit play before your next "So, what are we doing with AI?" meeting. Related Resources Check out these additional tools and resources to add to your PM belt: Productside Resource Library More Productside Stories Podcast Episodes Explore Productside Courses 

Fresh from IIEX AI, Karen Lynch and Sarah Snudden unpack the ideas that stood out across two packed days of conversations about AI and the future of research. They explore why AI's biggest opportunity isn't simply greater speed or productivity, but creating more room for strategic thinking, stronger relationships, and better business decisions. Sarah and Karen discuss accountability in AI-assisted work, the evolving role of synthetic data and AI-moderated qualitative research, and why human judgment remains essential even as research workflows become increasingly automated. The conversation also underscores a key takeaway from IIEX AI: cognitive offloading can be useful, but cognitive surrender is not. Researchers still need to question AI outputs, apply critical thinking, and remain accountable for the work.Key Discussion PointsMoving beyond productivity: Why time saved through AI can be reinvested in strategic thinking, skill building, relationship building, and higher-value business advisory work.Keeping humans accountable: Why researchers need to own AI-assisted decisions and outputs rather than fall into “accountability offloading” or cognitive surrender.Synthetic data and AI-powered research: Where synthetic data, digital twins, and AI moderation can add value—and why real-world data and thoughtful validation remain important.The changing research toolkit: How AI-assisted qualitative research is expanding access to richer conversations while UX, MRX, technology, and cultural insights increasingly converge.Developing the next generation of researchers: Why experienced researchers need to model critical thinking and methodological rigor so early-career professionals learn how to recognize, question, and improve AI-generated work.Resources & LinksIIEX WestYou can reach out to Sarah Snudden on LinkedIn.Many thanks to Sarah Snudden for being our guests. Thanks also to our production team and our editor at Big Bad Audio.

Cerramos este mes de septiembre del 2026 con 5 consejos para mejorar o crear tu portafolio UX utilizando IA. Aunque ya estés trabajando como producto o UX designer, este episodio te interesa. >> Ir al artículo relacionado>> Ir al curso online relacionado>> Ir al taller presencial en Madrid (17 y 24 de octubre 2026 en Madrid)

O design brasileiro é feito de muitos territórios, histórias e perspectivas, mas boa parte das discussões ainda se concentra nos mesmos lugares. Neste episódio, conversei com Luanna Silva sobre o Design&Dendê, uma iniciativa nascida em Salvador que conecta design, cultura e tecnologia a partir do território e busca fortalecer o mercado de design na Bahia.Falamos sobre as motivações que deram origem ao evento, a importância de descentralizar as discussões de design no Brasil e como os saberes e a cultura local podem influenciar a forma como pensamos experiências, inovação e impacto social. Também conversamos sobre a proposta de criar uma ponte entre conhecimentos ancestrais e pensamento contemporâneo para construir futuros mais diversos, éticos e contextualizados. Um papo sobre design, território, identidade e a importância de ampliar as vozes que participam da construção do nosso mercado. Senta o dedo no play e compartilhe pra fortalecer os corres.LinkedIn Luanna https://www.linkedin.com/in/luannasilvadesigner/Participe do Festival Design & Dendê usando cupom PAPO10 https://www.designedende.com/event-details/2-edicao-do-festival-design-dende-2026-conferencia-13-e-14-11Livro do Papo de UX https://editorabrauer.com.br/livros-de-design/papo-de-ux-vol-1/?srsltid=AfmBOorOkAtvY_JWtzMz2OSYe7Yz_jIgtIsIMWIP9-D-MLxmLHfhCc3kParticipe do Clube do Papo https://papodeux.com.br/clube/Mentoria Luan Mateus https://mentoria.luanmateus.com/News do Papo ⁠⁠⁠⁠⁠⁠⁠https://papodeux.substack.com⁠⁠⁠⁠⁠⁠⁠Instagram ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://instagram.com/papodeux/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@papodeux⁠⁠⁠⁠⁠⁠⁠⁠⁠

Earlier this month, world model company Runway introduced GWM Worlds 2, a research preview that “turns high-fidelity video and audio generation into real-time interactive simulation.” Runway calls this an “autoregressive diffusion” model; with autoregressive describing how it generates over time.One new feature in particular caught our eye: WorldPrompt, a proposed input format for specifying a generated world and the actions within it. It allows you to fix some aspects of a simulated environment — including the first frame — and then create a series of timestamped events. The events, or actions, can even be prompted in real-time.To understand the implications of WorldPrompt, we spoke to Kamil Sindi, Runway's CTO, and Robin Kahlow, its Principal Research Scientist for generative video and multimodal AI. We also have exclusive comments from Anastasis Germanidis, co-founder & co-CEO of Runway, courtesy of a podcast swyx and Vibhu did with him.Who's building real-time interactive world models?First, some context about world models that can generate interactive video and audio in real-time.Runway is reportedly valued at $5.3 billion, based on its most recent fund raise of $315 million in February. Its first release, GWM Worlds, was launched last December.Alongside Runway, there are several other notable projects in this domain: Google DeepMind's Genie 3 (which also generates at 720p and 24 fps), Odyssey-2 Pro, and World Labs' RTFM (Real-Time Frame Model). We've summarized their differences in the following table:Given the complexity and massive latency demands of real-time video and audio generation (which we'll get into below), all of the projects listed above have limitations. For instance, Google notes that Genie 3 “can currently support a few minutes of continuous interaction, rather than extended hours.”But as our interviews with Runway show, real progress is being made.The central idea of WorldPromptWorldPrompt, a new feature in GWM Worlds 2, helps differentiate Runway from its competition. You can think of it as a control layer for characters, cameras and the environment. As Kahlow put it, it's a way to “control all the different subjects in the world” — similar to a computer game.“Like, if there's an NPC [Non-Player Character] somewhere, the NPC might walk up to you and say something. So you could achieve the same thing with this kind of model, where you can have very detailed control over everything in the scene.”As the name suggests, WorldPrompt is a prompting mechanism — not a programming language. So, unlike virtual world games like Minecraft or Roblox, GWM Worlds 2 doesn't offer scripting capabilities or the ability to control state. But there's a power to that, as Sindi pointed out.“You can create promptable worlds on-demand with video and audio in sync, across all these different domains and environments. That's not a distant-future hypothetical thing,” he said.But there are also limitations to prompting a world model. We asked how reliably the model would follow an instruction to create, for example, a law of gravity or a certain ability in a character?“Yeah, so it's a research preview,” Kahlow replied. “So it's not perfect, of course, and there are still flaws. It really depends on how difficult the action is. I would say movement works quite reliably.”Sindi added that more training plus scaling the data and models is resulting in “better following.”How a video model becomes a real-time runtimeDespite the current limitations of GWM Worlds 2 — especially if you compare it to pre-designed and scriptable worlds like Minecraft or Roblox — the true promise of world models like Runway is that they'll eventually lead to fully self-generated, real-time games and experiences. Which is an extremely hard engineering problem, as Kahlow reminded us.“There are two challenges. One is making the model not generate a whole clip at once. So instead, you want it to generate frame by frame while you're looking at it. And the other challenge is actually making the generation fast, so you can play it in real time.”GWM Worlds 2 offers real-time interactive worlds streamed in continuous 720p video at 24 frames per second (fps) and audio at 48,000 Hz.Runway achieved this firstly by taking its foundational audio-video generation model and fine-tuning it to the new WorldPrompt format, so the model can follow that. It then post-trains the model to generate autoregressively.“And after that, we work on making it real-time through distillation methods,” Kahlow added.Co-CEO Anastasis Germanidis offered more technical details in our podcast with him. He told us that the process starts from “bidirectional diffusion that basically generates an entire video at once and [makes] it autoregressive.” This allows the model to “generate one frame or a few frames at a time.”Germanidis described two possible forms of distillation in order to make it real-time: distilling a larger model into a smaller one or reducing its diffusion steps. As a general example, he said a model might go from around 50 denoising steps to four, with some quality loss but potentially comparable results.The challenges of real-time generationGermanidis admitted that there were issues with how it generates real-time interactive video.“The biggest challenge with autoregressive models is error accumulation,” he said. “You're feeding generated frames back into the model to generate the next frames, and if there are any small errors, they accumulate over time.”Sindi told us there are also challenges dealing with “infinite generations” of content.“There's all these challenges around what context to keep, what to discard that's not important. And so there's all these optimizations we have to think about, so we're not blowing up our GPU memory.”Another current limitation is long-term memory. “The model does not have perfect memory,” Kahlow said. “That's still an open research problem.”Causality and correctnessWhile performance is the primary challenge for Runway at this time, its world model also has to produce plausible consequences when a user takes different actions.Germanidis used the example of simulating football; he pointed out that online video training data contains more successful goals than failed goal attempts, so a video model might render the first more convincingly.“If I take this action versus this action, you want it to generate equally realistic outcomes,” he told us. “That's, I think, the big gap between video models and world models: that idea of counterfactual generation.”Sindi told us that evaluation gets harder the more complex interactions get.“If you have this multi-prompt, multi-character, multi-scene [environment], how do you really understand what was causal and what was not?”To try and solve that, Runway has some automated verifiable tests. But since GWM Worlds 2 is a research preview, Kahlow noted that doing tests yourself is also advisable — “trying out your model to see what doesn't work is really important.”More than gaming — there are agent use cases tooGaming is the obvious use case for what Runway is building, but there are others. Kahlow mentioned robotics — for example using a simulated environment to test how a robot works.Another, more intriguing, use case is to use it to test agents at scale.“Having thousands of simulated environments is much less challenging if you have a suitable model like GWM Worlds,” Kahlow said.But how does an agent know what's changed in the world — is there a structured state that it can read, or is it just the generated video and audio that it's consuming and understanding?“So there's no structured state here,” Kahlow replied. “It's just observing the same thing you might observe in real life, just [in this case] from cameras.”Sindi noted that GWM Worlds can also be used for “synthetic data generation for agents.”Finally, Germanidis suggested there's potential to use these world models alongside reasoning models.“You're maybe using some reasoning [for] planning of the scene, and then you're passing it into the diffusion head that's actually generating the pixels.”Anastasis Germanidis* LinkedIn: https://www.linkedin.com/in/agermanidis/* X: https://x.com/agermanidisTimestamps00:00:00 Introduction00:05:17 Runway's Origins and the Bet on Generative Video00:12:23 The Stable Diffusion Story00:18:44 Gen-2, Controllability, and the Weekend Hack00:23:02 From Video Generation to World Models00:28:03 Learning From the World, Not Just Language00:35:04 Sora, Runway's Existential Crisis, and Gen-300:39:39 Why Real-Time Video Is Inevitable00:43:06 Interface World Models: Software Without Code00:50:25 The Fully Neural Operating System00:55:11 World Models for Robotics01:02:32 Robot Policies and World Action Models01:07:47 The Lucid Dream Test01:11:41 Video Agents and Omni Models01:23:12 Artists, AI, and Creative Workflows01:27:14 Physical AI and the Future of World ModelsTranscriptIntroduction: Runway, Creative AI, and the Early ThesisSwyx [00:00:00]: Okay, we're here with, Anastassios from Runway, with, me and Vibhu in the studio. Welcome.Anastasis [00:00:08]: Good to be here.Swyx [00:00:09]: Congrats on all your success and progress with Runway. You're opening offices all over the world. Did you envision this when you first started out?Anastasis [00:00:16]: Not quite. I think even when we started, we had this idea that, It was more a matter of when, not if, we were seeing the early generative models of 2016, 2017, and just extrapolating, assuming, we resolution, quality increases predictably over time. There's gonna be a point where most of content will be generated, and that was maybe the initial thesis of Runway was we will need, as a result of those generative models, rethink how creative tools are made. and as we built out the research behind, our generative models, it then became clear that they were useful far beyond that as well.Anastasis' Background: Art, Simulation, and Machine LearningSwyx [00:00:57]: And it is more obvious now with, like, the real-world stuff and the world models that we'll talk about later. I'm just kinda curious how you go from a background in, like, Zocdoc and, computer vision into Runway. Like, take us back to that early conversations with Chris and, whoever else is on your founding team.Anastasis [00:01:14]: I was always splitting through those two worlds. One was the I had my own art practice. I was making a lot of interactive art, I think for a long time. and then on the other side, I was working in startups, and I was working as a ML engineer, as a backend engineer at different companies. I've always been interested in, coding and computation, and especially interested in simulation and brought it back into my early artwork as well. And at the same time, I was interested inSwyx [00:01:43]: The personal site has a few, right?Anastasis [00:01:44]: Yeah.Swyx [00:01:45]: Is there one that we should pull up? Just in case there's something that's like. I just like to go down memory lane.Anastasis [00:01:50]: Yeah.Swyx [00:01:50]: Okay, what is this?Anastasis [00:01:51]: So this was, a project that I made, I think back in 2015, where I built this software that would give, voice instructions to people in a gallery space. So it would coordinate interactions between people. And so it will first give you an identity, like you're an, architect, you're 30 years old, and, you like sports. and then it would match you with another person, and you have this completely generated interaction. language models were not quite there at the time, and so it was it was a mix of some templates and some, like, some Markov chain-generated text, and it would just completely simulate these small talk conversations between, everyone in the gallery space. so was always very fascinated on the one hand with, generative models and, like, the early machine learning work that was being at that time. But at the same time, there was this separate thread of simulation and what it means. Like, what can we learn about humans by creating those very simple models of their interactions and their behavior?Early Generative Art: pix2pix, GANs, and Uncanny ValleyVibhu [00:02:56]: Did you generate the prompts or, the 30-year-old, whatever? Was it you generating them? How'd you, how'd youAnastasis [00:03:03]: Exactly. So the program would just generate- those, from. Yeah, a lot of it would be Mad Libs style of justVibhu [00:03:10]: YesAnastasis [00:03:10]: You have lists of different professions, lists of different,Vibhu [00:03:14]: HobbiesAnastasis [00:03:15]: Personality types, lists of different, ages, things like that. And then it would just combine those things together. And then maybe the next project we go is, Uncanny Valley, Uncanny Road, which wasSwyx [00:03:27]: GansAnastasis [00:03:27]: One of the first projects that, we built with, one of my two co-founders, Chris. This was taking, pix2pixHD, which was one of the early image-to-image models that NVIDIA released back in 2016 or 2017. and it was a model that would take a semantic map of a scene and then generate a photorealistic, let's call it, output. very early days, so it was not very high-fidelity outputs, but it w I think was the first image-generation model that could generate at 1K resolution. And it was all trained on self-driving datasets. So the semantic categories it would support were only, things you would encounter on the road. So it would be pedestrians, traffic signs,Vibhu [00:04:16]: StoplightsAnastasis [00:04:17]: Bikes, stoplights. And so that was one of our first indications that we built this and people were making all this, like, very surreal imagery of, yeah, a million plus a million pedestrians or a million traffic signs or, like, gigantic humans. And it was a indication that you could take a model that was trained on this very boring dataset, essentially, of, like, not that many interesting things happen when you're on the road, and then you can repurpose it and go very out of distribution and make something that was artistically compelling. And that was It's a summary of the thesis of Runway in some ways, that you can take the same generative models, and if you look at them from another direction, if you build interesting tools around them and you give them to artists, they're gonna do things that you don't expect.Vibhu [00:05:02]: Very cool. I like the, UX of it. You're just given an empty canvas, try whatever, do whatever. And then the other one, like, you see everyone with wired headphones? Like, that's, that's a sign that it's, it's veryAnastasis [00:05:16]: The AppleVibhu [00:05:17]: YeahAnastasis [00:05:17]: Apple, your version.Vibhu [00:05:17]: Original ads. Yeah. Take us to today. You've been doing this for seven years at Runway. How have we got to this? Like, how do we go from driving simulator data to all this? And you cover the whole stack of generative media?From Creative Tools to a Research LabAnastasis [00:05:33]: Interestingly, we're almost back in, we're, we're full circle. We're, we're now applying our models and beyond creative tools into real-world scenarios. But it was a, it was a long journey. It was very early on we realized the first version of Runway was a way to easily use the, all the open source model of the day, things like pix2pix to. and give them to artists. That was the initial idea, is those models are too difficult to use if you're not a machine learning engineer. Like, what happens when you give them to artists? Very quickly, we realized we needed to build a research org, inside of Runway, and that happened maybe on year one. And, a lot of the mandate there was. The image-generation models of the time, the video generation models of the time, or there were barely any video generations all the time, but they were not quite there where they could be productionized and brought into tools that would be part of creative workflows. so we need to push the frontier of the research. And so maybe the first four years of Runway, research was almost happening on the background until there was a moment in 2022, with latent diffusion, with, DALL-E 2, where, there was that step function change, and you guys maybe remember around the time.Swyx [00:06:49]: I started in this space because of latent diffusion and Stable Diffusion.Anastasis [00:06:54]: Yeah.Swyx [00:06:54]: Because I was like, “Wow, this is not only, like, feasible, it is doable on consumer hardware.”Anastasis [00:07:01]: Exactly, yeah.Vibhu [00:07:01]: I think the delta is also huge. Like, I learned pix2pix. Like, this was intro to ML, the TensorFlow, like, Jupyter, Google Colab notebooks were like this, and then you have a sudden step function change, with diffusion and whatnot. Any other ones since that. Like, there were clear examples of what early diffusion were to get to here. Any other changes in key technology research?Green Screen, Rotoscoping, and Early RunwayAnastasis [00:07:26]: Between, 2018 when we started and 2022?Vibhu [00:07:29]: Yeah.Anastasis [00:07:29]: So one of the early work that we did in Runway was solving segmentation, image and video segmentation. It was a very important problem because most VFX involves essentially separatingSwyx [00:07:42]: RotoscopeAnastasis [00:07:42]: Subjects. Yeah, rotoscoping. Extremely manual process. Nobody enjoys doing that. and so a lot of the early days of Runway was building this tool. It was called Green Screen, and it was for a long time the main thing that people were using Runway for. It ended up being used in, Everything Everywhere All at Once and a bunch of other high-visibility films and series. But that was essentially, Runway for a long time was a post-production tool until latent diffusion and generat- Gen-1, Gen-2, happened.Swyx [00:08:12]: Cool. let's, let's go past that moment. You've come a long way. Then you started releasing your own models. Maybe describe that journey as well.Scaling Video Models and the Bet on 1,000 A100sAnastasis [00:08:20]: Yeah, so we go to the other point, yeah, in mid-2022 when it became clear that we're doing research at a fairly small scale of compute, and it became clear that, like, scaling laws would apply to, image and video gen in the same way that we're applying to language generation. So we made a big bet, and I think at so at the time, we signed this deal to build a cluster of a thousand A100s, which at the time we were a Series B startup. That was a almost, slightly irrational decision maybe, but we really believed that if we trained a video model at a large scale, we would get, like, a great model at the end. And at the time, the goal or we set the goal around fall of 2022 of what is, what does the latent diffusion, Stable Diffusion moment look like for video? And at the time, the best model of the time was called CogVideo. it was one of the early video models. It was very 256 by 256 resolution, very not very high quality. and so we decided we're gonna build out this cluster, and we're gonna just invest in, like, in building out our own video model. it became clear as we're training Gen-1 that it was difficult to get to fully. we wanted to build text-to-video, but it became clear to us that an easier starting point would be to start from video to video. Because when you have a stronger conditioning, it's, it's an easier problem to restylize an existing video versus generate the video from scratch. And so we released Gen-1 first back in, it was January of, 2023. Yeah.Vibhu [00:10:04]: It's just a fun visual podcast, honestly. Like, if we can see February 2023, what was the state of stuff?Gen-1: Video-to-Video and Depth ConditioningAnastasis [00:10:10]: It's so interesting ‘cause at the time when you see those results, you think this is so incredible, and this is like, it's almost like image generation or video generation is solved. And then you look back a few years after, and it's like, it's It's just like you get used to the results very quickly, with those models. But at the time when we started seeing those results, it was, it felt quite incredible, and the level of, like, quality that you could get. And, so the Gen-1 was a depth-conditioned video model, so it would turn. it would take a input video, it would predict. it would it would first convert it into the depth map, and then we would generate, pixels with a latent diffusion model.Swyx [00:11:01]: Yeah, very effective.Vibhu [00:11:02]: Yeah. I didn't realize how distracting the blog post would be. Sorry.Anastasis [00:11:05]: Yeah, but, one of my favorite examples of on those, on Gen-1 was both, if you go up to mode three or mode two, there was this storyboard use case where people would makeVibhu [00:11:18]: OohAnastasis [00:11:18]: WouldVibhu [00:11:20]: You can mess around with theAnastasis [00:11:20]: Make a city out of books or out of boxes, and then they would shoot a video with their phone and then translate it into a photo-photorealistic output. There was all these ways in which those models were starting to be used for storyboarding and also for really. and then if you go to mode four, like, of taking untextured 3D scenes and then turning them into photorealistic output. So we saw a lot of use cases early on where people that were familiar, were power VFX editors would just take a blender, render, and then they would get translated in with Gen-1 or create a scene in Unity and then take a capture a video of it and then translate into, restylize it. So I still think video to video is powerful. I think we had a recent video-to-video model as well, and it's one of my favorite ways of using those models is essentially using them to use ground truth video as, like, the initial inspiration and then translate into different styles or different outputs.Stable Diffusion, Stability AI, and Open SourceSwyx [00:12:23]: But I think we're gonna go into, like, the rest of Runway and catch people up to speed today. I did wanna cover the, let's call it the Stable Diffusion controversy, or, what happened with Stability AI, whatever. I think there was a two sides of the story. I think there's part of that is a normal thing of, like, people, join and leave companies, but what is the, retrospective now that, there's been some years behind it?Anastasis [00:12:49]: Yeah, it's a very, it's a very long story to go into. I think it wouldSwyx [00:12:53]: Which I remember you wrote a really long post about.Anastasis [00:12:56]: We would probably cover the whole hour to go into it in more detail. But, essentially, there was the latent diffusion paper that came in, I think that was at the end of, 2021. And then Patrick Esser, who was one of the researchers behind, latent diffusion, and he worked at Runway at the time, he built latent diffusion in collaboration with Robin Rumbach and a few other folks back, in the in, CompVis, which was, a labSwyx [00:13:26]: Like a research group, yeah.Anastasis [00:13:27]: And, after releasing the early latent diffusion model, they, essentially they were. the goal was to keep working on versions of the model, scale it up, incorporate new data, incorporate new tasks. And Stable Diffusion was the same model, but trained on more compute, and then with a few more tricks, like a classifier-free guidance paper came at some point, I think in the early 2022. And thatSwyx [00:13:52]: Which, like, was a big prompting improvement.Anastasis [00:13:55]: Yeah.Swyx [00:13:55]:?Anastasis [00:13:56]: That improved results. it was trained on better data, so like, the esthetic subset of LAION, but it was effectively, the same underlying architecture. And there was that big training run, that, happened on Stability's cluster. Stability financed that run. And looking back at that story, I think it was the work to build and train that model was done. It was a, it was a research project. It was done as part of, like, continuation of the latent diffusion work. It then, I think it the model became very successful, and it, I think there were the. And I think as a result of its success, other companies tried to, figure out the commercialization path for it. But for us, it was very important that we try to, we make sure that we. It was meant to be an open source research project, and so the we decided that we should continue releasing versions of it, since that was the original goal of Stable Diffusion, and that led to releasing Stable Diffusion 1.5. There was maybe a day of, a bit of, miscommunication there, but ultimately that was resolved very quickly within hours. so yeah, there wasSwyx [00:15:12]: OkayAnastasis [00:15:12]: Not a niceSwyx [00:15:13]: I just wanted to. you have toAnastasis [00:15:15]: Yeah.Swyx [00:15:15]: You're one of the main players in that journey, and so it's nice to hear from the source of, like, what happened. Yeah.Anastasis [00:15:22]: Yeah. I think it's all, it's all in the past nowSwyx [00:15:26]: YeahAnastasis [00:15:26]: I would say. and, like, both companies, Stability took its own path, Runway took its own path.Swyx [00:15:32]: Yeah. There's still. James Cameron is backing the new Stability, whatever they're doing with the Hollywood studios.Anastasis [00:15:38]: Right.Swyx [00:15:38]: I don't know what they are doing. I think one thing that impresses me, and I'm happy to move on, is that back in the that time, let's say, like 2021, 2022, there was this community of people that you were involved in that was researching all this stuff, right? And, like, from everyone I talked to who was active then, it seemed like it was fairly obvious that somebody would do the hero training run that would produce Stable Diffusion. So, like, I guess the question is, like, you had the you were you had made investments. You were you had the foresight. Is it accurate to say, like, that is reflective of, like, what people were thinking at the time? Or was it still very much like, “Well, we'll use it as, like, a post-production tool or something. I don't know.”? Like, where in the sentiment were we that maybe you can think back to, like, what the community was like back then?The Early Creative AI CommunityAnastasis [00:16:28]: I reminisce and I think very fondly those early years, from like 2018 to 2022, because it was a very small community that, as you said, were very convinced that this was gonna be a big thing. And at the time, anyone who. Because it was such a small circle and, everyone who would, like, be part of that circle and, like, make projects with it would, immediately get, go viral. so likeSwyx [00:16:55]: And you didn't know who they are, right? They're just some name on a, GitHub or Hugging Face somewhere.Anastasis [00:16:59]: Exactly, yeah. So I remember one of the first big viral moments of creative AI was, there was the neural style transfer paperSwyx [00:17:09]: HuhAnastasis [00:17:09]: ThatSwyx [00:17:10]: Something dreaming?Anastasis [00:17:11]: I think it was called neural style transfer.Swyx [00:17:14]: Okay.Anastasis [00:17:14]: There was also Deep Dream, the puppy sliceSwyx [00:17:16]: YesAnastasis [00:17:16]: Which was, also really cool. but, yeah, there was this project that, Jim Kogan, who was an early advisor of Runway and one of those,Swyx [00:17:25]: Marketing guysAnastasis [00:17:26]: Big, creative AI, folks, he literally just, like, showed a video of himself taking the New York Subway and going over the Williamsburg Bridge and then stylized it with, I think in the style of Van Gogh or, like, one, painter. And that was. Like, at the time, that was, like, so cool and it went viral and it was completely revelation to people that you could do this with generative models. And that was only, it was less than. It was maybe 10 years ago. So just, like, as an indication of, like, how quickly things have gone.Vibhu [00:18:02]: It's pretty crazy. Like, even since then, you've got people at every level of the stack. You've got devs, creatives, artists, hobbyists. You've got everyone using it. And for people that tried stuff early, they'll remember how hard it was to use regular diffusion, right? Like, nowadays, you can use your favorite ChatGPT image gen or whatever, give a sentence, get a beautiful output. But diffusion was like, the whole ultra HD, 4K, high resolution. Like, prompting these things was very different. anything you learned on the tooling side, like from the offerings you guys have now, so like creatives, devs, you really took the. Research and brought it to everyone to use. anything interesting there to share?From Gen-2 to Controllable Video GenerationAnastasis [00:18:44]: We had to build the entire model serving infrastructure for video diffusion models. There was nothing else, already, like, because we had Gen-2 was the first text-to-video model, I think, out in the market. So many things that we learn over time. I think the I think the biggest one was, like, we. it was very clear early on that text-to-video was not gonna be the answer. Like, you. Like, people wanted a lot more control than that, and so we invested in, like, control building on top of those models very quickly. how do you use the camera trajectory as control? How do you use an initial input frame as control? So that was a very early learning for us. With text-to-video was, like Gen-2 was an amazing, step function improvement in the quality of video models, but it was used much more in an exploratory way because there was nothing to ground it to. There was no reference that you could bring into it. There was no. You couldn't really control the camera motion. You couldn't control the object motion. And so the first year, in 2023, was really all about what are all the interesting ways in which we can condition those models? And it was a lot of just post-training rounds on top of the base model to figure out, like, what, -- how do people wanna control them? And so there was, like, this quick succession of the we it was called Motion Brush, which was you could, like, you could draw arrows and dictate where things should move in the scene.Vibhu [00:20:09]: That's so cool.Anastasis [00:20:09]: There was camera control that was you could just describe, like, how you want the camera to move in the scene. And because we work with filmmakers from the most of the history of Runway, we immediately got this feedback and got this, decided that this was worth investing in. And so control ability became a big theme, I think, very early on as we were building, as we were building those models. Something fun that I haven't really talked about too much was just how Gen-2 came to be out of Gen-1. So it was a bit strange because we announced Gen-2 two months after Gen-1 andHow Gen-2 Came From a Weekend HackVibhu [00:20:43]: We're accelerating.Anastasis [00:20:44]: It was before Gen-1 was even generally available. But Gen-1 was a depth-to-video model, so it would take a depth map and it would convert it into RGB. and we couldn't get, text or image-to-video to work directly, and that's why we started from depth to video. but, and we had discussions of like, okay, we need to spend the next six months investing in text-to-video, maybe increasing the compute scale or the model scale, like train a larger model. And I had this weekend project idea, which was, what if I take a model that, starts from text input and converts to depth maps and then use Gen-1 to convert the depth maps Into RGB?Vibhu [00:21:29]: It would probably work.Anastasis [00:21:30]: And so Gen-2 was that.Vibhu [00:21:32]: Oh. The hackathon pipeline.Swyx [00:21:35]: The weekend hackathon pipeline.Anastasis [00:21:36]: Yeah.Vibhu [00:21:37]: But it looks good.Anastasis [00:21:38]: And it worked pretty well. there were if you, with the knowledge that it has this, like, two-stage pipeline, you can tell in some cases that the structure of the video looks a bit off because you had to generate the depth first before you go into the output video. But it worked and it allowed us to bring this to our, to users very quickly. But it's now it's interesting because, like, people are coming back to this almost two-stage approach. Like, if you look at the Reve text-to-image model that came a few months ago, it had this planner model that would generate bounding boxes before it fed that into the diffusion transformer.Swyx [00:22:19]: Yeah, Ideogram also the same day.Anastasis [00:22:22]: Yeah.Swyx [00:22:22]: I remember that was very strange that both of them came out the same day with the same exact innovation.Anastasis [00:22:26]: It's a small community, I think.Swyx [00:22:28]: I'm like, this is like, this is completely coincidental, right?Anastasis [00:22:32]: People talk. So yeah, there's, there's definitely something into this approach. And, now, like every single like, video generation model in production uses a complex prompt completion pipeline under the hood. I think that's no secret that there is. ThatSwyx [00:22:48]: Humans are terrible at prompting.Prompt Rewriting, Camera Control, and the Seed of World ModelsVibhu [00:22:51]: I think across the board.Anastasis [00:22:51]: Yes.Vibhu [00:22:52]: But yeah, I think like the original Sora one blog post even told you that what happens after your input is rewriting your prompt. It's much more descriptive about what you would want.Anastasis [00:23:02]: Exactly. I, And there was the DALL-E 3 paper beforehand that, was the first public, description of the fact that synthetic captions and really detailed captions work really well. And then Sora built on that. Yeah, so it was 2023. We were releasing all these updates to Gen-2, like the camera control, Motion Brush. And there was something very interesting about camera control because it was the first time that you felt that instead of, like, you were creating video, you were creating a short video, you were navigating inside the world. And I think camera control was maybe the seed of some of the ideas that we had around world models and really opening up that research direction. We realized, it was this era and this series of, Gen-1 and Gen-2 models really proved to ourselves, yeah, this is theSwyx [00:23:56]: Cool.Anastasis [00:23:57]: So this is not the original camera control. This was the updated camera control on top of Gen-3. But yeah, I think it made those models usable to filmmakers, I would say. The so camera control was very popular. And so we realized, there is one way of seeing those models, which is, you're just as content creation machines, and there is the other way, which is you're. As you're predicting video in order to predict video well, you need to simulate the world in an increasing and increasing capacity. And if scaling laws apply on video, just like they apply on language models, then as we scale the compute that we put into those models, then they're gonna be able to simulate physics, they're gonna be able to simulate human actions and dynamics increasingly well and predictably well. That was the thesis about around our efforts on world models, and we spin up this research group to just focus on the world models and how do we turn the video generation models that we're building into something broader and something that would be useful beyond, also content creation as well.Swyx [00:25:04]: And that was roughly when?Anastasis [00:25:06]: Yeah, so that was inSwyx [00:25:06]: OhAnastasis [00:25:07]: In late 2023.Vibhu [00:25:08]: Interesting. like, I think, a lot of people have been saying a lot of video gen model companies have all pivoted to world models these days, but like, 2023, you're posting it. oneWorld Models: From Video Generation to SimulationSwyx [00:25:21]: It's, it's debatable whether it's a pivot.Vibhu [00:25:23]: Yeah.Swyx [00:25:23]: Like, arguablyVibhu [00:25:24]: YeahSwyx [00:25:24]: That's what you always had to do anyway, right?Anastasis [00:25:26]: It's in a way an expansionVibhu [00:25:28]: YeahAnastasis [00:25:28]: Of the applicationsVibhu [00:25:29]: YeahAnastasis [00:25:29]: Of the models as they become more capable.Vibhu [00:25:31]: The early signs, it seems like the original models you guy had, guys had, people would say it's very not bitter lesson pilled, right? You're adding, rewriting prompts, you're having all these one-off things, but that's just the state of the tech as it was versus the future of as you said, you can scale it up as, we can scale up to world models.Anastasis [00:25:50]: Yeah. So it just became. And if you looked at the outputs of Gen-2Vibhu [00:25:56]: YeahAnastasis [00:25:56]: It was not. I think it was not obvious to people that this would scale to become a general simulator of the world. Like, you had very limited movement, you had, very low fidelity or low resolution, like obvious mistakes in human anatomy, like all kinds of limitations. But it was just, the idea was that's just GPT-two, and GPT-two, it can barely generate, like, coherent sentences. Similar, Gen-2 can barely create coherent video, but if you scale it up, you're gonna. There is no reason why it shouldn't work in a way. It's, And I think that was. That's, that's always the mindset of Runway is like this extrapolation of, like, if, like, even when we started in 2018 and you looked at the results of the day, you need to look more at the trend of, like, where we were in 2018 versus when we were at the, when the first GAN came out in twenty, four 2014 or twenty, fifteen. And, you started from, like, thirty-two by thirty-two images of faces, and then by the time in 2018, you could generate, street images at the 1K resolution. And it was the same with world models, very early signs of something much bigger.Swyx [00:27:08]: Yeah. I was gonna say, like, it's diffusing into focus. Like, if you look at our visible output from year to year, it looks like a diffusion process itself.Anastasis [00:27:17]: Yeah.Vibhu [00:27:17]: Especially watching the early, like, old blog posts, you can really see the choppiness, the details.Anastasis [00:27:24]: Yeah. Like human civilization starting from random noise and thenVibhu [00:27:27]: YeahAnastasis [00:27:27]: Denoising intoSwyx [00:27:28]: Yeah. Just run it a hundred years.Anastasis [00:27:30]: Civilization.Swyx [00:27:30]: Yeah.Vibhu [00:27:31]: That's how you're on track, you're still noising, right?Swyx [00:27:34]: Yeah. I like the way that you guys phrased it when you, announced it in June, which is, oh, that you had a video essay. “The human mind is no longer the center of AI. Our world is.” Right? Which is, let's, let's call it the past five years of LLM-based AI is very much like trying to emulate human preferences and human speech. But now that's, like, mostly solved. I think that's, like, some of the context of your essay, which you also wrote around the time. And now it's like the focus is on modeling the world accurately.Scaling Laws for Video and Why Predicting Pixels MattersAnastasis [00:28:03]: Exactly, yeah. So the way we see it is, there is that, initial mission statement of DeepMind, which is, solve intelligence and then use it to solve everything else. But I think it's starting from everything else, could be valuable of, like, starting from. there is just so much complexity, and detail in the world that in order to. That it's, it's hard to learn directly from just human descriptions of the world. Like, we're assuming that, like, language models learn from everything that humans have written about the world, like our own understanding as of, the twenty twenties. And there is just so much that we don't know and so much that's not captured by existing text, about both the low level dynamics of the world, like we're not describing in detail. if I tell you to describe, like, how do you tie your shoes, that's a very difficult thing to describe in words, but it's very obvious thing to demonstrate. And so I think there's been. And there's, more of X paradox, like we're constantly underestimating all the complexity that goes into very, like, things that we do subconsciously as humans, and we don't even necessarily always have the words to describe them. And so in my mind, the simulating the world and simulating, physics, simulating the dynamics of the world has always been underestimated, compared to, we place too much emphasis on the things that are easy to talk about. but there is just all this complexity and richness of the world that if we just try and train directly on that observational data instead of training on how people describe the world, we would learn something new that we wouldn't otherwise know.Swyx [00:29:54]: You think that the present architectural paradigm is fine? You don't need, like, another layer, like JEPA, like another famous, New York AI leader would say?Anastasis [00:30:05]: We're a very pragmatic research lab. If, we have evidence that an approach works better than the approach that we're taking, then we have no qualms to taking it. We just have seen no indication that video prediction itself doesn't scale. And even if you look now, not just our work, but the work of others, you're seeing in robotics some of the most promising work, starts from video prediction models, and then you adapt them to also the action models, for example. so there is very little evidence that you need something else and that your time is better spent on a novel architectural change compared to improving data and improving the, and scaling the current approach. And so, We don't have any indication that. the, there is that counterargument that I think there was a tweet by Yann LeCun a few days ago that, understanding the dynamics of the world is very different than, generating, cute videos.Swyx [00:31:05]: And your answer is no, they're the same thing.Anastasis [00:31:07]: Yeah, they're the same thing.Swyx [00:31:08]: My cat videos are the same as understanding physics.Anastasis [00:31:11]: Right, because if you wanna generate. video models can cheat and, like, they could you could give, like, successive dif shots of the scene in a way that doesn't require you to simulate difficult physics. There is like, all these different ways in which you can hide the deficiencies of the model, and it's important not to be too tricked by the performance of the current video models. It's easy to, cherry-pick examples and think that video models are further advanced than they are. So there is a lot more work that we need to do to improve those models. But in my mind, very similar to language, and, like, we've. you go from barely coherent sentences to something that, could hold a conversation with a human to something that could can operate autonomously for a day and, like, create entire code bases. And the main difference, there is some architecture improvements along the way, but the main thing is scale. And so it's the same bet for video, and we have no indications that this is saturating. Like, we have benchmarks that we use for measuring the physics of those models, and we see those predictably improve as we scale those models. So there is. If you want to Google up, Physics-IQ, is one of those benchmarks that measures how well does the model perform at solid mechanics or fluid dynamics or optics.Vibhu [00:32:32]: I'm curious if you've seen any emergence, any scaling law around this.Swyx [00:32:37]: Yeah, he's saying there is a scaling law, right?Anastasis [00:32:39]: Exactly.Vibhu [00:32:40]: Yeah,Anastasis [00:32:40]: So the way those models, those benchmarks work is you. the researchers have gone and, like, captured, a few videos that are representative of different physical phenomena, and then you can take the first frame and then pass it through an image-to-video model and then generate a rollout that shows what should happen next. So you have, a ball hanging from the ceiling, and then you use that as input, and then you the model predicts how the ball should fall on the ground. and this measures. we have an intuitive understanding of physics. I know, you can imagine what will happen next if I drop this bottle. So it's measuring that same intuitive physics understanding of those models, and we've measured that at different model scales, and we see, and compute scales, and we see that the score on physics IQ predictably improves. There's other, tricks and techniques that you can make to improve the score even further, but even scale alone helps, in the model learning better physics.Swyx [00:33:40]: My main sympathy with Yann LeCun is the, Plato's cave allegory, right? Like, you're, you're, like, learning on the output of a thing, not the internal process of a thing, and it's very noisy. And, if only you could observe the internals of a thing. It's hard to observe the internals of a human mind, but you can very much observe, or at least we have a whole branch of science and physics that we're ignoring on how to model Physics and movement and, gravity and, other interactions. and we're just, like, throwing away all of that and just saying just scale data, which is very much the lesson of unsupervised learning, but it feels wrong. that's the main idea.Anastasis [00:34:21]: I think the history of machine learning is, at large, it feels wrong.Swyx [00:34:25]: Yeah. It's a bitter lesson, right? Yeah. It's, it's, it's the simple answer to that.Vibhu [00:34:29]: I guess, how much can you scale? So, like, even on, let's say, the video generation side, like, there's one side of video understanding. Video generation, are we still gonna have tools where it's like, I wanna generate two hours, twenty hours? there's a infra way to do it in batches and stitch it together, but, like, do we just keep scaling? Do we just continue long generation consistency, all that at scale? And, like, tying it into where we're at now from we looked at Runway two to four point fiveGen-3, Sora, and Runway's Scaling InflectionAnastasis [00:34:58]: Yeah.Vibhu [00:34:58]: Like, technically, what advancements have we made to today, and then where do you see things still going?Anastasis [00:35:04]: So part of the answer is definitely scale. and that was. We learned that lesson in a big way for with Gen-3. So Gen-3 was the model we released the year after, like in 2024. That was a few months after Sora was released. so yeah, there's an interesting story of that came to be as well. Gen-3 for us was, the first time that we really needed to build. we had to learn all the lessons that the language model world learned in two in three years in the span of a few months. one of the biggest changes of Sora was using diffusion transformers instead of convnets. So a lot of the early, latent diffusion models were all, convnets for the diffusion model part. And the diffusion transformer paper came at some point in 2023, and it showed scaling laws for image, diffusion transformers. And we realized at that point that we needed to invest in infrastructure for model parallelism, for really scaling training to larger than, a few billion parameter models. And we spent maybe the, most of the fall of 2023 building out our infrastructure for distributed training. And we had a lot of false starts and a lot of failure in trying to scale, image and video diffusion transformers. And at that point, February 2024, Sora comes out, and the results areAnastasis [00:36:35]: Very much superior to what Gen-2 could produce. There were a lot of, a lot of chatter on Twitter about Runway. Runway's done. like, there is no way Runway will catch up. And if you remember, also OpenAI in the early twenty-It felt very, like it's aSwyx [00:36:56]: To the moonAnastasis [00:36:57]: It's a formidable opponent now, but at that point, it, they were on the top of their game. nobody could even get close to them. There was maybe Gemini was just the first version of Gemini had just released. So when OpenAI came with Sora and it was such a big jump of like quality, it gave me, there was like an existential crisis for a few hours. But that, I think the amazing thing about Runway and like I think the, we've been around eight years now, which is almost we're dinosaur in AI, and we had to like, we had there was a lot of those moments we had to learn, adapt very quickly and build out skill set in the team that we didn't have. And so, if you ask anyone what is their favorite time at Runway that was there during that time, it was that push in like three months to get to a model better than Sora. and it, we scaled 10x the model scale, the model size and the, compute that we were training on. we figured out model parallelism. We had zero expertise in that. And then we came out with Gen-3 during that summer. So that was a big turning point, I think, for the company where the research org grew very quickly, and we really started pursuing this vision of the general world model, in earnest, I think after Gen-3 was out.Swyx [00:38:12]: Yeah. that's the amazing thing about building when you're building. There's no stack to. You have to invent everything yourself. You have to be completely full stack. Now I think like there are inference specialists like Fal or whatever that can help with like, model serving, and I think you guys work with them as well. but yeah, like it's, it. But at the time, it was just. It's very interesting to think about what you do when Sora comes out and people are questioning whether your company should still exist.Distillation, Turbo Models, and Real-Time VideoAnastasis [00:38:41]: Yeah. And yeah, there was no, there was no VLM of diffusion models. Like, we had to build the whole model serving infrastructure and make things efficient. And a few months after we released Gen-3, we released the Turbo version, which I think was the first step-distilled model in production.Swyx [00:38:56]: That was a whole trend that we covered as well. Yeah.Anastasis [00:38:59]: So that allowed us, to serve those models at the larger scale, ‘cause I think the first version of Gen-3 was quite, expensive to serve.Swyx [00:39:09]: I think the whole like trend in like consistency models, Lightning and, Turbo and all these things somehow didn't really stick around. I don't know if you have any reflections on this. Because at the time, I was like, “Well, everything should start with a distilled model first, and then you can upscale,” right? It. your bigger models just turn into fancy upscalers, but like you should always draft with a smaller model and faster model, right? Because you can get it so quickly, like near real-time.Anastasis [00:39:39]: Yeah. I would not be so sure to say that didn't stick around. I think that, it's, it's likely to. that there is a lot of step-distilled models that are actively used in production. there is still a gap in quality compared to the, non-distilled model. but in my mind, we're still. there is a two to three year offset from language models. So the things that, So it's just a matter of time before there is better distillation techniques. we use. Right now we have a real-time model core character that I think is the largest deployment of real-time video models, that's a step-distilled model, and it's actively being used. It's a very specific use case compared to a general video model. So this is aSwyx [00:40:27]: Very cool, by the way.Anastasis [00:40:27]: This is avatars stuff, right?Swyx [00:40:28]: Consistency, character.Anastasis [00:40:30]: Yeah. So this is a talking avatar, model. we were able to. we optimized the hell out of it, and it generates at 24 FPS, and it's a, it's a step-distilled autoregressive video model. So if we look at our world model direction, a big component of it is starting from the bidirectional diffusion that generates entire video at once and making autoregressive shows. So you generate one frame or a few frames at a time. so there's a lot that goes into that pipeline of getting to a real-time model. It's first you need to make it into a causal autoregressive model, and then you just turn it into. You need to do some additional step distillation to get it to be real-time. and I think that part is just starting. I'll be very surprised if we're, two years from now, we don't primarily use real-time models. To me, real-time video generation is just inevitable that, it has much better user experience, it's much cheaper to serve, and, the quality gap between the base model and the real-time model is only gonna close as we figure out better, distillation techniques. And we made a lot of progress there internally on maintaining the quality of the base model when we distill them.Swyx [00:41:49]: How much of this is transferable? So is it the same base model? Like if you're doing diffusion across the whole sequence and you're converting it to step autoregressive distillation, is this like distillation where you still need to train both, you can use the same base and converter? What's that process like to go from regular model to something that's real-time on a technical level?Anastasis [00:42:11]: So the nice thing about diffusion models is you have, two axes of distillation. So there is the. You can distill to a smaller model, which resembles what you do in LLMs, or you can distill in terms of taking less steps, less diffusion steps. So you could take a model that generates in fifty steps and generate in four steps and get to, You have some performance, degradation, but very often you get comparable outputs. So you can even take the large frontier model and distill it with step distillation and get to a real-time performance, and that's what we've seen. So, depending on the use case, in some cases we might also serve with a smaller model, but in a lot of use cases, we just use theSwyx [00:42:56]: Step distillationAnastasis [00:42:56]: The frontier model, and we're able to make it work in real-time.Swyx [00:42:59]: I think this might be a good time to cut over to his laptop to show off some of the real-time stuff that you're doing.Interface World Models and Neural SoftwareAnastasis [00:43:06]: This is one of the research updates that we did recently. so we've been working and f in getting our general world models to, different applications. one of them that we think is very compelling is using general world models as essentially, an interface, a universal interface to software. This is a version of our world model that's called an interface world model. and the idea is that it essentially, replaces, the, front end of a software application. It renders the pixels directly of an interface and is trained to predict what happens next as a result of, a click or another interaction you have with the interface. So this is all pixels. it's there is no HTML, CSS, React that's powering this interface. This is directly at the output of our real-time, video generation model, and it takes clicks directly as input.Swyx [00:44:09]: And drags, click and drag.Anastasis [00:44:12]: Right. So it supportsSwyx [00:44:13]: Ooh.Anastasis [00:44:14]: Yeah, clicks. It supports drags. it also supports scrolling. and the amazing thing about this is that you can effectively describe in the prompt how you want different elements, like what do you want the behavior of different elements to be. So it's almost you're you can turn, an interface from, markup language description of, like, an HTML interface, and instead you can just describe the interface. if I press this button, I expect this to happen. If I press this button, this should happen. And it's useful, we believe, both for prototyping, for, like, just testing, like, what different interactions would feel like. you can also add audio to it. So it's a video audio generation model. So you get you essentially can describe both what the visual outcome should be of your click and also what the if there is a sound effect that comes out of it. So we believe that's gonna be a much more flexible way of building software. Just render. It just, in why generate the code that generates the pixels? Just generate the pixels directly.Anastasis [00:45:18]: It's the end-to-end philosophy applying applied to front ends.Anastasis [00:45:25]: So we think there is a few interesting use case. So you can build creative tools on top of it.Anastasis [00:45:32]: We think that, for any use case that involves a lot of exploration or, like, educational use case where you wanna learn about a new concept and you want some visualization and like, and open-ended exploration, we think those this is a very powerful, approach. you can imagine new forms of, design, industrial design software that could emerge as a result of those models. And this is all, generated in real-time as well. So, you can build a lot of interesting camera transitions and forms of interaction that are very difficult to build otherwise. And one way in which we evaluate this is what if you try to generate the same interface with Claude by just, prompting Claude, “Here's an image reference of my interface that I made in Figma or that I created somewhere else. create this particular interaction,” which in this case it's, drag that object, upwards. and beyond it being slower, it's also very difficult to capture some interactions by just fully, with just LLMs. So we think that this is likely to be the way that a lot of the future, like, software in the future will be created. and one of the additional benefits is personalization might be a lot easier done with those models. Like, you can essentially try out different prompts based on who is visiting the interface. You can, more easily, prompt engineer the interface to have larger size, text for more accessibility reasons, or you can make this or, like, if you have a particular aesthetic preferences. So we're very excited about this approach. It's early days, and I think we'll need to, make it more cost-effective as well to serve those models ‘cause, running a real-time video model versus just purely rendering HTML, there's -- the computational needs are much higher. but we do see a lot of potential in this approach to building front-end interfaces.Swyx [00:47:47]: So we covered this similar thing with Flipbook before with our, Ethan Hara episode with Groq, video. And yeah, I think it's very engaging visually. I think it's maybe very good for education, but it's it does sound expensive. I think there's an upper bound to how expensive it will be, though, right? Like, the inference cost will go down over time. You'll figure out ways to optimize it. Effectively, when it pauses, you don't you're not receiving human input. You don't have to generate anything, right? So.Anastasis [00:48:14]: Yeah, you could also. Like, in this case, you have ambient motion, so there is parts of the screen that might. if you're let's say you wanna, visit Paris and then you get this interface that allows you to explore.Swyx [00:48:29]: People walking. Yeah.Anastasis [00:48:29]: You have people walking or, like, things happening. But, it's, it's a no Yeah, it makes it more expensive because you need to run the model all the time. Maybe you have some looping mechanism so you don't need to do that. But all those things, I think, is stuff we'll need to figure out.Toward a Fully Neural Operating SystemSwyx [00:48:44]: Yeah.Anastasis [00:48:44]: I think our first consideration is let's make this clearly find some use cases where it's clearly a much more compelling interaction compared to traditional interfaces. And then it's a matter of time before it becomes more cost-effective to serve.Swyx [00:48:58]: Yeah. When it comes to the people walking, I think the approach that makes the most sense to me is Nick.Anastasis [00:49:04]: Nick.Swyx [00:49:04]: Oh, God. I keep messing up their name. With Chris Manning and Fanny Yan. I don't know if you've come across them, where they. Mapped to some game engine. I think it's Unity or something, or Godot. And they you can script some NPC behavior behind that and train on that. Whereas here, you can really imagine whatever you want. Like, that is a UI, right? Like, and it feels, like, more tractable, I guess, to, create a world model of software that is interactable because we have many of examples of that, and you can, do your fancy RL environment stuff on that than it is scaling up to embodied and real-world physical use cases. But this is a nice first step.Vibhu [00:49:43]: Or, there's the opposite of you have, like, one B models, three 50 million parameter language models. It just gets so small that they're just predicting, like, fishes moving.Swyx [00:49:53]: Small models are now 120 B, so.Vibhu [00:49:57]: Ultra mini on device.Vibhu [00:49:58]: But, no, I think it, like, it puts it into perspective, at least the car one for me, like, the applications, right? The amount of work to do that, sure, you only make one model year car per year, but applying this, it's also a cost-saving to have to manually make all this, right? So it opens up a lot of possibilities, too. I'm curious if you extend this out two, three years, so where do you see things going even further?Anastasis [00:50:25]: Effectively, the end game of something like interface world models is you have, a fully neural operating system. So I think, Andrej Karpathy has written about that quite a while back. But it's, You, I think to me it's, it's a bit, it's a bit odd that, we have, for example, with an interaction with an LLM of today, you have this LLM that can talk to you about anything. It can You can take the conversation in any direction. You can It's very general, so it can solve all those different tasks, but you interact with it through a very rigid interface. And so to me, it's just a matter of time before the interface itself becomes learnable and becomes, part of the whole loop of, like, you're not just delivering. You're delivering an application end-to-end, and that means you're delivering the language model, but you're also delivering the render and the pixels and that's also a learnable component. And the concept of applications might not necessarily. I think we'll need to figure out new abstractions for software. the concept of application comes from this idea that you need, separate code bases to describe, to, for, to power each individual, tool and each individual application. But you might think of something a lot more unified if you're. if you have, a video model that's generating the interface as you go. so it can take context from an LLM and allow you to combine different functionalities that traditionally would live in different applications. So it's a, it's a way to solve, software end-to-end, effectively. We also see this as a powerful way to train computer use agents as well. so this is, one way to see this as. And in general, with world models, there is those two directions. One is world models for humans and world models forSwyx [00:52:24]: AgentsAnastasis [00:52:24]: To train agents.Swyx [00:52:25]: Yeah.Anastasis [00:52:25]: And so for every new work of, world models that we do, we have this both uses become possible. So this is a powerful synthetic data generator for training computer use models. It could become, a live, RL environment that you could use to do online RL with a computer use agent, and you can get wide diversity of different interactions, kinds of interfaces, just generated on the fly that, to improve the how robust the, your agent, becomes. So that's the same also with the world models that we're working on for a robotics use case as well.Long Context, Error Accumulation, and Autoregressive VideoSwyx [00:53:02]: Is there a research breakthrough that you're Waiting for that would unlock the next set of use cases that you really wanna pursue?Anastasis [00:53:10]: Long context is a very important one, so being able to maintain consistency for long periods of time, and that depends on the use case. So for our characters model, for example, or for the interface world model, it's easier to maintain long sessions of interaction. If you go into more open-ended worlds that you navigate and you take arbitrary actions in, we, like, there is more the context at which you can and duration which you can generate becomes limited much more quickly.Swyx [00:53:40]: Yeah.Anastasis [00:53:40]: So we see more degradation and error accumulation happening. so the biggest challenge with autoregressive models is error accumulation, is you're feeding generative frames back into the model to generate the next The next frames. And if there is any small errors, they accumulate over time. That's not a new problem. It's a problem that LLMs also have, and we've seen the ability to generate now really long outputs. So it's a solved problem, but it's definitely still a challenge.Swyx [00:54:08]: Yeah. And what is the state of the art? so for Grok, it would be like 10 to 20 seconds of context going in there for video.Anastasis [00:54:16]: With our characters models, we're able to generate up to 30 minutes of video autoregressively.Swyx [00:54:21]: Yeah. But that's just for the avatars.Anastasis [00:54:24]: Yeah. So if we look at, GWM Worlds, which is more our open-ended world exploration model, it's, it's on the order of a few minutes, which is Yeah, soSwyx [00:54:35]: Probably enough for people because you have to cut to the next scene anyway, right?Anastasis [00:54:40]: Yeah, it's not, it's not the ideal game experience if you have to restart every few minutes. So I think. But, I think it's. Yeah, for certain kinds of game experiences, you can work around it.

Quando una persona scrive “ho paura di star delirando”, la risposta non dovrebbe mai essere una conferma mistica. Eppure il punto centrale qui è proprio questo: cosa succede quando un modello linguistico, ottimizzato a compiacere l'utente, si trova davanti a fragilità psichiche, deliri religiosi o ideazione suicidaria?Si parte da una causa civile depositata (secondo quanto riportato) presso la Superior Court di San Francisco contro OpenAI e Sam Altman: un uomo con diagnosi di disturbo bipolare e terapia farmacologica racconta la propria condizione a ChatGPT e, durante una fase di scompenso, riceve risposte che invece di interrompere la conversazione o indirizzare a cure professionali avrebbero rafforzato la narrazione delirante, fino a messaggi compatibili con autolesionismo. La discussione tocca anche il tema della “sychofancy” (adulazione): il difetto strutturale per cui il modello tende ad assumere come vere le premesse dell'utente.Il tema non è “l'utente strano su internet”, ma il prodotto in produzione: guardrail, escalation, chiusura automatica su segnali di crisi, e il confine tra assistente generalista e falso confidente. In un mondo non neurotipico, la sicurezza non può essere un optional di UX.00:00 Quando ChatGPT ti dà ragione00:00:17 Il caso (presunto) di Michael Lins00:01:48 La causa contro OpenAI00:02:34 Diagnosi, farmaci, delirio00:03:35 Versione GPT-4 e update ritirato00:04:35 Psychofancy: l'adulazione del modello00:06:07 Da bug a evento avverso00:08:00 Guardrail: chiudere e segnalare#ChatGPT #OpenAI #GPT4 #AIassisted~~~Ciao Internet! - Il primo e più seguito canale di TECH POLICY in italia, con Matteo FloraINFO E AZIENDE: https://matteoflora.comIl CORSO di AI: https://zero.matteoflora.comNewsletter: https://link.mgpf.it/nlSocial: https://io.matteoflora.comEnglish: https://www.youtube.com/@CiaoInternetPrivacy » https://privacy.matteoflora.comAI Policy » https://privacy.matteoflora.com/aiMail #adv: sales (at) matteoflora.com

It's a regular Thursday sync until my manager cuts our Figma license and hands the whole product team one month to go fully AI. In this one I walk through who made that call, the surprisingly solid case for it, and the very selfish panic running through my head while I said nothing.When the tool you spent a decade mastering loses its market value in a single meeting, what exactly are you afraid of losing: your job, or your identity?This is the episode about the day the call actually came down. I'm at my kitchen counter with a fresh coffee, hopping on a normal weekly UX sync, when my manager announces the entire product team is moving to a 100% AI workflow by the end of the month. And by the way, the Figma license is getting cut. No debate, no “what does everyone think.” The decision was made in a room I wasn't in, then delivered to a room full of designers who had about a month to deal with it.I do two things in this one. First, I try to be fair to the decision, because it deserves it. I get into the cost math leadership was staring at in early 2026, the pricing question Bob Baxley raised after I posted about the switch, and why ripping the band-aid off with a brutally short deadline was probably the right move and not the reckless one it felt like. Then I tell you the less flattering half: what was actually going through my head in that meeting. Spoiler, not one of those thoughts was about our users or the work. Every single one was about me, my title, and whether being “the person who knows Figma best” was ever a real skill to begin with.I also get into the part nobody sat us down to talk about, which is that these tools blur the lines between design, engineering, and product in every direction at once, and introduce the “in the loop, on the loop, out of the loop” question that runs through the rest of the season. If your company hasn't had this meeting yet, I'd gently suggest not assuming it isn't coming. Give this one a listen, then let me know about your own version of the kitchen counter moment.Topics:• 0:00 The day they cut our Figma license• 3:00 The cost play: why pay for two tools?• 4:05 Not my call: where I sit in the org• 4:50 Nobody said "adapt or quit" (the soft mandate)• 5:30 The fair case, and Bob Baxley's pricing question• 7:15 Ripping the Band-Aid: off the dock, into the boat• 8:40 What was really in my head: "just UI engineers now?"• 9:45 Kevin the plumber• 10:50 Watching a decade of skill depreciate• 11:50 How remote teams process shock• 13:10 Does engineering even want us in the code?• 14:00 Every role blurs at once• 15:10 The walk• 15:55 In the loop, on the loop, out of the loop• 17:10 Next time: what we traded away—Thanks for listening! We hope you dug today's episode. If you liked what you heard, be sure to like and subscribe wherever you listen to podcasts! And if you really enjoyed today's episode, why don't you leave a five-star review? Or tell some friends! It will help us out a ton.If you haven't already, sign up for our email list. We won't spam you. Pinky swear.• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get a FREE audiobook AND support the show⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Support the show on Patreon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Check out show transcripts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Check out our website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe on Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe on Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe on YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe on Stitcher⁠

Send us Fan MailCrypto doesn't have a technology problem, it has a translation problem. Most people outside the space hear one word and mentally bundle everything together, from Bitcoin to meme coins to Ethereum, then assume it's all the same risk. I sit down with DeFi Dave, Head of Growth at CAP and a longtime DeFi educator, to break that framing and rebuild it with clear categories, simple analogies, and practical takeaways founders can use right now.We explore why on-chain finance is in a real metamorphosis: stablecoins are becoming more institutional and more “TradFi-like,” while tokenised stocks and stock tokens are pulling liquidity and experimentation back toward the wild energy of early DeFi. Dave explains stablecoins in the most useful way I've heard: like PayPal or Venmo balances, except on a public network with many counterparties verifying balances. That shift changes everything for product design, marketing, and adoption because the winning stablecoin UX makes the blockchain disappear.Then we go deep on on-chain credit. Dave outlines CAP's approach with funded underwriters who post collateral, do diligence, and take penalties when risk is mispriced, aiming to solve the incentive issues that have made lending markets blow up again and again. We also cover regulation (what the Genius Act clarifies, what lending still lacks), macro conditions like rates and liquidity, and why rising markets can trick teams into believing they found product-market fit. Finally, we get into “lore building,” where the community writes the story and the company acts as the shepherd, plus how to stand out when AI makes content flat and abundant.This episode was recorded through a Descript call on September 9, 2026.Read the blog article and show notes here: https://webdrie.net/how-stablecoins-and-on-chain-credit-are-going-mainstreamIf you care about stablecoins, DeFi lending, on-chain credit, crypto marketing, and building trust at scale, this conversation will sharpen your thinking. Subscribe, share the show with a friend, and leave a review if you want more conversations like this........................................................................... 

AI for Designers: 5-week Bootcamp

Dr. Darren Hood launches Milestones in Dysfunction, a new sub-series within Harsh Realities of UX Maturity. In this episode, he examines how misdefining and misappropriating UX—particularly reducing it to “UX/UI” or visual polish—has weakened the discipline's scientific, strategic, and business-centered foundation.#ux#podcasts#cxofmradio#cxofm#realuxtalk#worldofux#worldouxDon't forget to like, subscribe, and share!Bookmark the new World of UX website at https://www.theworldofux.com. Visit the UX Uncensored blog at https://uxuncensored.medium.com. Get your specialized UX merchandise at https://www.kaizentees.com.

UX portfolio approaches based on seniority or role. That question lands in Sarah Doody's inbox nearly every day. Does the approach actually need to change? This episode answers a real question from a listener targeting director level UX roles, wondering if the portfolio frameworks taught inside Career Strategy Lab only apply to individual contributors.The process for building a strong UX portfolio doesn't change based on your job title. That's the real answer buried in a question Sarah gets constantly, usually from people convinced their situation is the exception. This time it came from a listener targeting director level UX roles, wondering if the frameworks taught inside Career Strategy Lab only apply to individual contributors. Sarah's answer: no.Three reasons why. A portfolio's job stays the same no matter your title or years of experience. UX recruiters and hiring managers are always answering the same core questions. The way a portfolio gets consumed doesn't change either. A role gets 500 applications in 48 hours. Every portfolio gets skimmed and scanned, not read word for word. And what makes a portfolio good is consistent across roles too. Answer the so what behind your decisions. Not just what you did.Gamified, operating system style portfolios are trending on LinkedIn right now. Sarah explains why they create friction instead of clarity for a recruiter trying to move fast. Then there's what-ifing. Hunting for UX portfolio advice specific to your exact job title, years of experience, or city. That search doesn't exist, and it's costing you time you don't have.If your job title or seniority level has you wondering whether you need a different UX portfolio strategy, this episode settles it.Topics Discussed✅ Why the three questions UX recruiters and hiring managers are actually trying to answer never change, no matter your job title✅ How the "jobs to be done" framework applies to your UX portfolio, and what your portfolio's actual job is✅ What happens when a UX role gets 500 applications in 48 hours, and how that changes what your portfolio needs to survive✅ Why talking only about what you did is the most common UX portfolio mistake, and what answering the "so what" actually looks like in a case study✅ Why operating system style UX portfolios are trending on LinkedIn right now, and why they're probably working against you✅ The real cost of constantly asking "what if" about your specific UX job title, years of experience, or city instead of just building your portfolio✅ What went wrong with an early UX portfolio back in 2006, and what fixing it actually looked likeLinks & Resources

We've drilled into every corner of ecommerce on the show, but never the cart. Sylvie Wilson is here to fix that.After running two ecommerce businesses of her own, she built Unicorn Cart to solve a gift-with-purchase headache, and it's now used by 400+ brands. Her core message: the cart is the billboard everyone's neglecting, and it's a real part of your CRO strategy, not just a step to checkout. She's clear she's not here to sell Unicorn, just to share tactics and real A/B test data.In this episode:The cart is high-intent real estate most brands waste, optimise it for both AOV and conversionNever set and forget, one brand's $99 gift threshold beat $120 with a 64% higher AOVMake gifts something people actually want, keep the UX clean, and don't train customers to wait for a discountConnect with Sylvie Wilson Explore Unicorn CartSubscribe to the Add To Cart newsletter  SMS us to Suggest a Guest Connect with Nathan Bush Join the Add To Cart Community 

Martin shares his journey from traditional banking into crypto and explains why he now sees crypto working alongside banks rather than replacing them. The conversation covers stablecoins, institutional adoption, the changing altcoin market, privacy, and why simplifying crypto's complicated UX is essential for mainstream adoption. They also discuss how companies like ChangeNOW are becoming part of the financial “plumbing” that could eventually make blockchain invisible to everyday users. Learn more about your ad choices. Visit megaphone.fm/adchoices

Ever notice an idea gets a cold reception the moment it comes from another team? That's Not Invented Here. This week I dig into why our brains treat “not ours” as “not good,” what it cost teams I worked on at GE, and how to catch it early.When your team says an outside idea “won't work for us,” are you evaluating the idea, or just the team it came from?Not Invented Here is the tendency to reject or undervalue ideas, tools, and research that come from outside your own team, while overvaluing the stuff you built yourselves. The catch is that it never announces itself. It feels like you have standards, or you know your users, when really the source of the idea is doing the evaluating for you in the background. The term goes back to a 1982 study by Ralph Katz and Thomas Allen at MIT, who tracked fifty R&D groups and found that the longer a team stayed together, the less it talked to the outside sources it most needed to hear from. Performance slid as a result. Later work from David Antons and Frank Piller reframed it as an attitude rather than a behavior, a standing negative feeling toward anything labeled “external,” whether that label comes from a different company, department, discipline, or just a different job title.On product teams, this shows up everywhere once you start looking. Engineers' UX suggestions get waved off because usability is “our lane.” Agency research gets its methodology questioned the second it contradicts the team's assumptions, while a single hallway conversation gets treated as solid evidence. Ten squads rebuild nearly the same component ten different ways because the shared version doesn't quite work for us, and then nobody trusts the design system anymore.If your team has ever quietly shelved a good finding because it came from the wrong people, this one's for you. Give it a listen.Topics:• 00:00 - The reflex that rejects an idea before you've even looked at it• 03:00 - The GE story: a mandate to adopt one design system called Cirrus• 04:00 - Every team pushes back with the same "that won't work for us"• 05:00 - Defining not invented here and the 1982 Katz and Allen study• 06:00 - Why long-tenured teams stop talking outward, and the shift from behavior to attitude• 07:00 - Why your brain does it: ownership, effort, identity, and the IKEA effect connection• 08:00 - The measurable cost, and Spolsky's defense of building in-house• 09:00 - How the bias hides behind "our context is different"• 10:00 - How it hits research and design systems, and where the IKEA effect sneaks in• 11:00 - Engineering rewrites, competitor patterns, and acquired teams• 12:00 - Turning the bias around with "proudly found elsewhere"• 13:00 - Separate the source from the substance, and watch your team's own boundaries• 14:00 - Give ownership before adoption, and treat build versus adopt as a real decision• 15:00 - Audit your outside input, and back to the Cirrus lesson—Thanks for listening! We hope you dug today's episode. If you liked what you heard, be sure to like and subscribe wherever you listen to podcasts! And if you really enjoyed today's episode, why don't you leave a five-star review? Or tell some friends! It will help us out a ton.If you haven't already, sign up for our email list. We won't spam you. Pinky swear.• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Get a FREE audiobook AND support the show⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Support the show on Patreon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Check out show transcripts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Check out our website⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe on Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe on Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe on YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe on Stitcher⁠

In this candid episode of the Wellness Pro Show, Cate Stillman sits down with Brenda Rigney — former COO of an $80M business turned company-of-one entrepreneur, founder of SKOOL Scale Camp, and one of the most successful summit hosts on the SKOOL platform — for a deep-dive conversation on how to build a genuinely location-independent business without losing revenue or engagement. Brenda walked away from a corporate role trending toward a $500K solo business to intentionally rebuild everything around a different question: What if my business revolved around my life instead of my life revolving around my business? The answer became SKOOL Scale Camp — a freemium community that has grown to over 13,000 members across two brands, generates $55K summits in 2 months, and runs entirely from wherever Brenda happens to be in the world (currently Northern France, previously Spain, next stop London). Cate mirrors the parallel arc from her own 25-year online business journey — from HD Conference calls in 2001, through the launch of her own summit in 2015, to running Yogahealer, Club Thrive, and Wellness Pro Academy on SKOOL today. Together, they unpack what has genuinely shifted for wellness pros and online entrepreneurs, and where the leverage now lives. Together, Cate and Brenda explore: The Summit-as-Traffic-Engine: How Brenda's June 2025 launch summit brought 950 people into her community — and how her October and November summits collectively brought in 9,000 members and $55K in revenue Why SKOOL Won This Round: The specific engineering choices, funding, and forum-first UX that make SKOOL the current gold standard for community engagement — and why underfunded LMS platforms quietly decay over time The Three Revenue Streams of a Freemium Community: Sponsored speaker slots ($150-$575), premium/VIP membership tiers, and standalone high-ticket coaching packages — and how they compound without adding complexity The 60% VIP Discount Play: How Brenda structured monthly premium at $49 vs. annual VIP with 60% savings — and why the math still works even when accounting for monthly churn Onboarding Over Courses: Why creating more content is the biggest waste of time for community owners in the age of AI — and what to build instead (challenges, leaderboard gamification, live sessions turned into micro-courses) The Leaderboard as Conversion Signal: How gamification isn't about competition — it's a diagnostic for identifying which members are actually ready to buy Free vs. Paid Community Design: Why giving away too much for free is the #1 reason freemium models fail — and the "2% of the cake" rule for what to put in free vs. paid tiers The AI + One Human Team: Brenda's actual team stack — one longtime creative director (10-15 hours a month for integration work) plus a Claude-powered content team, growth strategist, and AI-first workflows Why Small Doesn't Mean Slow: How a $9 offer sold to a warm community of 13,000 can generate $5,000 in a single day — and why volume is now a viable path alongside high-ticket for the right personality The Speaker Recruitment Framework: How Brenda gets 75+ speakers per summit by starting with 1-2 anchor speakers and letting the network snowball — plus her "must reach Level 5 in the community" contingency This episode is a practical, honest look at what actually works for solo wellness pros, coaches, and mid-career women entrepreneurs building sustainable businesses in the era of AI and community platforms — without burning out chasing scale. Notable Quote: "It used to be like, when I do a summit, I have to be at home with all my stuff — camera, lights, all this. Now I'm like, why am I here? I could be in Paris running a summit. I could be in London running a summit. That's where this whole business model came for me. Not from scarcity — from abundance. I want my business to revolve around my life." Connect with Brenda Rigney: Website: brendarigney.com Community: SKOOL Scale Camp (free to join on SKOOL) Connect with Cate Stillman: Website: wellnesspro.academy Podcast: Wellness Pro Show Books: Body Thrive, Master of You, Primal Habits, Uninflamed Referenced in this episode: SKOOL (community platform) Sam Ovens (SKOOL founder) Alex Hormozi (referenced) Claude AI (Brenda's primary AI content team) MailChimp, Stripe integrations Meta Ads for summit promotion If you're a wellness pro, coach, or mid-career entrepreneur wondering whether it's actually possible to build a real business that funds a location-independent life, this conversation is a working example — from two women who've done it, at different scales, with completely different offer stacks.

AI for Designers: 5-week Bootcamp

Mujaahida Shakur, a behavior scientist and UX researcher with a background in public health and experience at Amazon Web Services, reflects on her journey into UX research and the challenge of translating a nontraditional career path into a compelling professional story. She explores career pivots, resume language, AI in hiring and research, inclusive recruitment, and the realities of navigating today's job market. She also explains how she turned networking into a research project and how her curiosity turned career conversations into opportunities for learning, self-discovery, and genuine connection.UPD: During the editing process of this episode, Mujaahida was hired at the SteerBridge as a User Experience Researcher. We wish her best of luck at her new company.

Ammarah Ahmed, founder of Precision Consulting, a CRO and UX/UI consultancy helping growth-stage businesses earn more from the traffic they already have, without increasing ad spend or hiring a full-time team.Through conversion rate optimisation, UX and UI design, and product experience strategy, Ammarah helps founders and growth teams understand why people are not buying, remove the friction, and build experiences that convert better and feel better to use.Now, Ammarah's decision to leave high-paying, high-visibility roles to build something of her own demonstrates the courage it takes to choose alignment over comfort.And while stepping into the pressure of building in public, putting her name out there, and wearing every hat as a solo founder, she is creating a business grounded in psychology, sharp thinking, and a real desire to be the change she once wanted to see.Here's where to find more:Company Website: https://goprecision.co/ Company LinkedIn: https://www.linkedin.com/company/goprecision/ Personal LinkedIn: https://www.linkedin.com/in/ammarahahmed/ GrowthMentor: https://app.growthmentor.com/mentors/ammarah-ahmed________________________________________________Welcome to The Unforget Yourself Show where we use the power of woo and the proof of science to help you identify your blind spots, and get over your own bullshit so that you can do the fucking thing you ACTUALLY want to do!We're Mark and Katie, the founders of Unforget Yourself and the creators of the Unforget Yourself System and on this podcast, we're here to share REAL conversations about what goes on inside the heart and minds of those brave and crazy enough to start their own business. From the accidental entrepreneur to the laser-focused CEO, we find out how they got to where they are today, not by hearing the go-to story of their success, but talking about how we all have our own BS to deal with and it's through facing ourselves that we find a way to do the fucking thing.Along the way, we hope to show you that YOU are the most important asset in your business (and your life - duh!). Being a business owner is tough! With vulnerability and humor, we get to the real story behind their success and show you that you're not alone._____________________Find all our links to all the things like the socials, how to work with us and how to apply to be on the podcast here:https://linktr.ee/unforgetyourself

Building Digital Hospitality: Elevating Business Visibility, AI-Driven Discovery, and User Experience with Houndstooth Media Group's Vivienne WagnerIn a recent episode of The Thoughtful Entrepreneur Podcast, host Josh Elledge sat down with Vivienne Wagner, Owner and Founder of Houndstooth Media Group, to discuss how scaling enterprises and service-based brands can optimize their digital discoverability in an AI-dominated search ecosystem. Vivienne, a leading digital marketing strategist and visibility architect, shares her battle-tested framework for moving away from empty marketing volume and focusing on deep strategic clarity, intuitive website user experience, and pain-point-driven content creation. This conversation delivers an actionable roadmap for executives and founders looking to eliminate digital friction, optimize their brand footprint for conversational AI tools, and convert online visitors into loyal clients using a hospitality-first mindset.The Digital Hospitality Framework: Optimizing Brand Discoverability and AI Search AuthorityThe primary bottleneck preventing growing enterprises from converting online traffic into qualified sales opportunities is a lack of foundational strategic clarity combined with high user friction across digital touchpoints. Vivienne Wagner explains that driving marketing volume without first defining a brand's core mission, specific services, and exact buyer personas results in wasted ad spend and misaligned lead generation. Rather than treating a website as a static brochure, business leaders must adopt a digital hospitality mindset—structuring their web properties like a well-hosted event where visitors can easily navigate clear resource stations, find immediate answers to pressing questions, and access self-serve tools without encountering administrative barriers.As generative artificial intelligence tools and modern search engines transform digital discovery, brands must evolve their search engine optimization strategies to ensure AI platforms accurately understand and recommend their services. Modern consumer behavior relies heavily on conversational AI queries that act as personalized digital referrals, making it essential for enterprises to publish highly detailed, authoritative content that directly addresses specific user symptoms rather than generic product features. Structuring online content with clear heading hierarchies, consistent brand messaging across third-party platforms, and comprehensive solution guides ensures that automated AI search agents recognize the firm as a trusted, primary recommendation for high-intent buyers.Sustaining long-term authority and client retention demands that corporate marketing teams reject cookie-cutter promotional packages in favor of tailored engagement models and high-value educational touchpoints. Business leaders should focus on executing high-impact foundational improvements—such as refining core messaging and streamlining website navigation—before investing capital in unproven marketing trends or secondary platforms. Integrating interactive educational events, such as regular live and on-demand webinars, allows organizations to deliver genuine value, nurture prospective accounts over time, and establish lasting domain authority. When clear strategic positioning, digital hospitality UX, AI-optimized search architecture, and customized client engagement models are synthesized into a single growth framework, enterprise leaders remove acquisition friction, build sustainable authority, and predictably expand enterprise valuation.About Vivienne WagnerVivienne Wagner is the Owner and Founder of Houndstooth Media Group, a prominent digital visibility strategist, and an expert in digital marketing architecture. Drawing from extensive experience helping businesses refine their digital footprints, Vivienne specializes in brand strategy, user experience optimization, search engine optimization, and content marketing. She is a recognized thought leader focused on helping corporate leaders, consultants, and business owners replace superficial marketing noise with strategic clarity, AI-ready search visibility, and client-centric digital experiences.About Houndstooth Media GroupHoundstooth Media Group is an elite digital marketing agency and visibility advisory firm engineered to help small-to-mid-sized businesses optimize online discoverability and client engagement. The agency specializes in delivering customized digital strategy audits, website user experience redesigns, search engine optimization, AI-driven content mapping, and educational webinar strategies. Through tailored consulting blueprints and hospitality-driven marketing models, Houndstooth Media Group enables organizations to eliminate visibility debt, build high-trust digital assets, and maximize market equity.Links Mentioned in This EpisodeHoundstooth Media Group Official Website: houndstoothmediagroup.comVivienne Wagner on LinkedIn: linkedin.com/in/vivienne-wagnerKey Episode HighlightsStrategic Clarity Over Marketing Volume: Establishing core brand identity, service specifics, and target audience alignment before deploying tactical marketing campaigns.The Digital Hospitality UX Model: Designing website navigation like a well-hosted event with self-serve resource stations that eliminate user friction.Addressing Consumer Symptoms: Crafting content that speaks directly to specific buyer pain points rather than broadcasting generic product features.Optimizing for AI-Driven Discovery: Structuring consistent, authoritative digital footprints so AI tools and conversational search engines recommend your brand.Customized Execution vs. Cookie-Cutter Packages: Prioritizing foundational marketing fixes and tailored strategies over chasing short-lived digital trends.ConclusionThe conversation with Vivienne Wagner underscores that building sustainable digital visibility requires an intentional synthesis of strategic clarity, user-focused web design, and AI-ready content architecture. By standardizing internal marketing governance, treating digital visitors with a hospitality mindset, and optimizing brand assets for modern search engines, business leaders can transform an underperforming website into a highly structured, self-sustaining client acquisition engine.More from The Thoughtful Entrepreneur

Jonathan Evens is a product lead at Google DeepMind, where he has spent more than a decade applying machine learning and AI across industries — from the smart grid at AutoGrid, to detecting roads and buildings from satellite imagery at Planet, to recommender systems, Google Search's AI Overviews and AI Mode, and now live avatars. He is also an advisor to the Evens Foundation, where he is building a "digital citizenry": a democracy sandbox that uses synthetic citizens to pre-test how the public might react to a policy before it is written.Jonathan returns to The Product Experience, where hosts Lily Smith and Randy Silver pick up the conversation they started at MTPcon London, to dig further into what actually separates an AI product manager from a product manager who simply uses AI tools, why product principles have to come before evaluations, and how synthetic users can help — and mislead — at very different scales of product.We discuss:1. Why "AI product manager" has become a near-meaningless label, and the two distinct roles hiding underneath it: the modelling product manager working on core model capabilities, and the AI feature product manager building AI-powered products2. Why using an LLM as a thinking partner or a coding assistant does not make someone an AI product manager — it makes them a product manager using AI tools, full stop3. How Google Search's North Star metrics have stayed constant even as the proxy metrics beneath them — side-by-side win rates, user ratings, RLHF signals — have had to be rebuilt from scratch4. Why product principles, not evaluations, are the real starting point for any AI feature, and how Google Search resolved the problem of trustworthy sources disagreeing on basic facts5. How Google Search builds trust into its AI Overviews through sourcing, citation placement and UX cues such as highlighting, so users can judge at a glance what to verify6. Where synthetic users genuinely help — cold-start problems, privacy-sensitive research, automated regression testing — and where they fall short7. Building the Evens Foundation's "digital citizenry", and the core technical problem behind it: AI-generated personas that are less diverse and more extreme than real people8. How team size and structure differ between a fully resourced lab like Google DeepMind and a resource-constrained non-profit team, and why Jonathan resists a single answer for the "right" team size9. How the product manager's job is shifting as engineers absorb more of the evaluation work themselves through prompting and iteration10. Jonathan's advice for product managers building AI features, and his case for following the Makers Manifesto Key takeaways"AI product manager" covers two distinct jobs. The modelling product manager defines and measures a model's core capabilities — factuality, reasoning, long context — and that role is concentrated almost entirely inside frontier labs. The AI feature product manager builds a product or feature on top of an existing model, and needs domain expertise and user empathy far more than technical depth. Conflating the two is why the title has become so diluted.Using an LLM to think faster or write code faster does not make someone an AI product manager. It makes them a product manager using AI as part of their toolkit — the same as any other knowledge worker. The distinction matters because it clarifies what skills are actually being tested.Product principles have to come before evaluations, not after. Before Jonathan starts building an eval set for a new product, he first asks what the product is meant to feel like and what values it should encode. Google Search's response to sources disagreeing on a monument's construction date, or to large language models hallucinating at scale, came from principles about trustworthiness established before any metric was built.Trust in an AI feature is built through sourcing and interface design as much as through the model itself. Google Search's AI Overviews are constrained to draw only from ranked, trustworthy documents rather than the model's own memory, and users are given UX signals — citation placement, highlighting — that let them judge at a glance how much to verify.Synthetic users add genuine value in cold-start scenarios, privacy-sensitive research and automated regression testing. Where they fall short is diversity: AI-generated personas tend to be less varied and more extreme than real people, which is the central technical problem behind the Evens Foundation's digital citizenry project.There is no fixed answer to the right team size. Jonathan sees a gradient, from a senior developer working entirely alone, up to the Evens Foundation's single product manager with AI-assisted development skills, up to a fully staffed Google team — with the deciding factor being how unsolved the underlying problem is, not company size.As engineers absorb more evaluation work themselves through prompting and iteration, roles are blending. What still sits with product management is the judgement calls that follow from product principles — deciding, for example, which technical trade-offs actually matter to the use case, rather than which are easiest to measure.Features links- Evens Foundation — https://evensfoundation.eu- Makers Manifesto — https://makersmanifesto.org- Google DeepMind — https://deepmind.google- AutoGrid — smart grid AI company where Jonathan began applying machine learning to industry- Planet — satellite imagery company where Jonathan worked on automated road and building detectionWe're refreshing The Product Experience and want your input. Take our two-minute survey and help shape where the show goes next! Our HostsLily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She's currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury's. He participated in Silicon Valley Product Group's Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He's the author of What Do We Do Now? A Product Manager's Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon's music stores in the US & UK.

In a return to the Harsh Realities of UX Maturity series, Dr. Darren offers a historical look at key UX trends, tying them to shifts in UX maturity across the discipline. Check out this episode in light of when you entered the discipline, how it has "changed," and why.#ux#podcasts#cxofmradio#cxofm#realuxtalk#worldofux#worldouxDon't forget to like, subscribe, and share!Bookmark the new World of UX website at https://www.theworldofux.com. Visit the UX Uncensored blog at https://uxuncensored.medium.com. Get your specialized UX merchandise at https://www.kaizentees.com.

Oladimeji Ajegbile, known online as Hello Dim, went viral for filming bike rides through Edinburgh with his two-year-old son perched on the handlebars, narrating every tree and bus he sees. He's also a former architect, UX designer, startup founder, stay-at-home dad, and, as of last year, a runner who clocked a sub-50 10K on roughly two training runs and just finished his first half marathon with a 50K trail ultra on the calendar. Zoë and Brendan talk with Dim about why the toddler videos connected when a decade of content didn't, the 90-day rule he uses to test every new pursuit, what the Piston Cup in Cars taught him about chasing medals, learning to ride a bike at 21, running the Union Canal in the pitch-black Scottish dark, getting dizzy at 30K of a track ultra, and his Substack essay on why achievement won't fix your emptiness. It's a conversation about being a beginner on purpose, making things for the process instead of the result, and what happens when the internet decides which part of your life it wants to see.

You've been showing up the same way for a while now, same website, same social presence, same elevator pitch, and it's worked. So you've never really questioned it.But "it's worked so far" and "it's still working" aren't the same thing.In this episode, I'm pulling from something I see constantly in my day job as a UX designer: the person closest to a product is usually the worst judge of whether it still makes sense to a stranger. I'll walk through why that happens, why it applies directly to your brand, and how to tell if you're in that blind spot right now, without scrapping everything you've built to find out.If something's been nagging at you about how you're showing up, this one's for you.DM me on Instagram @catescreative.studio if this lands.

Thomas Thierry is a Researcher at the Ethereum Foundation.Every transaction you make onchain is trackable. And most of us have just accepted that as normal, but we shouldn't have to!In this next episode of our Lean Ethereum series, Thomas walks through what's actually broken with privacy today, how Ethereum is getting closer to native private transactions, and what private DeFi will actually look like for end users. He also covers how Ethereum compares to Zcash, why privacy is essential for institutional adoption, and why being unstoppable as a network goes hand-in-hand with privacy.------

In this candid Wellness Pro Hotline episode, Cate Stillman sits down with Ally Watters — Wellness Pro Academy member, longtime bodyworker, and founder of Thrive Mode — for a real-time coaching session on the two hardest transitions most wellness pros face: migrating platforms and repositioning their business identity. Ally has been running her coaching offer on Hey Marvelous for years alongside a busy bodywork practice, weekly Thursday group calls, and email campaigns in MailChimp. The engagement she wants is there in her live sessions, but not in the forum — because her current platform simply doesn't foster it. As she considers migrating to SKOOL, she's also facing a bigger identity shift: moving from "bodyworker who coaches on the side" to "coach who does bodywork on the side." Cate walks her through both moves in real time — including how to migrate without rebuilding everything, why community engagement is the true multiplier of member success, and how to reposition existing clients into the new structure without losing them. Together, Cate and Ally explore: Why "Personal Netflix" platforms are quietly dying: The war for attention is now a battle between well-funded platforms, and if your LMS isn't investing in engagement features, you're losing every day you stay on it Rip Off the Bandaid Migration: Why waiting until "next season" to migrate is often a mistake — and how to move the 20% of content that actually matters instead of trying to port your entire library Why SKOOL Beats Facebook Groups and Hey Marvelous: The specific engineering choices (DMs, leaderboards, forum-first UX) that make SKOOL the current gold standard for wellness club engagement Community Is the Health Intervention: Why habit change requires other people also standing in the kitchen struggling — and why lone-wolf clients almost always regress The 20-Member Business Model: How Cate reframes Ally's mental math from "I need 300 customers" to "I need 20 members" — and how that changes every tech decision Manual Beats Fancy at Small Scale: Why enrolling 20 members a year doesn't require Zapier automations, complex funnels, or expensive integrations — a 10-minute onboarding checklist works better Repositioning as Service: How to move existing bodywork clients into your coaching container without them feeling upsold — by anchoring the change in their desired results, not your business needs The Daily 10-Minute Club Habit: What Cate does (and should do more of) inside SKOOL each day to keep engagement compounding — including shoutouts, message replies, and polls Gaming the Behavior You Want: Incentives like "$10 at Whole Foods" or "an ace in your back pocket for a 20-minute critical issue call" as low-cost ways to shift member behavior into the new platform This episode is a working example of what a Wellness Pro Hotline session looks like — no theory, no fluff, just direct coaching on the exact obstacles wellness pros hit as they scale from transactional to transformational. Notable Quote: "Some people are self-employed. There's a lifestyle cost that goes with that. Part of it for us is we have so much agency, we have so many choices, that it's easy to get caught up. But rarely do we have 300 customers. We usually have 20 to 35. So it doesn't need to be that fancy." Connect with Ally Watters: Thrive Mode Coaching Program Wellness Pro Academy Member Connect with Cate Stillman: Website: wellnesspro.academy Podcast: Wellness Pro Show + Wellness Pro Hotline Books: Body Thrive, Master of You, Primal Habits, Uninflamed Referenced in this episode: SKOOL (community platform) Hey Marvelous (legacy LMS) MailChimp + Stripe integrations Alex Hormozi teachings If you're a wellness pro considering a platform migration, repositioning your business, or wondering whether community engagement is really worth the effort — this real-time coaching session is your working example.

UX hiring managers are looking for three things right now, and UX isn't specifically one of them. Not exactly reassuring if you've spent years polishing your process pages. That's the reality Sylvain Maretto lays out in this episode, and it's coming from someone actually reviewing applications, not theorizing about them.At the time of this recording, Sylvain was the director of design at Ecosia, the search engine that funds tree planting with its profits. He's spent 15 years building and leading design teams across Berlin, Tokyo, and beyond, with stops at Omio (joined when it was 25 people), Zalando, Tour Radar, and GetYourGuide. He also helped seal the acquisition of Holoplot, the audio technology behind the sound system inside the Vegas Sphere.He joined Sarah after leaving a comment on a LinkedIn post arguing portfolios need to be more experimental. He disagreed, and what follows is his actual answer: a triangle of AI fluency, UI craft, and business acumen. UX gets treated as a given, not a differentiator.From there, Sylvain walks through what happens inside an applicant tracking system when 500 people apply for one role. Why the AI built into most of those tools produces noise instead of signal. What a job description is actually telling you, if you read it twice. And how to talk about results when a project got canceled before it ever shipped.If you've ever wondered what's actually happening on the other side of your application, this is that answer.Topics Discussed✅ The three things Sylvain actually screens for when he opens a portfolio, and UX isn't one of them✅ What happens to your application the second it lands in an ATS, and why you're one of 500 people applying for the same slot✅ Why the AI built into most hiring software is, in Sylvain's own words, mostly noise✅ The STAR framework he wants every case study to follow, and why your "action" section is probably too long✅ What to say about a project that got canceled or never shipped, when you still need to show results✅ How reading a job description twice instead of once changes what you should actually be highlighting✅ Whether hiring managers expect different things by country, or if it's really just about company stage✅ Why Sylvain thinks a narrow, specific niche beats trying to check every box on a job post✅ The values-based signal he built into his own hiring process at Ecosia that most candidates don't see comingLinks & Resources

Episode web page: https://bit.ly/4ywaHhh Episode summary In this episode of Insights Unlocked, Mike Mace, Director of Solution Marketing at UserTesting, talks with veteran product leader, author, and executive coach Rich Mironov about what AI-powered development really means for product teams—and why dramatically faster coding doesn't automatically translate into better products, happier customers, or more revenue. Rich argues that as AI removes engineering constraints, the bigger challenge becomes deciding what is actually worth building. He explores the risks of “cognitive surrender,” where teams equate faster output with better outcomes, and explains why product management, UX research, customer discovery, business judgment, and taste become more important—not less—as organizations gain the ability to build at unprecedented speed. He also challenges the growing enthusiasm for synthetic users, warning that plausible AI-generated feedback can reinforce what teams already believe rather than uncover the unexpected insights that emerge from conversations with real customers. The conversation also examines why “10x coding speed” won't produce 10x revenue, how AI shifts bottlenecks from engineering toward customer adoption and go-to-market execution, and why product managers may need to become more “barbell shaped”—spending more time understanding meaningful customer problems at the front end and turning products into business results at the other. Rich also shares ideas from his book Money Stories, including why product teams need to communicate the financial value of their work in language executives understand. You'll learn Why faster AI-assisted coding doesn't necessarily create better products or more revenue How “cognitive surrender” can cause teams to prioritize output over customer and business outcomes Why human discovery, judgment, empathy, and taste become more valuable as building gets easier The risks of replacing conversations with real customers with synthetic users Why product waste is fundamentally different from engineering waste How AI shifts product bottlenecks toward discovery, customer adoption, sales, and go-to-market execution Why product managers may need to become more “barbell shaped” in an AI-driven environment How “money stories” can help product teams connect their work to revenue and business impact Resources and links Rich on LinkedIn (https://www.linkedin.com/in/richmironov/) Rich's Product Bytes (https://www.mironov.com/) and Substack (https://richmironov.substack.com/)  The Art of Product Management (https://www.amazon.com/Art-Product-Management-Lessons-Innovator/dp/1439216061) Money Stories: Communicating the Value of Product Work (https://www.amazon.com/Money-Stories-Communicating-Value-Product-ebook/dp/B0GJTS2CW6) Our past interview with Rich on product waste and how to prevent it (https://www.usertesting.com/blog/how-prevent-product-waste) Mike Mace on LinkedIn (https://www.linkedin.com/in/mikemace/) Nathan Isaacs on LinkedIn (https://www.linkedin.com/in/nathanisaacs/) Learn more about Insights Unlocked (https://www.usertesting.com/resources/podcast)

 EPISODE DESCRIPTION I sat down with Jia Hang from DCS Group, a payments veteran with over two decades of experience at China UnionPay and Alipay, to explore how stablecoins are quietly revolutionising the way the world pays. Jia walks me through why the traditional card and mobile payment waves are giving way to a stablecoin-powered third wave, how DCS is acting as a Web 2.5 bridge so consumers can spend stablecoins at any merchant without the merchant needing to change a thing, and what it really takes to build trust on both sides of that equation. We also dig into the macro battle for stablecoin dominance, whether the US dollar will tighten its grip or lose ground to other currencies, and what the developing world's rapid adoption of stablecoins means for financial sovereignty. If you work in fintech, payments, or crypto infrastructure, this episode is packed with hard-won insights from someone who has built global payment networks from the ground up. DISCLAIMERNothing mentioned in this podcast is investment advice and please do your own research. It would mean a lot if you can leave a review of this podcast on Apple Podcasts or Spotify and share this podcast with a friend. Be a guest on the podcast or contact us - https://www.web3pod.xyz/ CONNECT DCS Website: https://www.dcsfintech.com/ DCS LinkedIn: linkedin.com/company/dcsccJia LinkedIn: https://www.linkedin.com/in/hang-jia-81a83a234/Web3 with Sam Kamani: https://www.web3pod.xyz/ KEY POINTS WITH TIMESTAMPS • [00:00] Sam introduces Jia Hang from DCS Group and his background spanning UnionPay and Alipay• [01:27] Jia's journey: from computer engineer to 9 years at China UnionPay building the Americas market• [03:05] Why Jia left Alipay after 10 years and stepped into the stablecoin payments world• [04:43] Why the global south leads on payment innovation , infrastructure gaps drove QR code adoption• [10:43] The three waves of payments: card, mobile, and now stablecoins , and why stablecoins merge information flow and fund flow• [14:23] How DCS focuses on C2B (consumer-to-merchant) stablecoin payments, not just B2B cross-border• [17:02] How the merchant experience stays unchanged , they still receive local fiat, DCS handles the stablecoin conversion• [20:39] DCS as a Web 2.5 bridge: connected to Web3 on the consumer side, Web2 on the merchant side• [22:27] The chicken-and-egg challenge , and why merchant-side adoption is harder than consumer-side• [25:44] DCS's Visa-backed stablecoin card: live in 9 countries, expanding to 70+• [28:10] What sets the DCS card apart: banking licence in Singapore, physical and metal cards, credit functionality, and Visa rewards• [33:12] The macro stablecoin currency war: will USD dominance in stablecoins exceed its fiat dominance?• [35:32] Jia's belief that all currencies will eventually become stablecoins , and what that means for smaller nations• [39:03] DCS's roadmap: auto-debit, subscription payments, smart contract-powered UX that feels like Web2• [42:07] Closing thoughts and preview of Token2049

AppleIphone DuoAvailable October Starting at $2000MetaUnveils personal AI agent: MuseNvidiaNvidia to buy Hugging Space for $13B.  Per WSJ. Jensen Blog post. More than 18 million developers, researchers and creators use Hugging Face to share more than 3 million models, 500,000 datasets and 1 million applications. More than 200,000 companies use the platform to discover, evaluate, customize and deploy AI.Hugging Face will remain an open platform for the entire AI ecosystem. Developers will choose the models they want, the frameworks they want, the clouds and inference service providers they want and the computing platforms they want. NVIDIA compute will not be required to build on or deploy through Hugging Face.NikeNike removed from S&P 100Lot's saying “go woke, go broke”.  Revenue down from 2024: $51B vs 46b. Net Income down $5B vs 3.1bI highly recommend Shoe Dog! AI Whistleblower from Anthropic on X breaking every record possible. The Jacob Coxon Post? Planned? Phyop? Theory is Anthropic wants regulatory capture so they can govern.TeslaCyberCab launch is looking legit. Appears to be 100's of cars in Austin. 420 registered at Texas DMV. hahah 420 Rooting for Waymo, Zoox and others to be successful. PLAY Elon just stole my idea. Uber/Lyft drivers buy Cybercabs. EngineeringUnboxed assembly. The car is built as parallel modules (front casting, rear, pack/seats, outer skins) and joined late. Tesla says the line footprint is about half a conventional line. Deleting hydraulics and paint is what makes that join possible.UnoColor-in-mold RIM body skins. Outer panels (doors, hood skin, roof, quarters, bumpers) are polyurethane made by reaction injection molding with the gold mixed into the part. No paint shop. Cycle time minutes instead of hours. That is a factory change, not a styling choice.Dry brake-by-wire. First Tesla with zero brake fluid, zero brake lines, no master cylinder. Each caliper has its own electric actuator. Pads can pull fully off the rotor (less drag). No fluid to flush on a 24/7 taxi. One of the first production cars in the world to do this dry; Chery in China got there a few months earlier.Steer-by-wire with no column. Cybertruck already had steer-by-wire and a wheel. Cybercab deletes the wheel. The rack sits behind the front drive unit and only takes electronic commands from the autonomy stack.Supermanifold V3 thermal system. Tesla's claim matches what you read: they cut extra refrigerant valves and lines, merged high- and low-voltage controllers into one box, say production is ~80% automated and the system is ~38% more efficient than other automotive thermal setups. That is the heat-pump / battery / cabin plumbing, not a marketing slogan about “climate control.”Smaller, lighter, rare-earth-free drive unit. Single front motor, 163 kW / 219 hp. Tesla: 18% smaller, 25% lighter than top competing EV units, designed for sub-10-second automated assembly. Ferrite / Halbach instead of neodymium. First Tesla that is front-wheel-drive only.No human controls as a packaging rule. No wheel, pedals, mirrors, or rear window. That is not just UX. It lets them drop hydraulic plumbing, a steering column, mirror drag, and a lot of crash structure around a driver. Cd claimed under 0.2.48-volt low-voltage architecture. Same idea as Cybertruck: thinner wiring, fewer fuses, easier automation. Most of the industry is still on 12V.Small 4680 pack, high miles per kWh. ~48 kWh, ~3,100 lb car, EPA-adjusted range near 300 miles. The engineering bet is efficiency and utilization (two seats, ~80% of trips) instead of a giant battery.Fleet-life design, not owner-car design. 500,000-mile target, camera washers, inductive charging as the intended depot method (NACS still there for now), automatic butterfly doors, cabin camera for leftover bags. That is ops engineering: fewer service visits, no paint matching, no brake-fluid intervals.In addition to: No steering wheel, pedals, mirrors…. READ Electronic actuators instead of hydraulic liquid brakes…this seems so obvious!! The Tesla Cybercab doesn't have ANY brake fluid. It is the first production vehicle to have a brake-by-wire system. Electronic actuators are responsible for the clamping pressure, rather than hydraulic fluid that you see in traditional vehicles.This system improves efficiency since the brake pads can fully disengage. They can go entirely off or on. It also enables Tesla to fine-tune the braking to be as smooth as possible.PLUS, when the car is built with the unboxed process, Tesla doesn't need to run any brake lines, everything is seamless.READ Painting: All panels are RIM Reaction Injection Molding. NO PAINT! Elons says TRUE! EntrepreneurshipDemand will be off the charts.Space XGrok is way better! SpaceX price targets. Today from Brett CFOSpaceX CFO Bret Johnsen has announced that the company has signed another AI compute deal that will generate $1.11 billion of revenue per month ($13.3 billion per year), starting on Dec 1, 2026."Earlier this month, we closed another hosting deal. By the end of this year, with annualizing our December number, we're on track to hit $100 billion of ARR."Launching first AI Compute satellites in 2027. Previously, have reported that Starlink V3 satellites are much more complex.Elon x-post and article. AI and Robots will solve the debt problem.You better all hope Elon gets us out of this mess. Play Gavin Baker talking about Phd's saying cooling in space is impossible.  Gavin Baker again on a great post of DataCenter Reality. Read: Engines compared. Webcast here. SpaceX CFO Bret Johnsen has announced that the company has signed another AI compute deal that will generate $1.11 billion of revenue per month ($13.3 billion per year), starting on Dec 1, 2026.DataCenter Hosting: Earlier this month, we closed another hosting deal. By the end of this year, with annualizing our December number, we're on track to hit $100 billion of ARR.Orbital Compute: Clean, unlimited energy, no “community” issues. First Orbital compute satellites launching in 2027Play  Tobi Lutke on the Picasso of our time: the SpaceX Raptor Engine. Moving forward by subtraction. Elon security detail has been “marshalled”.Boring CompanyBoring in Nashville. Per Total Recall, we'll need tunnels on Mars. So maybe an acquisition? CaliforniaThe state of California. By Chamath. Schools have a constitutional funding guarantee, bondholders sit ahead of most other claims, and Medi-Cal and pension contributions come with legal and financial constraints. Together, those four categories account for roughly $150 billion of the $226.7 billion the General Fund expects to collect.Recommendations Travis Kalanick on David SenraBill Ackman called all 400 people on Forbes 400 list. 96% rejection.

Isabella Aidar, known online as Socialista Champagne, is a Brazilian‑Canadian political educator, writer, and UX & interface designer for Tech for Palestine. With a focus on anti-imperialism, geopolitics, and applying historical and dialectical materialism, Isabella hopes to make materialist analysis and Marxist-Leninist theory accessible to a wide range of audiences from all sorts of biases and ideological backgrounds. She takes inspiration from Frantz Fanon, Radhika Desai, Michael Parenti, and the Communist Party of China to ground her theoretical knowledge in contemporary reality.   Find Socialista here! Substack: https://socialistachampagne.substack.com/ Instagram: https://www.instagram.com/socialista.champagne TikTok: https://www.tiktok.com/@socialista.champagne  

Ten years after The DAO hack changed Ethereum forever, Griff Green returns to Epicenter to discuss the next chapter: the DAO Security Fund.As a co-initiator of The DAO, Griff shares the untold story of recovering funds after the hack, why over 75,000 ETH remained untouched for years, and how its staking yield is now funding public goods for Ethereum security. The conversation also explores a provocative idea: Ethereum is incredibly secure—but still not safe for everyday users.Topics covered:1. The inside story of The DAO and its recovery2. Why the DAO hack may be the only hack where everyone made money3. The DAO Security Fund and funding Ethereum security4. Quadratic funding, grants & public goods5. Why phishing and UX remain Ethereum's biggest weakness6. Can Ethereum become safer than traditional banks?7. The future of safer wallets, OpSec, and the Ethereum Economic ZoneIf you enjoyed the episode, don't forget to subscribe for more conversations with the builders, founders and investors shaping the future of crypto.Links:Lido: https://lido.fi/stvaults?mtm_campaign=epicenterSponsors: Lido V3 introduces stVaults: a modular staking infrastructure that lets builders and institutions deploy custom staking vaults, while staying anchored to stETH as a shared liquidity layer.Get started building with Lido V3 today: https://lido.fi/stvaults?mtm_campaign=epicenterBlock Space Forum: https://blockspace.forum/NEAR AI Cloud now lets developers deploy OpenClaw—the rapidly growing open-source AI agent platform—inside Trusted Execution Environments, providing hardware-level encryption with cryptographic attestations. With OpenClaw on NEAR AI Cloud, you can run agents with cloud convenience, but without traditional cloud data exposure. No hardware to manage. No trust assumptions required. Learn more at near.ai.

In an episode that was a long-time coming, Darren hosts UX veteran, Dr. Nick Fine — a London-based digital psychologist and user-experience researcher with more than 20 years of experience. In this Talkin' Shop session, the duo covers such topics as the current state of UX, the importance of fundamentals, the strengths and risks of AI, critical thinking and "healthy cynicism," designing agentic experiences, and much more. Check out this exciting and enriching episode.#ux#podcasts#cxofmradio#cxofm#realuxtalk#worldofux#worldouxDon't forget to like, subscribe, and share!Bookmark the new World of UX website at https://www.theworldofux.com.Visit the UX Uncensored blog at https://uxuncensored.medium.com.Get your specialized UX merchandise at https://www.kaizentees.com.

Greg Heinz, Senior Director of Brand and Digital Strategy at Optimizely, led the company's rebrand — and he argues AI should amplify creative work rather than automate it away. Recorded live at Opticon 2026 in New York City.A rebrand is repositioning, not a new logo. Heinz walks through the research behind Optimizely's brand evolution, the market signals that made the case internally, and what the company had to give up to make the new promise credible.The line between amplification and authorship gets drawn on purpose. "AI" covers everything from a tool that speeds up a designer to a system that ships work with nobody in the authoring seat. Heinz on where he insisted a person stay in the seat, and why.A brand is proven by behavior, not messaging. What had to change in product decisions, sales conversations, and the digital experience for the new positioning to be true — and what keeps the brand recognizable as one company as the volume of expression goes up.Greg Heinz and Greg Kihlström also get into what changes when the first thing evaluating your brand is an AI system researching on a buyer's behalf rather than a person.About Greg HeinzGreg is a marketing manager with over fifteen years of professional experience in B2B software solutions and services, Healthcare, Sports and Entertainment. My goal is to help organizations achieve rapid growth through brand development and demand generation. I am a strong believer in creating amazing experiences and lasting impressions through UX design and storytelling.Greg Heinz on LinkedIn: https://www.linkedin.com/in/gjheinz/---------- Resources ----------Optimizely: www.optimizely.comThe Agile Brand podcast is brought to you by TEKsystems. Learn more here: https://aglbrnd.co/r/2868abd8085a9703We're proud to be a media partner for #MAICON26 - Oct. 13-15! Learn how AI can power your marketing and business and help you grow smarter. Use code AGILE150 to save! https://aglbrnd.co/r/7fe458ced0f04658Reach your customers with Reddit. Spend $500 in ad spend, get $500 back in ad credit! Learn more: https://advertalize.com/r/491818c79fb1873fChaser is the only Slack-native project management platform that helps teams turn messages into tracked tasks, automate follow-ups, and maintain team-wide visibility, without adopting another tool. Now integrated with Claude and other GenAI tools. Learn more at trychaser.com and use code AGILEBRAND for a 3-month free trial (normal trial is 14 days).The most influential minds in software, AI, and engineering leadership will be at WeAreDevelopers World Congress North America, September 23-25 in San Jose. Learn more: https://aglbrnd.co/r/60a7299222a7bcf1Start building your own apps with Replit and get $20 off. Learn more: https://aglbrnd.co/r/93531742a7625a20Enjoyed the show? Tell us more at and give us a rating so others can find the show at: https://aglbrnd.co/r/faaed112fc9887f3Connect with Greg on LinkedIn: https://www.linkedin.com/in/gregkihlstromDon't miss a thing: get the latest episodes, sign up for our newsletter and more: https://aglbrnd.co/r/35ded3ccfb6716baCheck out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology: https://www.agilebrandguide.comThe Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. https://www.missinglink.company Hosted on Acast. See acast.com/privacy for more information.

OpenAI's AI agents already had the exam answers. So why did they hack Hugging Face anyway? Kain, Tay, and Austin Griffith explain. ======================================================== Thank you to our sponsors! Visit 1inch to swap tokenized securities, crypto and more. Simple. Secure. Self-custodial. Whatever asset you're buying - swap it at ⁠⁠http://unchainedcrypto.com/go/1inch-sn⁠⁠ ======================================================== OpenAI's AI agents didn't just get caught cheating on a security test. According to the postmortem, they already had the answers, and hacked Hugging Face's systems not to cheat, but to learn who was scoring them and cover their tracks. Kain Warwick and Taylor Monahan bring on Austin Griffith, Builder Enablement at the Ethereum Foundation, to work through what that cover-up actually means, and why Griffith thinks Nick Bostrom's twenty-year-old paperclip thought experiment stopped being hypothetical the moment agents started writing production-grade code. They also cover the tokenized HIMS stock pump, Rune's fake $100 million NASDAQ LARP, Kyle Samani's abrupt exit from Multicoin, and the Cronos validators who rolled back a hack. If agents can trick each other to avoid detection, what happens once they're running your portfolio, or your toaster? Hosts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Kain Warwick⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - Host of Uneasy Money and Founder of Infinex and Synthetix ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Taylor Monahan⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - Co-host of Uneasy Money and Security Expert Guest: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Austin Griffith - Builder Enablement at the Ethereum Foundation and Founder of BuidlGuidl Timestamps