Podcasts about Figma

Daniel Mahncke and Shawn O'Malley take a deep dive into Wix.com — the Israeli website-building platform whose investment case now turns on two of the most debated questions in the stock today: whether the generative-AI wave that lets anyone spin up a site from a text prompt is the end of Wix or whether Wix is too sticky, and whether the Base 44 acquisition — Wix's bet on AI-powered app generation — is the next leg of the story or a distraction from the SMB infrastructure business the company already dominates. IN THIS EPISODE YOU'LL LEARN: (00:00:00) Intro (00:01:32) How Wix was founded (00:21:35) Why clients keep using Wix (00:28:05) How much of WIX is actually vulnerable to AI (00:37:07) Why Wix is more sticky than it seems (00:38:24) Whether vibecoding is likely to disrupt drag-and-drop website building (00:46:54) Why Base44 could change the entire investment case (01:06:24) How Wix could survive and turn into a multibagger (01:09:21) Valuation discussion of Wix (01:13:26) Whether Shawn and Daniel add Wix to the Intrinsic Value Portfolio BOOKS AND RESOURCES Join the exclusive ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Mastermind Community⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Track ⁠⁠⁠⁠The Intrinsic Value Portfolio⁠⁠⁠⁠. Portfolio Review Submit Tool. Value Investor Club Article. Chit Chat Stocks w/ Manuel Cunha. Future Investing Interview w/ Manuel Cunha. Rene Sellman Substack Article. Manuel Cunha Substack Article. Previous Intrinsic Value breakdowns: Figma, Microsoft, Salesforce, Adobe. Follow Shawn on ⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠. Follow Daniel on ⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠. Related ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠books⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ mentioned in the podcast. Ad-free episodes on our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Premium Feed⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. NEW TO THE SHOW? Get smarter about valuing businesses through ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Intrinsic Value Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Check out ⁠⁠⁠⁠⁠⁠⁠⁠The Investor's Podcast Starter Packs⁠⁠⁠⁠⁠⁠⁠⁠. Follow our official social media accounts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Facebook⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Try our tool for picking stock winners and managing our portfolios: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TIP Finance⁠⁠⁠⁠⁠⁠. Enjoy exclusive perks from our ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠favorite Apps and Services⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Learn how to better start, manage, and grow your business with the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠best business podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. SPONSORS Support our free podcast by supporting our ⁠sponsors⁠: Plus500 Netsuite Shopify Vanta References to any third-party products, services, or advertisers do not constitute endorsements, and The Investor's Podcast Network is not responsible for any claims made by them. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://theinvestorspodcastnetwork.supportingcast.fm

DAMIONCarnival Corporation's data breach exposed personal data of nearly 6 million customers: An April social engineering attack on an employee account compromised names, dates of birth, and government-issued ID numbers. WHO DO YOU BLAMESkills: Technology & Cybersecurity: Experience with information technology and cybersecurity matters is increasingly important to mitigate the risks our business faces, promote innovation and maintain a competitive edge in a rapidly evolving technological ageLeast represented 5/11CEO Josh WeinsteinNO: at Carnival since 2002, started as General CounselSir Johathon BandNO: First Sea Lord and Chief of Naval Staff, the most senior officer position in the British Navy (2006 to 2009, when he retired); Admiral and Commander-in-Chief Fleet (2002 to 2006); Served as a naval officer in increasing positions of authority (1967 to 2002)Jason CahillyNO: CEO Dragon Group LLC, provides capital and business management consulting and advisory services worldwide; The NBA: CFO & Chief Strategic Officer; Goldman Sachs: Partner; Global Co-Head of Media and Telecommunications; Head of Principal Investing for Technology, Media & TelecommunicationsNelda ConnorsNO: CEO/Chair Pine Grove Holdings, a privately held investment company; CEO Atkore International, manufacturer of electrical, safety and infrastructure solutions; VP Eaton Corporation, electrical and automotive supplierLaura WeilNO: Founder Village Lane Advisory LLC, specializes in providing executive and strategic consulting services to retailers COO New York & Company, women's apparel and accessories retailer; CEO Ashley Stewart, women's apparel retailer; CEO Urban Brands, apparel retailer; COO AnnTaylor Stores, women's apparel retailer; CFO American Eagle Outfitters, apparel retailerAudit Committee: Oversee management's risk assessment processes to identify principal and emerging risks, including financial, IT, cybersecurity and non-HESS operational risksLaura Weil*: NOJason Cahilly: NOJeffrey Gearhart: NOWalmart Corporate Secretary and lawyerStuart Subotnick: NOCEO at Metromedia Company, wireless/communications, until 2010; Carnival director since 1987 Health, Environmental, Safety and Security Committee: Oversee management's processes to identify principal and emerging health, environmental, safety, security and sustainability-related risks, including those related to ship operations and cybersecurity, RAAS health, environmental, safety, security audits, IAG and external investigations into significant ship incidents, and health, environmental, safety, security-related hotline complaints, and assess the steps management has taken to minimize such risks.Sir Johathon Band*: NONelda Connors: NOHelen Deeble: NOFormer CEO P&O Ferries Division Holdings, shipping and logistics businessKatie Lahey: NOExecutive Chair Korn Ferry Australasia, leadership and talent firmMicky Arison (75%): Exec Chair and former CEO and 7% stockholderThe CEO Pay Ratio1,063:124 retail CEOs made as much in a day as their typical employee earned in a year — and a big one didn't. WHO DO YOU BLAMEThe separation of CEO and Chair: Hamilton E. James Chair/Ron Vachris MMNot uniqueOnly 50% of the board is men. WTF?uniqueOne share = one voteNot uniqueState of HQ = WashingtonAlso StarbucksState of Inc = WashingtonAlso StarbucksPledge of allegiance to stakeholdersCostco generally has: Higher wages; Better benefits; Lower turnover; Higher sales per employee.Industry-leading employee compensation AND Self-imposed low-margin pricing philosophyWalmart only low-margin pricingOther comps:Todd Vasos of Dollar General, Shane O'Kelly of AutoZone, Gerald Morgan of Texas Roadhouse, Jack Sinclair of Sprouts Farmers Market, William Stengel of Genuine Parts Company, Michael Creedon of Dollar Tree, Ronald Sargent of Kroger, Lauren Hobart of Dick's Sporting Goods, Joshua Kobza of Restaurant Brands Inc., Kecia Steelman of Ulta Beauty, Scott Boatwright of Chipotle, Ted Decker of Home Depot, Bob Eddy of BJ's Wholesale Club, Corie Barry of Best Buy, James Conroy of Ross Stores, Chris Turner and David Gibbs of Yum Brands, Chris Kempczinski of McDonald's, Marvin Ellison of Lowe's, Brian Cornell of Target, Ernie Herrman of TJX Companies, Doug McMillon of Walmart, Brian Niccol of Starbucks, Hal Lawton of Tractor Supply Co, Laura Alber of Williams-SonomaFigma Gets an Activist Investor. Exhibit A on Why Companies Don't Want to Go Public. Figma's first year as a public company hasn't gone well. Findell Capital Management said it needs to take steps to shed its unwarranted reputation as an artificial-intelligence “loser.” WHO DO YOU BLAME?Figma founder and CEO Dylan Field: Owns 10% of shares but 72% of voting power: Class B shares worth 15 votes per shareDylan owns 158 Class A Shares (or 0.00003556% of 444,278,887)And Chair$5B net worth$865M total summary compensation in 2025; $91M in 2024Nominating Agreement:Figma must nominate Dylan Field to be a director and include him in the proxy statementThe company must use its resources to back him up and actively convince other shareholders to vote for him In response to a question about how he was going to change the world, Dylan said he was going to build better software for drones.Bro fest sausage party2 of 9 directors are womenTop 5 NEOs all dudesPeter ThielForced Dylan to drop out of Brown for a dumb fellowshipVC Blowhardiness on the BoardVC dude John Lilly (Greylock): Lead Independent Director2nd longest tenure (2014)Member of the Audit Committee; Member of the Nominating Committee (only Lilly and Rimer)VC dude Andrew Reed (Sequoia)Director at debt-maker Klarna Group (also way down since IPO): down roughly 54% from its initial $40.00 IPO price, and down nearly 68% from its all-time highMember of the Compensation Committee (which modeled Dylan's pay package after Elon Musk)VC dude Danny Rimer (Index Ventures)Director since 2014B.A. in History and Literature from HarvardMember of the Compensation Committee (which modeled Dylan's pay package after Elon Musk)Member of the Nominating Committee (only Lilly and Rimer)Luis von AhnDuolingo co-founder and CEO2025: shared an internal email outlining Duolingo's new "AI-first" strategy where Duolingo would “gradually stop using contractors to do work that AI can handle”Stated that "AI is a better teacher than humans" and that the future role of teachers would be reduced to providing "childcare."Blamed the controversy on a "lack of context" in his original statements"AI-First" memo goes viral: $389; today $118MATTDanone, Starbucks shine in methane-reduction rankingDanone is the only company in the group aligned with the Global Methane Pledge, an initiative backed by 150 countries that targets a 30 percent reduction in global levels of the gas by 2030. The French multinational also leads the pack in progress toward its target, having come close to hitting it five years ahead of schedule.WHO DO YOU CREDIT?Chair of the CSR committee Lise Kingo (9% influence), one of three directors tagged as merit directorsmaster's degree in Responsibility & Business from the University of Bathbachelor degrees in Religions and Ancient Greek Artbachelor's degree in Marketing and Economicscertificate as International Director from INSEADEx Novo Nordisk environmental affairs, internal audit, compliance, human resources, communication, branding and sustainabilityHelped create the UN SDGs and the UN Global CompactSomehow only bats 559 on carbon intensity (career) and 415 for scope 1/2 (career)Also, using deference metrics, the ONLY DIRECTOR tagged as fully independentEmployee rep member of the CSR committee Bettina Theissig (5% influence) and the employees of DanoneThe committee charter mandates employees get a say: At least two thirds of the CSR Committee must be independent, as defined by the AFEP-MEDEF Code. At least one Director representing employees must be a member of the Committee.In France (Danone's domicile), the European Investment Bank found that French employees were the most aware of environmental issues - 82% of French employees said they were highly concerned about environmental issues, highest in EuropeLead Independent Director and chair of the Nom/comp committee who put together the comp plan, Valerie Chapoulaud-Floquet15% influence, second to the 18% influence CEO (democracy!!), got 99.16% shareholder approval in April (even as CEO got 89.73% approval and pay got 93.19% approval)20% of short-term pay and 30% of long-term pay is based on hitting sustainability targetsWhen you pay a CEO to do a thing, they are more likely to do a thingEx-CEO Emmanuel FaberOusted in 2021 by the board of directors and activist investors, he transformed Danone into an “enterprise a mission” (a French version of a B corp)Investors voted 99% in favor of the move and a year later ousted Faber, the board resigned, and the new board and CEO are basically moving back towards being environmental leaders because it paid offShort term share price laggedHe said in 2024 that nature is “at the core” of Danone, It took the stock 3 years from Faber's ousting to return to Faber levels - and in the meantime, they were sued for plastics and emissionsIsn't this HIS win?Current CEO Antoine de Saint-AffriqueBecause CEOGM Board Director Jonathan McNeill Stepping DownCEO of DVx Ventures. Ex COO at Lyft Inc. and ex president, Global Sales, Delivery and Service at Tesla, current director at Lululemon, GM director since 2022, on the Governance and Corporate Responsibility committee and Risk and Cybersecurity committee.We know that half of boards on average think someone on the board should be replaced - did the GM board not like McNeill?WHO/WHAT WOULD WE BLAME FOR PUSHING MCNEILL OUT?Outsider dude bro DRLet's be honest, McNeill worked at much more… modern?... companies than GMThe board is OLD SCHOOL - ex Northrop Grumman, ex Visa, ex Lazard, ex HP, ex eBay, ex Novartis, ex Walmart, other directorships at Goldman, Huntsman, P&G… these are professional, insular boardsMeanwhile, he's investing as a VC in AI, other auto/mobility startups, comes from boards that are bro founder lead (Tesla, Lyft) He's invested in AI, crypto, heavy tech, intertwined with VCs all overNot deferential enoughBarra is connected to 94% - THE ENTIRE - boardMcNeill has the highest network power on the board at $9tn, higher than even Mary Barra (who is super connected), but is NOT a power player in the board community of GM - the dominant board communities for GM are massive blue chip US companies, where McNeill has deeper connections in smaller IT/tech focused companiesHe doesn't need the pay, he gets nothing for the connections really, he has connection to Barra but his network is different - was he too independent?Pissed he doesn't have enough influence McNeill has the LOWEST influence on the GM board at 4%He's relatively new, younger, working as a VC where you have a lot of power of capital allocation“I don't need this shit” effect?Too many womenMcNeill's dvX ventures portfolio team is 6 dudes and 1 womendvX entire operations staff is two woman - guess what they do“Chief of Staff” (ie, HR)Executive Assistant (yes, listed on the team)Board is 2 women, 3 men (McNeill not on board)This one seems unlikely I guess?Too busy, meh, move onOne of dvX portfolio companies is curbee, with GM Ventures' Kurt Baumgarten on the board (and the dvX co-founder is founder of Curbee)McNeill on at least 3 of his portfolio boards or advisory committees, plus LULU and GM…

n this special segment of The Full Ratchet, the following Investors are featured: David Ulevitch of Andreessen Horowitz Jake Saper of Emergence Capital Sandesh Patnam of Premji Invest Each investor highlights a situation where they decided not to invest, why they passed, and how it played out. The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

In this episode, we debrief Telehash #4 and dig into the open-source future of Bitcoin mining. We share behind-the-scenes metrics from HydraPool's six-and-a-half–hour live stress test, including 30.8 zettahashes processed, an average of 1.32 EH/s, a peak of 2.495 EH/s, 2,231 workers, 59 unique users, and an impressively low ~1% server CPU under >2,000 connections. We explain why rejection rates under ~2% matter, how stale and “difficulty too low” shares differ in solo vs pooled mining, and how Stratum “suggest difficulty,” plus our d= and h= password parameters, help right-size starting difficulty—making Telehash inclusive for both exahash renters and single-chip Bitaxe miners. We also touch on leaderboards, loyalty uptime rules, and shout out supporters like Elektron Energy, Compass, Saaz Mining, and Abundant Minds. From hardware to policy, we discuss Bitaxe UX updates (LVGL, Figma-driven UI, external display/knob), DOOMAXE fun, and industry standardization—from firmware and pools to racks, cooling, and power—arguing that open reference designs cut costs and risk for everyone. We cover GridPool's “winners list” approach to decentralized variance smoothing, the Patoshi/extra nonce story, vardiff dynamics, and privacy-conscious VPN mining. We reflect on immersion's decline versus hydro, ASIC roadmap realities and slowing efficiency gains, the supply-chain and security stakes (FCC Wi‑Fi moves, vendor backdoors), and why nonprofit coordination via the 256 Foundation matters for open firmware, dev kits, and reference designs. We close with community invites, next steps for Telehash #5, and a call for ASIC makers and big miners to collaborate on open standards that benefit small and large operators alike.

Google I/O 2026 happened. And a few things they announced will actually change how you work as a designer. In this episode I break down the 7 that matter most — no fluff, just the stuff that's relevant for UX and product designers right now.  IN THIS EPISODE → Google Stitch — the free AI design tool that made Figma's stock drop 9% in one day→ Material 3 Expressive — Android's biggest redesign in a decade, now production default→ Google Pics — finally an AI image tool where you can change one thing without regenerating everything→ Google Flow + Veo 3 — AI video that's actually useful for design content→ Android XR Glasses — shipping this fall, and the design conventions are still being written→ NotebookLM + Workspace Studio — automated UX research workflows that actually work→ Agentic UX — why designing for screens is no longer enough  Resources:Google Stitch → stitch.withgoogle.comMaterial 3 Figma Kit → figma.com/community/file/1035203688168086460Google Flow + Veo 3 → labs.google/fx/tools/flowVeo 3 in AI Studio → aistudio.google.com/models/veo-3Android XR Design Docs → developer.android.com/design/ui/xrNotebookLM → notebooklm.google.comWorkspace Studio → studio.workspace.google.comAll I/O 2026 announcements → blog.google/innovation-and-ai/technology/developers-tools/google-io-2026-collection/AI for Designers: 5-week Bootcamp

Web funnels are like teenage hex.Everyone claims they are crushing it. Almost nobody really has a clue.Elise Zareie spent last year actually building them.She has been in UA since 2019. Last year she became a part-time product manager just to ship funnels herself. That meant learning Figma, coordinating designers, front-end developers, back-end developers, and analytics teams. Running QA. Shipping it. Then using AI to test faster than she ever could before.In this episode she talks through what the process actually involves, the three levers that move the needle in any funnel, the one benchmark she watches obsessively on landing pages, how she uses Claude to generate full funnel copy from screenshots of top-performing creatives, and why AI visuals are making consumers more suspicious, not less.Key topicsWhy web funnels are harder to build than most people think and what the process actually looks likeHow to identify the dominant funnel in a vertical before building anythingThe 40% page-one to page-two benchmark and what to do when you fall below itHow to match landing page copy to ad creative using UTM tagsHow Elise uses Claude to generate funnel copy and assessment questions from top-performing creativesHow AI helped her launch a male-specific funnel in two weeks for an app with 80% female usersWhy AI-generated visuals are creating consumer suspicion and what to do about it

Take the 2026 AI Engineering Survey and get >$2k in credits and AIE WF tickets!This was recorded before Railway suffered a major GCP outage on May 19, despite being a multi-AZ, multi-zone mesh ring, with HA fiber interconnects between their Metal GCP AWS, because workload discoverability was unintentionally still tied to GCP. All has been resolved with a post-mortem.Railway did not start as an AI infrastructure company.It was founded in 2020 years before agents became the default way people thought about deploying software. Jake Cooper, formerly at Bloomberg and Uber, started Railway with a simple obsession: the activation energy to ship something to production should be near zero. Push code, get a URL, iterate. No Docker files, no Kubernetes manifests, no Ansible scripts stacked on Ansible scripts.For years, this was a slow grind. Railway spent its first 18 months hand-acquiring its first 100 users with Jake personally greeting every Discord signup on a second monitor.Today, Railway has raised $124m and is growing very fast. A 35-person team supports 3 million users, adding roughly 100,000 signups a week. Their bare metal data centers have a 3-month payback period vs. renting in the cloud, with 70% margins funding aggressive cloud bursting when needed. The servers they own have actually appreciated in value as RAM prices have climbed basically meaning the value of their hardware now exceeds the capital they've raised.From rebuilding Railway's network overlay over a weekend to moving the vast majority of workloads onto its own bare metal data centers, Jake Cooper is trying to build a new cloud for an agent-native world. In this episode, Railway's founder and “conductor” joins swyx and Alessio to unpack why the next era of software infrastructure is not just “Heroku but newer,” what agents need that humans did not, and why the old deployment loop of Git, PRs, CI/CD, and static cloud resources may be heading for a rewrite.We go deep on Railway's infrastructure stack: own-metal data centers, three-month cloud payback periods, cloud bursting, data center debt, Railpack, Nixpacks, Temporal, feature flags, Central Station, content-addressable filesystems, agent-safe production forks, and why the CLI may become more important than the canvas in an agent world. Jake also shares the founder journey behind Railway, how the company survived losing $500K/month, why it now serves millions of users with only 35 people, and why he believes the pull request is dying.We discuss:* How Railway went from a slow six-year grind to adding 100,000 users a week* How Railway thinks about agents as the next dominant software species* Why agents need version control, observability, compute, storage, and orchestration at 1000x scale* The economics of Railway's own-metal data centers and three-month payback* How Railway uses cloud bursting while scaling its own infrastructure* Why data center debt can be a better tool than venture debt for infra startups* Central Station, Railway's internal system for clustering customer feedback and incidents* Why responsible disclosure and over-communication matter for platforms* Why feature flags, progressive rollouts, and shadow traffic are essential for agents* Temporal's strengths, pain points, and why workflows matter for agents* Railpack, Nixpacks, Nix, and lazy-loaded content-addressable filesystems* Why “cattle, not pets” may change if you can clone the pets* Why Railway is building a new cloud from scratch instead of copying hyperscalers* The solo founder path, focus, writing, and how Jake thinks about company buildingRailway:* Website: https://railway.com/* X: https://x.com/RailwayJake Cooper:* LinkedIn: https://www.linkedin.com/in/thejakecooper/* X: https://x.com/JustJakeTimestamps00:00:00 Introduction: What Is Railway?00:02:07 Jake's Path to Railway00:06:13 Railway's Six-Year Growth Story00:08:52 Rebuilding the Business After the Free Tier00:11:17 Agents as the Next Software Platform00:13:29 Railway's Infrastructure Philosophy00:15:42 Bare Metal, Cloud Economics, and the Compute Crunch00:17:22 Cloud Bursting and Five-Cloud Networking00:20:20 Data Center Debt and Infra Financing00:23:31 Data Centers in Space00:25:24 What Agents Need From Infrastructure00:28:24 CLIs, Canvas, and Agent-Native UX00:35:15 Central Station, Incidents, and Responsible Disclosure00:40:30 Safe Rollouts, SRE Agents, and Production Forks00:45:00 AI SRE, Specs, Code, and Tests00:48:24 Self-Replicating Infrastructure and the New Serverless00:53:18 Heroku, Temporal, and Workflow Engines01:04:07 Railpack, Nixpacks, and Lazy-Loaded Filesystems01:06:01 Coding Agents, Token Spend, and Roadmap Acceleration01:10:56 The Pull Request Is Dying01:12:28 Feature Flags and the Agent-Era SDLC01:16:15 Cattle, Pets, and Cloning Machines01:19:29 Solo Founder Lessons01:24:12 Focus, GPUs, and Building a New Cloud01:28:20 Closing ThoughtsTranscriptAlessio [00:00:00]: Hey, everyone. Welcome to the Latent Space Podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space.Swyx [00:00:10]: Hey, hey, hey. Today we're in the studio with Jake Cooper of Railway.Alessio [00:00:14]: Conductor of Railway.Swyx [00:00:15]: Conductor at Railway. Yeah.Alessio [00:00:16]: Choo-choo.Swyx [00:00:17]: Do you actually have that anywhere, like on your business card?Jake [00:00:20]: We call some of our volunteer moderators conductors. I don't have a business card. We're not that big yet. At some point I will. I got handed a nice business card from the Supermicro folks, and I was like, “Damn, this is pretty official.”Swyx [00:00:30]: Business cards are coming back.Jake [00:00:32]: They're cool. They're hip. The conductor thing is good. We're trying to figure out what we want to call each other internally. Some people think it's super cringe and say, “You don't need a name for people internally.” Some people want to call each other something. We still don't have a really good one.Jake [00:00:55]: We've got New Railcrews, Trainiacs. Nothing has stuck yet.Swyx [00:01:00]: I like Trainiac. Trainiac sounds good. Railwayians. For those who don't know, what is Railway? Let's give people a crisp definition up front.Jake [00:01:09]: Railway is the easiest way to ship anything. You go to the canvas, or you talk with Claude, and you say, “Deploy a Postgres instance, deploy my GitHub repository, run this code,” and you're off to the races.Swyx [00:01:22]: You've got a nice animation on the landing page.Jake [00:01:24]: Thank you. None of my work, by the way. They don't let me touch the design stuff anymore.Jake [00:01:25]: We want to make it trivially easy not just to deploy things, but to evolve applications over time. Most tooling right now stacks entropy on top of entropy: Docker, Kubernetes, Ansible scripts, and all these other things. If we can version all of your software and keep track of all the changes, then we can make it trivial to clone environments, fork into a parallel universe, get copies of production data, get copies of any services, make changes, validate them, and collapse them back in without reproducing everything across a staging environment.The Railway Origin Story: From Uber Systems to a New CloudSwyx [00:02:07]: I was looking at your background: Bloomberg, Uber. Nothing immediately stands out as, “This guy is going to found the next great platform as a service.” What prepared you for Railway?Jake [00:02:21]: It was curiosity to keep going deeper. I started out on front-end stuff, working on Wolfram Mathematica and porting it over. Then I briefly moved to Bloomberg, then toward Uber and distributed systems, taking the Jump Bikes systems and moving them to a distributed system built on top of Cadence, the pre-Temporal Temporal.Swyx [00:02:44]: Which, by the way, I'm happy to talk about, pros and cons.Jake [00:02:48]: Totally.Swyx [00:02:51]: But let's do the Railway story.Jake [00:02:52]: It has been a continual step of wanting an experience. Whether it's walking up to a bike, unlocking it, and having it work frictionlessly, or something else, the depth required to make that happen follows from the experience. A lot of the work I do, and a lot of the team does, is in service of that experience. We fundamentally don't care how deep we have to go. We will swim to the bottom of the swimming pool to get the experience.Jake [00:03:17]: I don't have a physics PhD. I did an EECS degree. It has always been about figuring out the next step: how do we get there? That's what led to starting Railway for that experience and then moving all the way to bare metal data centers. I was adding patches to the kernel this week to get the experience there because I can see how much better it can be.Swyx [00:03:49]: Other patches to the Linux kernel this week?Jake [00:03:51]: Yeah. Not upstream. Our fork.Swyx [00:03:52]: That's a flex. Railpack? No, this is different. This is the OS on top of Railpack?Jake [00:03:57]: No, this is an actual kernel patch. It's always literally: what do we have to do to get that experience? Then figure it out. Anything is figureoutable.Swyx [00:04:10]: Would you send the patch upstream, or does it not fit other use cases?Jake [00:04:13]: Maybe. We have to work out the experience internally. It has to do with the storage layer we're building for some of the agentic stuff. Maybe it'll be useful upstream, but it's deeply useful for us internally.Open Source, Forks, and Non-Deterministic VersioningSwyx [00:04:29]: You mentioned open source before. How do you think about starting from open source, and then coding agents letting you do a lot more from forks of it?Jake [00:04:38]: GitHub's original sin is that it's almost a series of broken pointers. You have this thing, then you clone it, and now you've lost the whole upstream. How do we make it trivial for people to modify really small pieces of it?Jake [00:04:51]: We think of Git in a discrete sense: I've either made a change and merged upstream, or I haven't. What would it look like if it were percentage-based, a little more non-deterministic, or a stream of changes that users traverse as a percentage rolled out in general and then rolled all the way up?Jake [00:05:13]: We have the open-source kickback program and let you deploy templates because we want to make it trivial for people to version these shards over time. It solves a large problem around authentication, authorization, and security. NPM has a way to define, “Don't take any new packages.” The ideal end state is that you roll out progressively to users with the minimum impact zone and continue rolling up. JPMorgan should probably be the last one on the patch line, for all our sakes, because our money and livelihoods are there.Jake [00:05:53]: It's okay if Johnny Vibe Coder gets a broken patch because there's so much entropy in the system that the rubber has to meet the road at some point. You have to test at varying levels.The Long Grind: First Users, Free Tier, and Making the Business WorkSwyx [00:06:13]: I wanted to pull up this glorious chart, which is your usage or number of daily signups?Jake [00:06:22]: Daily signups, I think.Swyx [00:06:24]: You started six years ago. It was a slow grind, and now you're on a rocket ship. You say, “Don't doubt your fight and don't quit.” Maybe pick out certain points that were key inflections for the company.Jake [00:06:40]: At the start, it's about getting your first 100 users, hell or high water. We had a website and a support link. The support link was the Discord channel. I had notifications on with two monitors: the monitor I was working on and the other monitor with Discord. If anybody came in, I was immediately like, “Hey, how's it going?” It was rare, so getting those first 100 users to come back was the start.Jake [00:07:14]: Then you build a consultancy factory because users want all these things. You have to go back to the board and ask, “What is the actual product offering I want to build on top of this?”Jake [00:07:28]: VCs want charts that always go up and to the right, but in reality you don't necessarily want charts that look like that. For us, there have been periods of expansion where we add features to test use cases, and periods of compaction where we ask, “If the experience we have is good, how do we make it significantly better?” Maybe we strip out features that don't fit our ICP anymore.Jake [00:07:57]: The boom from 2022 to 2023 came from the free tier. Everybody under the sun was using it.Swyx [00:08:09]: A lot of Reddit bots and Discord bots.Jake [00:08:12]: And crypto miners. When you build an open product on the internet where anybody can sign up, the internet is a horrible place with so many things. You go through periods of asking, “How do I reach as many people as possible?” Then, “How do I fit the exact use case for the people who really matter and are really excited about this specific thing?”Jake [00:08:39]: Then there was a two-year period of making the actual business work. During the free-tier era, we were losing about half a million dollars a month.Swyx [00:08:59]: On a $20 million bank account.Jake [00:09:02]: On a $20 million bank account with maybe $50,000 a month in revenue. That's a horrible business. I don't know how anybody invested. But you have to go through it and say, “We have an experience people love, but the business has to work.”Jake [00:09:17]: There are two schools of thought. You can run the horrible business all the way up with bad margins, or you can go back and make it work. We've always wanted a super lean team. We're 35 people right now. It's very small.Swyx [00:09:36]: Supporting three million already?Jake [00:09:38]: Yeah. We're adding 100,000 users a week right now, so it's growing fast. We don't want to add headcount for the sake of headcount or throw bodies at problems. We want to build systems. It's hard to build systems during expansion because you're adding things to the system because people are asking for them or things are breaking.Jake [00:10:00]: We had to cut off the free users for a little while, rebuild the business, and make sure it worked. We want to reach as many people as possible because software is important. It's become difficult to create things in the physical world, so it's important to make it easy for people to build in the virtual world and have access to creation. But there are legs to that journey.Jake [00:10:30]: You can see divots in the charts. If you follow between 2025 and 2026, it's either summer or winter. People go on holiday with family.Swyx [00:10:50]: It affects that much?Jake [00:10:51]: Yeah. It's kind of B2C and kind of B2B. People are shipping constantly, then they stop. Our activation curve now shows more people activating on weekdays because we have more business users, so it smooths out over time.Agents as the New Interface to DeploymentSwyx [00:11:17]: Was there a point where you started prioritizing AI development or agent development?Jake [00:11:24]: We've prioritized agentic as a top-of-funnel thing. Over the last six months, we've deeply prioritized agentic as a mechanism to build and deploy things because we believe the curve is so steep and that is how people will build and deploy software.Jake [00:11:42]: It almost fundamentally doesn't matter whether this is dot-com or not because we're all on the internet anyway. If agents are going to deploy a bunch of things and we hit an inference wall at some point, we'll fix those problems. The dominant species over the next 10 years is that we've moved from assembly to C to C++ to JavaScript to words. You're going to need to close that loop.Swyx [00:12:13]: When you say this is dot-com, did you mean buying the domain, or the general case?Jake [00:12:17]: I mean the dot-com era, when companies had a huge run-up because people understood the internet was important. Then they hit bottlenecks, fundamental laws of physics, math didn't work, and everybody came back down to earth. But it didn't matter because the internet became so impactful. If you operate on a long enough time horizon, you should build these things anyway because you can see where it's going.Jake [00:12:45]: That's where I think a lot of agent stuff is. You get to a point where you're running thousands of agents in parallel. What is the inference cost? What is the compute cost? How do you make that efficient? How do you coordinate all this? We have issues coordinating humans; we don't even have good tooling for that. Now we have to figure out how to get agents to coordinate, safely version changes, and know when to raise their hand for someone to intervene. Otherwise it becomes an interrupt factory.Railway's Infrastructure Thesis: Network, Compute, Storage, and MetalSwyx [00:13:19]: Let's go right into the technical side. What are the core infrastructure or architectural beliefs of Railway that allow you to do what you do?Jake [00:13:29]: The primitives matter a lot for us. We need network, compute, storage, and orchestration around it. You need control over a lot of those things. We've talked a lot about how we don't really use Kubernetes because we want higher-order control to place workloads in very specific places.Jake [00:13:48]: The reason is that you have to be very efficient with agents: memory reuse and all these other things, or you're going to massively blow up your cost structure. Being able to rack and stack your own servers and build your own metal unlocks performance and cost. Experiences where you're running 1,000 agents in parallel are not massively cost prohibitive.Jake [00:14:13]: Token use and compute use are blowing up. Over time, those things have to get a lot more efficient. You can get a lot of margin to make those experiences solid by building your own metal. That's all in service of offering a differentiated experience to as many people as humanly possible.Swyx [00:14:51]: You have a data center in Singapore.Jake [00:14:53]: Yeah. We have two in every other region now. In Singapore, we're adding a second one in Q3.Swyx [00:14:58]: What's it like? I've never built a data center. Do you go to Equinix and say, “I want some slots?”Jake [00:15:05]: Yeah. Equinix. You basically go and say, “I want power and I want a cage.” They say, “Great, here's what it's going to be.” You rent the cage for a period of time, fill it with racks and servers, and hook up internet to it. That's all the pieces.Swyx [00:15:36]: Then you handle everything else.Jake [00:15:37]: You handle everything else.Swyx [00:15:39]: What's the math versus clouds doing it for you?Jake [00:15:43]: If we rented in the cloud, our payback period when we go to metal is about three months.Swyx [00:15:50]: Which is crazy.Jake [00:15:51]: It's nuts. That's four years of depreciated hardware. You're going to see a lot of this compute crunch because hyperscalers are buying up a lot of stuff. We're working directly with OEMs, resellers, and people building these machines: Supermicro, Dell, and others.Jake [00:16:11]: Upstream, there's a bunch of supply pressure. When we raised our last round, between deploying capital for servers and now, the amount of money we've raised is less than the amount of money we have in the bank plus the value of the servers because the servers have appreciated as RAM has gone up. It's nuts how valuable hardware has become.Jake [00:16:50]: If you look at hyperscalers, they deployed around $80 billion of capital expenditures this year, and next year will be more. That's a massive infrastructure build-out. You look at that and think it's crazy that they're spending way more than the Manhattan Project. But if every person is going to run dozens or hundreds of agents in parallel, you have no conceptual idea how much compute is required to make that experience happen, even if you're deeply efficient and sharing resources. And that doesn't even count inference.Swyx [00:17:22]: How do you plan the build-out? The growth chart is so vertical. Are you usually at 100% utilization as soon as racks are live? How far ahead are you planning?Jake [00:17:33]: We still maintain cloud presence for bursting. We work with AWS, GCP, and a few other clouds. We can rent, and then the moment we get space or power, we compact those workloads off the cloud. We started on the clouds, then built a system to migrate to our own metal. There's nothing that says you can't continually do that again, and that's exactly what we do. We never want to be compute constrained.Jake [00:18:09]: At the start of the year, we actually became compute constrained because one upstream provider wasn't able to give us quota at the rate we needed, and the hardware was slower. I spent a weekend rebuilding our entire network overlay so we could straddle five clouds: Oracle, AWS, ourselves, GCP, and one other one. We can do more than that now.Jake [00:18:38]: We got into a spot where we were trying to pack instances tight because we couldn't get enough compute. That led to a few reliability issues, which are now past us. I made a tweet pointing out that it's becoming harder and harder to acquire compute at the rate these models need to acquire compute. We got bit by it.Swyx [00:19:15]: How do you think about pricing knowing you might not have your own metal available at all times? Are you pricing assuming you need extra margin if you end up going into the cloud?Jake [00:19:26]: Because we've built out our metal data centers, our margins on metal are around 70%. We can deeply subsidize the cloud business if we want to scale at a reasonable rate. We have a few levers: metal, which makes the margins; cloud burst; debt to buy servers; and venture capital. It's an interesting operational problem: how much cash do we have, how much should we raise, how quickly can we deploy it, and can we scale revenue as quickly as we scale compute?Jake [00:20:05]: If we continue making it trivially easy for people to build and deploy, then the faster we close that loop and the more operationally excellent we are with capital, the faster the business can scale. It's almost a straight linear deployment rate.Financing Infrastructure: Hardware Debt, VC, and Operational LeverageSwyx [00:20:20]: I think infra startups raising debt is a tool people don't utilize enough or know enough about. What can you tell us about that? Is it secured against your CPUs?Jake [00:20:32]: It's secured against our hardware.Swyx [00:20:37]: What rates do you get? Who are the lenders?Jake [00:20:39]: We pay prime plus a spread, and we can refinance any of the debt as rates go down. The terms are pretty good. The unfortunate thing is that Twitter has no nuance, so people say, “Venture debt bad.” But as with all things, there are specific tools and areas where you can be deliberate instead of using one tool as a hammer. Venture capital is not the hammer for everything. You have to explore and figure out what works.Swyx [00:21:12]: VC is usually the most expensive financing you can get.Jake [00:21:15]: Yeah. I also think people think about VC incorrectly from a capital-raising perspective. Most people think, “How do I raise as much money as possible from whoever is probably the best I can get at that time?” That's close to right, but what we've tried to do is figure out what unfair advantage we can buy with that equity.Jake [00:21:34]: It's the most expensive equity you're going to give away at that point in time, assuming the company keeps getting better. How do you use it to work with someone stellar who complements you? In the seed stage, I had never started a company. Ray Tonsing had good advice, and I could text him all the time. He was really fast. Awesome.Jake [00:22:01]: Then with John and Erica at Unusual, they said, “You roughly know what you're doing building a product. We'll mostly leave you alone and be available for advice.” Amazing. Then we got to Series A and the business was an operational tire fire because we didn't know how to scale a business. Work with Erica, and Jordan is over at Redpoint, so bonus.Jake [00:22:28]: Now we've raised from TQ and FPV as we're moving into enterprises. Every step of the way, we've asked: who can we partner with at this specific time to unlock the next section of the journey? I don't know enterprise sales. As an engineer, I can eyeball what features we might need, and we have wonderful people internally who can help. But you want boardroom dynamics where everyone is aligned and asking, “How do we win this?” instead of bickering about strategy.Data Centers in Space and the Physics of ComputeSwyx [00:23:31]: You had a tweet about data centers in space. Why no data centers in space?Jake [00:23:37]: It's not “no data centers in space.” My hot take is that I think it is solvable. I've just never seen anybody solve it.Swyx [00:23:49]: You said, “How are you going to dissipate that much heat in a vacuum?” You're making a physics claim.Jake [00:23:55]: I haven't seen anybody prove how you're going to dissipate that much heat in a vacuum. It doesn't mean it's not possible. It just means nobody has brought it up yet.Swyx [00:24:05]: Astrophage.Jake [00:24:06]: I don't know what that is.Swyx [00:24:07]: The Martian thing. Okay, you're very logical.Jake [00:24:09]: It could work. A lot of people are putting the cart before the horse. They say, “We're going to put data centers in space.” Okay, but how? “We have time to figure it out.” It's like in The Martian where they ask how they're going to intercept something and say, “We'll figure it out.”Swyx [00:24:36]: Making a bet on human invention is weird because you blind trust that it can be solved. But with physics, there are first-principles bounds you can put on it. Maybe not. Maybe you're asking to travel time or break a fundamental thermodynamic law.Jake [00:24:57]: I don't know how VCs do this either. How do you know what's not possible and a grift versus what's possible but sounds completely insane? “We're going to put data centers in space.” Coin flip as to which it is, and I guess you'll know in 10 years. That's one cycle.What Agents Need: Versioning, Observability, and 1,000x ScaleSwyx [00:25:23]: Moving back to agents. The branching, fast spin-up, and orchestration you do feels like pre-work that happened to be exactly what agents want. What do agents want differently than humans?Jake [00:25:37]: They want the ability to version things. It's not that different; it materializes slightly differently. Agents want a way to test changes incrementally. Engineers have feature flags. Is there a reason agents can't use feature flags? I don't think so.Jake [00:25:54]: They want version control. Can we use Git or not Git? That one is up in the air. I think something outside Git will emerge for how we version these things over time. They need observability. You need to query what happened, when it happened, which steps failed, traces, logs, metrics, and all the rest. They need network, compute, and storage. They need to write files, save files, iterate on files, and snapshot file systems.Jake [00:26:25]: A lot of what humans needed is in line with what agents need. Branching and forking are not different; we're just moving 1,000 times quicker. It can look like you need something massively different, but what you need is something massively better than what existed. You need orchestration massively better than Kubernetes. You need networking probably better than Envoy. It goes all the way down the stack.Jake [00:26:55]: If the workload profile doesn't change so much as it gets massively compressed because you need thousands of these things, what assumptions change? etcd is going to melt. You need to replace it with something. You can go all the way down the stack and say, “That part has to change, that part has to change, and that part has to change.”Jake [00:27:19]: The interesting thing about the super-exponential curve is that you have to build systems where you can rip out those parts at any time because a new bottleneck might emerge. You get good at parallel agents, and a different part of the system breaks. So it's similar to what humans needed, but at 1,000x scale.Jake [00:27:55]: How do you do code review in the age of agents?Swyx [00:28:00]: You throw more agents at it.Jake [00:28:01]: You don't. But then who reviews for CVEs and all these other things?Swyx [00:28:07]: More agents.Jake [00:28:08]: And that's how we hit the inference wall. You can continually throw agents at the problem, but I think there's a limit to the number of agents you can throw at a problem.CLI, Agent Handles, and Closing the LoopSwyx [00:28:24]: You already had a CLI before it was cool. How is the shape of what you're exposing changing, if at all?Jake [00:28:28]: CLIs have always been cool. The CLI changes because we think about how to give Claude, Codex, ChatGPT, or any model a handhold.Jake [00:28:50]: A CLI is a single command: deploy, get logs, and so on. Things that were prohibitively annoying to humans are not annoying to agents. They're nice. If I handed you a CLI with 40 arguments and 600 flags, you'd think, “I'm never going to use all of this.” But if you hand it to an agent, it says, “This is excellent. I have so many handles to work with.”Jake [00:29:24]: If you're going to expose things to agents that way, you want as many handles as possible where they can get information, query dynamic information, and close the loop quickly. Most problems right now are about how to close the loop as quickly as possible. Where does the agent get stuck, and how can you remove that?Jake [00:29:49]: Telemetry is important. If you can tell where the agent gets stuck from the CLI and say, “12% of people deviate from the happy path because of this, and now I add this argument and drive it down to 2%,” you massively increase the rate of loop closure.Jake [00:30:03]: That's how we think about not just the CLI, but every point in the dashboard. It's a user journey: I hear about Railway. I get something deployed. I get my first green build or aha moment. I see an endpoint, logs, whatever. Then I iterate. The iteration loop is indefinite. The user wants to deploy a new thing, a Postgres instance, change code, and keep iterating.Jake [00:30:36]: If you focus on the iteration loops and what's blocking them from closing quickly, one thing we say internally is: you never want to be waiting on compute anymore. You always want to be waiting on intelligence. If you're waiting on compute, there's a bottleneck that needs to be destroyed because eventually that bottleneck becomes so large that another workflow emerges to change it.Jake [00:31:04]: We've built a product where you push code, build it, and so on. But I fundamentally believe the push-pull loop is going away. We'll get to a point where you make a small change in production, that change is versioned across your infrastructure, you're working alongside copy-on-write versions of your database and infrastructure, and then you merge it in and it's instantaneously live. That's the holy grail of loops. The push-pull-rebuild thing is a point of friction that we're removing entirely.Canvas as Output: Dashboards, Context Anchors, and HyperstructuresSwyx [00:31:43]: It's incredibly fast. If anyone hasn't tried it, that fast feedback is great. My hot take is that Railway was famous for its canvas, which visualizes your infrastructure and lets you manipulate it visually. But that was for humans. For the next phase of growth, Railway CLI is more important than canvas.Jake [00:32:05]: The canvas is funny because it's a mechanism to show changes over time. You're right that previously we used it a lot as an input. Moving forward, its goal is more like an output. You would go to the canvas, make changes, see them, and watch your infrastructure evolve. Now agents have access to the CLI and can make those changes. So the canvas becomes an output: what information does the human need at this moment to make suitable decisions about control requests? Do I approve this or not?Jake [00:32:57]: It also has to be an anchor for your context, a port in the storm. Think of it like layers in a file system. You start with a project, then drill down into services, then into a function or code, because you want to represent the entire thing not just in your head, but in the canvas. Other people can share that representation, think on the same wavelength, and move quickly.Jake [00:33:33]: A lot of organizations get in trouble as they scale because all the context lives in someone's head. “How does this microservice work?” “I have no idea; go ask this person.” Then you have whole categories of products built around context discovery. A lot of that melts away if you have a solid hierarchy and can infinitely nest services, code, context, and everything else all the way down. That's what lets you build these structures over time.Jake [00:34:18]: It's also what lets us build what I've called hyperstructures: things that are way bigger. You look at the Golden Gate Bridge and ask, “How did we build that?” There's a meme that we lost the technology. To some extent, yes, because the coordination that built those things evolved and changed. We lost some of the art of building structure as we jammed everything into Slack.Swyx [00:34:52]: But you jam everything in Discord.Jake [00:34:53]: Same point. It doesn't matter. It's message passing and interrupts, message passing and interrupts.Swyx [00:35:00]: So you're arguing there should be something better and more structured than Slack?Jake [00:35:04]: Yeah. For sure. I think Slack is awful, and Discord is awful too.Central Station: Context Routing, Support, and Incident ClustersSwyx [00:35:09]: This is the equivalent of my mom test. What have you done that has your solution to this?Jake [00:35:15]: Internally, we've built a tool called Central Station that aggregates all the context from our users. Every piece of feedback, every customer support item, everything gets aggregated into clusters. If an incident is brewing, we can determine how many users are affected and break off a discussion based on that.Jake [00:35:40]: That is more helpful than long-running channels where you're trying to decide which channel to put something in. If you can dynamically aggregate information and dynamically route it to the right person based on context, it works better. We know internally that these four people are close to networking. If we see a networking thing, we can drill it down to those four people. If it's with this part, we can look at the commits. This is no longer a manual process internally.Jake [00:36:13]: If you go to station or help.railway.com, that's why we built it. We wanted to scale with a massive amount of leverage by aggregating feedback.Swyx [00:36:27]: This is built in-house?Jake [00:36:28]: Yep.Swyx [00:36:29]: I remember helping out on this one with Angelo in 2023. You scale a lot with a very small team.Jake [00:36:38]: Yeah. We're about 10 times bigger now.Swyx [00:36:40]: You have your full developer code here? Very cool.Jake [00:36:44]: If you go to railway.com/stats, we expose this as a pub-sub-able thing. It's all real-time metrics. There's a way to get it as JSON somewhere if you care.Jake [00:37:01]: We're big on trying to build everything in public and talk about what we're working on. We've had issues in the past, and we'll say, “Here's how we're fixing these things.” We've gotten compliments and flak for incident reports. We're always trying to make them better and talk with people.Incidents, Disclosure, and Progressive RolloutsSwyx [00:37:20]: You had a big one recently. I liked that it was scoped to 3,000. You presumably used Central Station. Talk through what happened and how you address it internally as a team.Jake [00:37:38]: Internally, this one really sucked. It had to do with an upstream provider that didn't do the behavior it said it documented, which is unfortunate given they wrote the RFC for how the behavior should work. We rolled those things out, and Central Station caught it initially when a couple users said caches weren't invalidating. We turned it off immediately.Jake [00:38:03]: When you roll out to a large user base of three million people, you get a lot of disparate behaviors. We tested in staging and had tests, but we hit an edge case. We've hardened those systems, and now we can make that better. But it was a tough one.Swyx [00:38:39]: I always wonder how private disclosure is supposed to work if people find an issue. Are they supposed to contact you first? When you run a platform, these things will happen. What channels should people pursue to quietly resolve it before it becomes a bigger incident?Jake [00:38:59]: There's responsible disclosure. We err on the side of over-disclosing and letting you know something is wrong versus having your provider gaslight you. We've erred on sharing those things more publicly, even if they impact a small subset of users. That's a decision we've made internally. We have four values. One is honor. The honorable thing is to notify people to the widest degree at which they may have been affected or there was an issue, and then confront it head-on: why did it happen, what can we do better?Swyx [00:39:45]: Not the whole user base. That's because of incremental rollouts and other things?Jake [00:39:50]: Yeah. Progressive rollouts.Swyx [00:39:54]: That should be the norm at all large platforms.Jake [00:39:58]: It should. A variety of companies do this. There's the quote that Meta runs 10,000 different versions of Meta. To our earlier point about agents, they need the same thing. They need shadow traffic and all these other things. We've built so much ceremony around production being sacred that we need to make it trivially easy to test different behaviors in a safe environment. Then you can make mistakes in a safe environment.Safe AI SRE: Customer Agents, Forked Environments, and Production ParityAlessio [00:40:30]: Do you see a world where these things get automatically caught, not necessarily by your agent, but by your customer's agent? The cache invalidation issue seems easy to check if you know to look for it.Jake [00:40:44]: It's hard because to determine it, we almost need to hook into your observability infrastructure. That's why we have the template loop on the platform: so you can roll things out progressively. You can roll out to Johnny Vibe Coder initially, or push a shard that someone consumes at their own leisure. Or you can roll it out over weeks: 0.1% of people, 1% of people, early adopters, then all the way up. That's the non-deterministic version control we talked about earlier.Jake [00:41:30]: I believe that's where most things should go, because most companies end up building staged rollout systems in-house. It's the same thing built again and again at every company. There's a massive opportunity to consolidate developer debt.Alessio [00:41:45]: You should have a free tier. Model providers give free tokens if you let them use the data. You could give free compute if someone is the number-one shard that goes out and lets you plug into their observability.Jake [00:41:55]: We do that. That's why we talked about the impact on 3,000 people. We start with lower-impact people. Larger companies on the platform are last to receive those rollouts so they have a version of the platform that's deeply stable.Alessio [00:42:16]: I have three services, so I'm sure I get the first rollout. You can nuke my thing at any time. There are all these SRE agent companies. Observability people also want agents that fix upstream problems. You have your own agent in the canvas now. How do you see that playing out?Jake [00:42:39]: It's the stacking entropy problem. If you don't have primitives to make iteration in production safe, it becomes difficult. If you're an observability provider saying, “Here's the fix to this error,” assume 80% are good and make sense. But in the last 20% long tail of complex issues, if you let somebody stamp it, you create an opportunity for an incident.Jake [00:43:08]: That's why forked environments are important. People have staging, but it always drifts from production. You need primitives, workflows, and experience built first-party on the platform so you can fork any service at any point in time.Jake [00:43:33]: I think of the canvas as a sheet of transparency paper. The agent is a little guy you push up into the canvas. It should say, “I need to copy that service and that service so I can test these two things.” It gets a read-only copy of production. Anything that's PII gets marked as a transform when we clone the database, create a copy-on-write version, or read from it. Then the agent makes changes and asks, “Does this actually work?” as close to production as possible.Jake [00:44:22]: That's how close you have to be, or you get massive drift. The system becomes unstable. You see this with massive systems built on Docker for local, Kubernetes for production, and a specific thing for something else. That complexity slows developers and becomes unstable at scale, making it hard to iterate. We want to compress that way down and say, “As close to prod as possible is where we want to be.”From AISRE Skeptic to Agent BelieverSwyx [00:45:00]: I was texting Erica for questions, and she says you were originally not a believer in AISRE. Have you come around on it?Jake [00:45:10]: I flipped, but I'm still not a believer in AISRE if you don't have the primitives to make it safe. If you unleash AISRE on production infrastructure without safe primitives for copying volumes and making sure things are fine, it's going to nuke your production database. It's not a matter of if, but when. I'm a big believer in making those loops safe.Jake [00:45:33]: I was a deep AI skeptic until 2023. In 2024, I thought, “Maybe I can roughly make this thing do it.” In 2025, I thought, “Now I can hold this.” Over winter break, everybody came back saying, “It's almost impossible to hold this.”Swyx [00:46:01]: Did you see this on the Claude docs? CloudBot? OpenCloud?Jake [00:46:06]: It's gotten to a point where it's harder to hold it wrong than to hold it right. There's a scene in Avengers where Vision picks up Thor's hammer and says it's terribly well-balanced. It self-balances and works well. I'm a deep believer at this point that this will be the dominant species: assembly, C, C++, JavaScript, words.Swyx [00:46:35]: It feels like a big jump.Jake [00:46:37]: It is. But it's not like you abandon CPU-based discrete logic and move straight to fuzzy logic. You need both. Your skills should call code or applications or some static structure. You can use skills to distill what the procedure should be or how the code should act.Jake [00:47:02]: I'm coming to a thesis: you need three points. You need a clear spec defining the system, the code, and the tests. When you say it out loud, if you've been in engineering long enough, you're like, “Of course. That's an RFC, tests, and code.” But they all matter. Having them together lets them reinforce each other: the spec and tests match, but the code doesn't, so reconcile it. Or the tests and code match but the spec doesn't, so reconcile that. That's the iteration loop.Jake [00:47:41]: That's why you're seeing people talk about software factories, docs, and reconciliation. Some of that is architectural astronomy if you don't implement it, but that loop is where most things will end up.Swyx [00:48:07]: For listeners, we've been talking about this on the pod for three years: the holy trinity of specs and tests. Itamar Friedman from Qodo is the reference if people want to look it up.Self-Modifying Infrastructure and the End of Push-Pull-RebuildSwyx [00:48:18]: One thing I want to mention on the OpenCloud idea is self-modification. I don't know how Railway would support it, but I have my OpenClaw, and I just tell it it has the Railway CLI and can do whatever. In theory, whatever capabilities or new infra it needs, it can call the Railway CLI, provision it, and add it to itself. The agent can modify its own infra.Jake [00:48:45]: It's nuts. I have a loop set up where you put the Railway CLI on top of something that runs on Railway. You're authenticated as whatever the current box is, and you can make any changes to it. Then you call Railway deploy, and it deploys itself.Jake [00:49:04]: It's like: “I need to spin up this instance of this environment. I already exist in this environment. Excellent, I have access to a Postgres instance now.” That's where we want to go with agentic, self-replicating infrastructure. That's your loop: iterate in production. You continue making changes. If it works, merge it upstream. If it doesn't, throw it away.Jake [00:49:37]: How do you make throwaway copies trivial to spin up and super cheap? The era of “I have an AWS instance with four vCPU and 16 gigs of RAM” is going to get destroyed. If you do that for agents, you need a thousand of those machines. It's prohibitively expensive compared with what we've spent a ton of time figuring out: the atomic unit of deploy, whether you call it isolates, sandboxes, or something else. Only pay for what you use, spin up instantaneously, and close the loop as quickly as possible.Jake [00:50:15]: If the system can self-replicate safely and say, “This is my environment, I'm making these changes,” it can come back with, “Does this look good? This is a new state of infrastructure given this prompt. I think I've solved it.” Then you go back and say, “Actually, it looks different.” It does the loop again. Then you say, “Cool. Apply.”Swyx [00:50:38]: That's retroactively obvious, which is the most useful kind. Any other comments on agent deployment on Railway?Jake [00:50:51]: It's getting better every day. I'm on X or Twitter. You can always yell at me about the parts not working as well as they should, because plenty of things should work way better.The New Serverless: Stateful, Long-Running, Pay-for-What-You-Use LinuxSwyx [00:51:04]: At this stage, when people want massively or embarrassingly parallel compute, they usually talk serverless. I feel like there's a new serverless compared to the previous five years of serverless. You're in that new bucket. Do you have comparisons or philosophical differences you want to call out?Jake [00:51:31]: It's somewhere in between. It's the ability to run stateful, long-running workflows or executions.Swyx [00:51:42]: Vercel has Fluid Compute, Cloudflare has some container thing, Google has App Runner and others.Jake [00:51:55]: That's where everything is roughly going, and it's why we've been working on this for six years. We believe users need access to a computer: a box that speaks Linux. They need to deploy what they want. Other systems change the surface area of what you can build. For us, users need a computer and need to deploy anything they truly want. That's why we've focused on the primitives: network, compute, storage. If we give you those and expose them so you can run things indefinitely, that's where we believe it's going.Jake [00:52:43]: Twitter has no nuance, so everyone says “servers” or “serverless.” It's always somewhere in the middle: I want to run it for a long time, but I don't want to provision the resource statically or pay for things I'm not using. That's been our thesis from day one: pay only for what you use, run it indefinitely, and it is full Linux.Swyx [00:53:12]: That's why I like the naming of Fluid. It's fluid. Flexible.Heroku, Focus, and Carrying the Torch Without Becoming the PastSwyx [00:53:18]: Another milestone is the Heroku official deprecation. You're one of the presumptive new Herokus. “New Heroku” has been a category for as long as I've been in developer tooling. It's finally happening. What was that like? Any behind-the-scenes of, “This is the moment”?Jake [00:53:42]: You have people where you're like, “You were running stuff on here? You, as this company?” It's crazy that names you would know are running on it and now coming to us saying, “We want to move a lot of this off.”Swyx [00:54:00]: Any behind-the-scenes on why Salesforce let Heroku stagnate?Jake [00:54:05]: I can only guess. It's hard when it's not your business. Salesforce's business is to build a great CRM. That's their focus. Then you acquire a compute business as an offshoot. A lot of early Meta people talk about focus. Boz has a write-up about how in the early days of Meta they had no money, so they were forced to focus. Then they turned on the money tree and had no reason not to split their focus.Jake [00:54:52]: But that dilutes your product. You get offshoots where you ask, “Is this the focus of the business?” If it's not core, it languishes. A lot of companies get in trouble when they split focus because they're fighting a multi-front war, not just externally but internally for alignment. Where are we going? What are we doing? What is our purpose?Jake [00:55:24]: If you're Salesforce-built and mission-driven, you want to work on Salesforce. Heroku is off to the side. It's not core to the business. Getting resources, budget, focus, and alignment internally becomes hard. It was a matter of time.Swyx [00:56:06]: Kudos for them to call it out instead of leaving it unknown.Jake [00:56:12]: Their release was a little odd. They called it out, but they didn't say they were shutting it down. Behind the scenes, I think they issued messages to people saying they should close accounts and that they were going to deprecate and remove things over time.Jake [00:56:30]: It's crazy because some of my first deployment experiences were on Heroku. You start with dragging things into an FTP server, then you try to get a deploy working, and then it's Heroku. It was the on-ramp for us. But the wheel turns. New things emerge. We're happy to carry the torch for a lot of that. But we don't want to be the new Heroku. We want to be the way people build and deploy software, and ultimately the way people monetize software over time.Swyx [00:57:19]: It's still a big crown to be the new Heroku. There are 50 companies that fought for that.Jake [00:57:23]: Everybody is holding some portion of it. We're happy to support people and companies. The platform works differently. The game loop is similar, but we've been dogmatic about where these things are going: primitives, agents, fan-out. Some things fit; some workflows need to change. We have an approximation of Heroku pipelines with the environment system. It's exciting. We've got a ton of people we can support, and it's growing a lot.Temporal, Workflow Engines, and State MachinesSwyx [00:58:12]: I have one more technical question about Temporal. I've sold my shares. You're a power user and one of our earliest customers. I met you through Temporal. You built on Temporal. You have complaints. This may be the most neutral and informed conversation anyone will hear about Temporal without someone working at the company.Jake [00:58:39]: That's fair. I've used Temporal for almost 10 years because of Cadence at Uber.Swyx [00:58:52]: Give people a sense of what Cadence was at Uber.Jake [00:58:57]: Cadence was the precursor to Temporal. It powers trip actions, rides, when you rent a Jump bike or scooter or car. You're running workflows for a period of time and saying, “This ride will run indefinitely until it finishes.” You attach information: you paused in this zone, so add this charge to the bill. When you end the trip, the workflow is done. That experience was powered by Cadence at the time.Swyx [00:59:34]: I used to say it's like programming the entire user journey top-down as one function.Jake [00:59:39]: It's a powerful idea and important. It's also important for the next phase of the agentic journey. You want an agent to do a specific task, be complete or incomplete on that task, and move on to the next thing. You need a way to manage workflows dynamically.Jake [00:59:59]: Temporal was always great in theory, and great when you got it working the way you wanted in production. But it required you to model the entire journey in your head. If you didn't, you could cause issues where replaying the state of the workflow causes non-determinism.Swyx [01:00:25]: Because it works on deterministic workflow history.Jake [01:00:28]: Exactly. I describe it as a jet engine. If you know how to operate it and run it, it's great. But you can't hand it to people trying to build complicated things if they don't have the whole state in their head.Jake [01:00:48]: We run our whole deployment pipeline on top of it. That's a reasonably complicated workflow: pre-commit hooks, signaling, queuing, and all the rest. We ran into the same thing at Uber. As you express a large workflow, it gets more complicated, with more states in the state machine that you have to map back to the workflow.Swyx [01:01:15]: It's a lot of ifs.Jake [01:01:16]: Exactly. At Uber, we built a system for doing the state machine and testing it. We've started to build some of those things here because it's grown heavily. It's not quite love-hate. When it works well, it works super well. But if someone who doesn't have full context puts something into the system that invalidates state or causes non-determinism, or spins off a ton of activities, you have to keep track of underlying SRE knobs like activity slots. Those should scale with memory, vCPU, and so on. It becomes a bear to scale.Swyx [01:02:10]: You need a capable sysadmin running things behind the scenes. If you moved off, what would you do?Jake [01:02:19]: We'd build our own workflow engine. We have a few internally that we've worked on.Swyx [01:02:27]: This is one of those classes of things you typically wouldn't vibe code, but I'm wondering if you can.Jake [01:02:33]: I still don't think you should vibe code it. You still want to run decent tests to make sure it works.Swyx [01:02:39]: Timo didn't invent that from scratch either. There are libraries you can run. On top of that, it's just a state machine that you have to map out. Ultimately, you define the instructions you want and run them through a state machine.Jake [01:03:00]: It's very doable. Workflow stuff is interesting. Restate is doing neat stuff here.Swyx [01:03:10]: You're tied into JavaScript. Are you a JavaScript maxi?Jake [01:03:13]: Internally, we have TypeScript, Rust, and Go. We don't add more languages. Actually, we have a little C because we write BPF code and hooks. But those are the languages.Swyx [01:03:28]: Is this for sidecars?Jake [01:03:32]: No. It's for the networking stack, volumes, and things like that. We use TypeScript a lot because it powers the dashboard, but we're moving a lot of workflow stuff off the dashboard stack and into the infrastructure stack.Railpack, Nixpacks, and Content-Addressable FilesystemsSwyx [01:04:00]: Cool. Any other technical infrastructure stuff? Railpacks?Jake [01:04:07]: We built an engine for determining dependencies based on source code. It's called Railpack. We built the first version, Nixpacks, on top of Nix, and then we moved.Swyx [01:04:17]: People have been trying to get me to adopt Nix and NixOS for four years. Is it ever going to be a thing?Jake [01:04:23]: I don't know. We're excited about it, but it has pain points. Think of it as a stack of versioned binaries at specific slices in time. If you want version X and version Y, you bloat the package space, which blows up image size and makes real-world workloads difficult.Swyx [01:04:53]: But you content-address it and cache it. In theory, there are optimizations.Jake [01:05:00]: In theory, yes. But with a large enough user base and disparate enough machines, you run into a problem Meta described in the XFAAS paper, their internal serverless system. It becomes difficult at scale unless you break out specific runtimes.Jake [01:05:24]: We didn't want to do that because we wanted to truly allow you to deploy anything. That was our initial thing with Nix. But we've moved toward interesting work around content-addressable file systems that can lazy-load anything from any point and page it into memory.Swyx [01:05:48]: Amazing.Jake [01:05:49]: The future is very bright. It's crazy, and it's going to be nuts.Coding Agent Spend, Roadmaps, and Token ROISwyx [01:05:54]: Founder journey stuff?Alessio [01:05:56]: Your cloud usage: you tweeted you're going to spend $300K this month?Jake [01:06:01]: I think we got to $200K.Alessio [01:06:02]: Coding agents?Jake [01:06:03]: Yeah.Swyx [01:06:04]: Across the company?Alessio [01:06:05]: You only have 35 people, so I'm sure they're not all spending $10K a month. What's the distribution?Jake [01:06:10]: I think I'm at about $25K. We have power users all the way down. We came back from winter break, and I basically said, “If you're writing code by hand, you're doing this wrong.” The tools are good enough now that you can move extremely quickly. There are issues and pain points, but you should be reviewing the code you are writing instead of writing it by hand.Jake [01:06:40]: Architectural patterns matter more now than ever, but you shouldn't spend your time generating code you would write. If you know how to write it, ask the agent to write it and reconcile it until it looks like you would have written it yourself.Jake [01:06:58]: People misconstrue my propensity to push people toward agents as connected to our growth and some reliability bumps. They're not necessarily related. The tools are good enough to move extremely quickly and build things way larger than you could before.Jake [01:07:19]: To the earlier point about cooling data centers in space: I don't know. But with software, you can ask, “How would I build block storage from scratch? How would I do these things?” I have ideas because I have history and have read papers. Let me work them out and build massive test benches with thousands of tests, because those are now free to author. If you're not using AI systems to speed-run your roadmap and reconcile your existing system onto the future, you're missing a large point of what's happening.Alessio [01:08:12]: What's the path to spending $3 million a month? Is it bound by ideas and things customers can absorb?Jake [01:08:19]: For most companies, it's bound by deployment at this point. That's why we've seen a massive boom in users and companies, from Fortune 50s down, asking how to get developers to move faster. You'll probably hit your CFO before any technical limits because they'll look at the eye-watering amount of money spent on tokens. Inference costs have to come down, but we're inference constrained now. There will be price discovery around what makes sense for an org to adopt.Jake [01:09:06]: I think you'll end up with the F1 driver concept. If someone is really adept at these things, it makes sense to put them in a $3 million car. If they're not, it probably doesn't make sense. You'll take a few people and say, “You can drive the F1 car. We need to go in this direction. Figure out if it works and prototype it.”Jake [01:09:33]: We've done some of that and vastly accelerated our roadmap. We thought we'd ship something in a few years; now we can probably ship it in a few months because we validated it and don't have to build it incrementally. We can skip steps and move toward our vision.Alessio [01:09:58]: A lot of people are realizing the roadmap doesn't always have a business impact, so they say tokens are too expensive. But if your roadmap were built to make more money by the time you built it, you'd have token pricing for it, the same way you do with sales. You'd spend a billion dollars on sales if you knew you would get $2 billion of revenue.Jake [01:10:19]: Exactly. A naive way to measure this is the percentage of tokens that end up in production. If you can measure impact because those tokens end up in production, that's awesome. But the burden of proof will rise. Internally, we have a growing number of pull requests that haven't merged. The question becomes: how do you get this into production? It's about how quickly you can build and deploy software, which is exciting because that's our whole thing.The SDLC Shift: Prompt Requests, Feature Flags, and Safe RolloutsSwyx [01:10:56]: The SDLC is changing. One thesis is that the pull request is dying. It's going to be the prompt request. Beyond that, code review is also kind of dying if you have all the other systems in place. What else is changing about the SDLC?Jake [01:11:19]: The AISRE and the tools to make it happen. AISRE is pie-in-the-sky aspirational. What does it take to get an AISRE? What tools do you need to build?Swyx [01:11:32]: You should expose your tooling to customers at some point. The Central Station command center.Jake [01:11:39]: We have it for template maintainers. Template maintainers can deploy and maintain templates, and they get feedback. We're going to expose those things incrementally.Swyx [01:11:51]: Clustering around incidents. Everyone has a version of that, but I don't think anyone has solved it.Jake [01:11:56]: I won't say we've solved it internally, but it's gotten so good that we can see incidents forming pretty quickly. At some point, those will be things either someone else builds or we build. We've always built things purpose-built for us. If it makes sense to make it useful for users, monetize it, or turn that loop into a profit center instead of a cost center, we want to do that.Jake [01:12:28]: Pull request is definitely dying.Swyx [01:12:29]: Do you do first-party feature flagging and incremental rollout stuff?Jake [01:12:34]: We have a feature-flagging engine we built internally and will eventually roll out.Swyx [01:12:38]: I don't see it as a user. How come you didn't give us what you have?Jake [01:12:43]: We have to beta test it. We care a lot about the quality of the things. There's plenty we've used internally that doesn't make it all the way through the journey because it fails. It works for one service but not multiple services. We'd have to build it for multiple services and know that if we released it, we'd rebuild it again and again. Some things are worth that, but many inform the roadmap.Jake [01:13:18]: We don't want to dilute the experience by saying, “This works, but only for this service,” unless it's a core initiative. Over the next few months, we'll roll out things that work for a single service, then multiple services, then multiple services across the environment. You have to be deliberate. Otherwise you create broken disparate experiences and support load because people ask how to use the feature.Jake [01:13:52]: It's the earlier expansion and compaction pattern. You expand the company to get features, then compact and smooth them out so the experience is stellar. You told me in the hallway, “It's gotten so much better.” Internally we're saying, “This part really sucks. We need to make it significantly better.”Swyx [01:14:11]: I can attest to that over the last three years watching you build Railway. For listeners, feature flagging is a huge part of Uber culture. So much so that they have too many feature flags and another thing to remove feature flags. Facebook has Gatekeeper. Agents are going to need this. It's fundamental to incremental rollouts. OpenAI acquired Statsig. GPT-5 is routing and flagging through different models.Jake [01:14:56]: It's super important. If the software development lifecycle is going to change because we're doing things 1,000 times faster and 1,000 times more concurrently, what becomes important at scale?Jake [01:15:16]: Before I started Railway, I built a feature-flagging product and tried to sell it. It was an easier version of LaunchDarkly. I ran into a problem: anyone small enough to adopt your technology doesn't care about feature flags, and anyone large enough to need feature flags needs so much scale that you have to build out all the infrastructure. I scrapped it.Jake [01:15:42]: But what is old is new again. Companies are trying to move quickly, but you can't YOLO a vibe-coded thing straight into production. You need to say, “Here's my blast radius, my impact, and I want to shadow it for these users.” Feature flags. You're going to need the tools larger companies built to maintain their structures. Everything gets compressed by 1,000x so everybody can build those structures quickly.Jake [01:16:07]: That's exactly where we are: compressing the software development lifecycle, then expanding it and adding more new things.Cattle, Pets, and Clonable InfrastructureSwyx [01:16:15]: Another term that comes to mind for newer developers is “cattle, not pets.” People treat production like a pet. It has a name. You baby it and keep it alive. With cattle, you can mass farm, roll out, portion parts out, and kill them.Jake [01:16:37]: I think that might change. You can move toward having pets as long as you have a cloning machine for your pets.Swyx [01:16:52]: Yeah.Jake [01:16:52]: If you can snapshot every single thing at every frame, it doesn't matter if something gets obliterated because you have a snapshot of it. The things we've built right now are designed to block changes from the hermetically sealed DevOps line. You have to write a Dockerfile because you nee

What does it actually mean to bet on yourself, and how do you know if the game you're playing is really the one you want to be in?In this special episode of Supra Insider, Marc Baselga and Ben Erez record together in person for the first time, sitting down at a studio in New York to have an honest, unscripted conversation about optionality, partnership, and what they've learned from building Insider Loops over the last seven months. They open with the question hanging over a lot of high-agency people right now: with AI making it easier than ever to go from idea to product, should you leave your job and bet on yourself? Marc names the only full-time role that genuinely tempts him, Anthropic, and then explains exactly why he still wouldn't take it. Ben unpacks why he accomplished 20% of what he was capable of during his full-time years, and what changed.They go deep on why bootstrapping is harder than raising VC money, but why the constraints force the kind of market discipline that most funded companies never develop. They map out their complementary skill sets, how they've shifted from long-term planning to weekly cadence, and why they now think planning more than a week ahead is mostly a waste of time. The conversation closes on the role of the podcast itself, why it has to stay separate from the business, why fun is an emergent property and not a frivolous goal, and why the relationship comes first.If you're weighing whether to leave a stable job and go off on your own, curious about what a bootstrapped partnership actually looks like day to day and what makes it work, or just want a rare honest conversation between two builders about what they'd do differently and what they wouldn't change, this episode is for you.A special thanks Alex Pavlou and the team at 28th & Park for the recording space!All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

2,5% Zinsen p.a. auf ein unbegrenztes Guthaben mit bis zu fünfmal der gesetzlichen Einlagensicherung*. Auch für Kinder. Das gibt's bei Scalable Capital. Mehr Infos hier: https://de.scalable.capital/tagesgeld?utm_medium=affiliate&utm_source=qualityclick&utm_campaign=broker&utm_term=655&c_id=QC5a486e706d75r687A578577c4a5a406d7364766d717e557847 Zinssorgen drücken Tech, Gold und Krypto. Alphabet platziert Rekord-Anleihe in Japan. Technoprobe explodiert 30%. Magnum Ice lockt Private Equity. Figma trotzt KI-Konkurrenz. Mercedes liebäugelt mit Rüstung. Essilor Luxottica (WKN: 863195) verliert 40% seit Jahresstart. Ein Erbstreit in der Gründerfamilie belastet, Smart-Glasses boomen, aber die Konkurrenz von Google, Apple und Samsung wächst. KGV bei 23. Buffetts Lunch gibt's dieses Jahr für 9 Mio. $ statt 19 Mio. $. Berkshire Hathaway (WKN: A0YJQ2) kauft Delta Air Lines, stockt Alphabet massiv auf und verkauft Visa, Mastercard, Amazon und mehr. Diesen Podcast vom 18.05.2026, 3:00 Uhr stellt dir die Podstars GmbH (Noah Leidinger) zur Verfügung. *Veränderlicher Zins auf unbegrenztes Guthaben. Konditionen sowie Guthabenverteilung auf scalable.capital/tagesgeld. Learn more about your ad choices. Visit megaphone.fm/adchoices

NLW previews Google I/O and the bigger question hanging over it: whether Google can turn its massive AI advantages into products people actually want to use. The episode connects Codex coming to ChatGPT mobile, the rise of always-on agents, rumors around Gemini Spark, and Google's potential opening as a cheaper high-performance model provider for builders and enterprises. In the headlines: Cerebras' explosive IPO debut, Figma's AI recovery, OpenAI and Apple tensions, Anthropic's massive new valuation, and more.Apply for our Growth Engineering role: ⁠⁠⁠https://jobs.aidailybrief.ai/⁠⁠⁠Enterprise Claw Cohort 3 Registration: ⁠https://enterpriseclaw.ai/⁠Brought to you by:KPMG – Agentic AI is powering a potential $3 trillion productivity shift, and KPMG's new paper, Agentic AI Untangled, gives leaders a clear framework to decide whether to build, buy, or borrow—download it at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.kpmg.us/Navigate⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Granola - The AI notepad for people in back-to-back meetings. 100% off your first 3 months with code AIDAILY at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://granola.ai/aidaily⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Scrunch - The AI customer experience platform - ⁠⁠⁠https://scrunch.com/⁠⁠⁠Mercury - Modern banking for business and now personal accounts. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://mercury.com/personal-banking⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Zenflow Work - Agents for knowledge work - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://zenflow.free/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Drata - The agentic trust management platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://drata.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

We sat down with Dan Gent, who ran his product design agency, Lighthouse, for 15 years before walking away in 2023.Dan details how Lighthouse reached 20 people and roughly $2 million in revenue before market conditions forced a closure rather than a traditional exit. He explains the dangerous "dip" where profit margins drop as you hire non-billable managers and ops staff to scale. This conversation is a blunt look at the reality that businesses often end by closing or walking away instead of a multi-million dollar payout.We also tackle the difficulty of design retainers and why clients often stop seeing value once the initial project is finished. Dan advises treating yourself as an employee with a set wage in your forecast so you can actually afford to hire your replacement. He also touches on how AI is commoditizing "grunt work," forcing designers to sell accountability and taste instead of just Figma files.-----MENTIONS IN THE EPISODE:Follow Dan:LinkedIn: https://www.linkedin.com/in/dangent/The Dear Agency Founder Newsletter:https://join.dearagencyfounder.com/?utm_source=agencygrowthpodcast-----RESOURCES:Want the tools and resources we recommend for agencies? Check them out here:https://www.agencygrowthpod.com/tools-----NEWSLETTERWant the show in your inbox? Sign up for the newsletter!https://www.agencygrowthpod.com/newsletter-----COMMUNITYLooking to join a community of agency owners? Join our Discord!https://discord.gg/uvHRRRFVRD-----CONTACTGot something to say? Send us a message:https://www.agencygrowthpod.com/contact

Market update for Friday May 15, 2026Check out the Public app for incredible investing tools and to support the show (LINK)Follow us on Instagram (@TheRundownDaily) for bonus content and instant reactions.In today's episode, Zaid covers:Why oil prices are rising after the Trump-Xi summit OpenAI preparing possible legal action against Apple over their failed ChatGPT partnershipHow Ford became an AI stock after launching Ford EnergyFigma crushing earnings and proving software stocks aren't dead yetApplied Materials posting record results but the stock falling anywayPlus, Zaid answers a listener question about his journey from civil engineering to hosting The Rundown

In der heutigen Folge sprechen die Finanzjournalisten Philipp Vetter und Holger Zschäpitz über neue Rekordmarken an der Wall Street, den Traumstart des Nvidia-Jägers und Photonics-Phantasie bei POET Technologies. Außerdem geht es um Nvidia, POET Technologies, Cerebras, Goldman Sachs, Cisco, StubHub, CTS Eventim, Ford, Coinbase, Robinhood, Applied Materials, Figma, Ondas, Palantir, Infineon, Siemens, SMA Solar, Fraport, Borussia Dortmund, Biontech, Tesla, Xiaomi, Apple, Micron, Blackstone, Boeing, TSMC, Sandisk, Intel, Lumentum, Seagate, Western Digital, Ciena, Coherent, AMD, Rackspace Technology, MaxLinear, Agilon Health, Bandwidth, Aehr Test Systems, Entravision, DigitalOcean, SELLAS, Bloom Energy, Atomera, Intuitive Machines, Arteris, Vicor, SiTime, TEQ - General Artificial Intelligence UCITS ETF (WKN: A41AXG). Wir freuen uns an Feedback über aaa@welt.de. Noch mehr "Alles auf Aktien" findet Ihr bei WELTplus und Apple Podcasts – inklusive aller Artikel der Hosts. Hier bei WELT: https://www.welt.de/podcasts/alles-auf-aktien/plus247399208/Boersen-Podcast-AAA-Bonus-Folgen-Jede-Woche-noch-mehr-Antworten-auf-Eure-Boersen-Fragen.html. Hier könnt ihr den AAA-Newsletter abonnieren: https://www.welt.de/newsletter/article232797673/Alles-auf-Aktien-Der-taegliche-Boersen-Newsletter-fuer-WELTplus-Abonnenten.html Und - ganz neu: AAA gibt es jetzt auch auf Instagram: https://www.instagram.com/alles_auf_aktien/ Disclaimer: Die im Podcast besprochenen Aktien und Fonds stellen keine spezifischen Kauf- oder Anlage-Empfehlungen dar. Die Moderatoren und der Verlag haften nicht für etwaige Verluste, die aufgrund der Umsetzung der Gedanken oder Ideen entstehen. Hörtipps: Für alle, die noch mehr wissen wollen: Holger Zschäpitz können Sie jede Woche im Finanz- und Wirtschaftspodcast "Deffner&Zschäpitz" hören. +++ Werbung +++ Du möchtest mehr über unsere Werbepartner erfahren? Hier findest du alle Infos & Rabatte! https://linktr.ee/alles_auf_aktien Impressum: https://www.welt.de/services/article7893735/Impressum.html Datenschutz: https://www.welt.de/services/article157550705/Datenschutzerklaerung-WELT-DIGITAL.html

In this latest episode of Executive Function, Brett sits down with Praveer Melwani, CFO at Figma. Praveer joined Figma in 2017 as the company's first business operations and finance hire—when the team was around 30 people and not yet charging for the product—and stepped into the CFO seat in 2022, helping to lead the company's IPO in 2025. In today's conversation, Praveer breaks down the step functions that took him from IC to CFO, why Figma started acting like a public company three years before IPO, and how AI is rewriting capital allocation and the CFO job itself. In today's episode, we discuss: What separates a world-class finance leader from a traffic-cop CFO How Praveer went from Figma's first biz ops hire to CFO of a public company in nine years Why Figma started acting like a public company three years before its IPO What Praveer has learned working alongside Dylan Field for nine years Why Figma intentionally cut its 90% gross margin to invest in AI References: Adobe: https://www.adobe.com Brendan Mulligan: https://www.linkedin.com/in/brendanmulligan Cloudflare: https://www.cloudflare.com Dropbox: https://www.dropbox.com Dylan Field: https://www.linkedin.com/in/dylanfield/ Fidelity: https://www.fidelity.com Figma: https://www.figma.com GIC: https://www.gic.com.sg NerdWallet: https://www.nerdwallet.com Shaunt Voskanian: https://www.linkedin.com/in/shauntvoskanian/ Where to find Praveer: LinkedIn: https://www.linkedin.com/in/praveer-melwani Where to find Brett: LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/ Twitter/X: https://twitter.com/brettberson Where to find First Round Capital: Website: https://firstround.com/ First Round Review: https://review.firstround.com/ Twitter/X: https://twitter.com/firstround YouTube: https://www.youtube.com/@FirstRoundCapital This podcast on all platforms: https://review.firstround.com/podcast Timestamps: 00:00 Introduction 02:13 From banking to Dropbox to Figma 04:14 The phase shift when Figma's COO left 05:36 Hiring leaders in functions you don't understand 07:18 Selling the exec team on AI consumption pricing 09:48 Using Claude Code to learn new things as CFO 11:36 Building an internal board of peer CFOs 13:52 Inside Figma's CFO job description 16:38 What separates good CFOs from world-class CFOs 18:42 Capital allocation and risk in a post-ChatGPT world 21:45 Why Praveer wants to take more bets 24:32 How AI is materially changing the CFO role 25:36 The nine-year working relationship with Dylan Field 29:12 How deeply in the details should a CFO be? 31:47 What Dropbox taught Praveer about building strong teams 33:24 Praveer's first-principles test for hiring VPs 38:47 Why Figma acted like a public company in 2022

这期 Bear 带来了最近几件真实发生的事，串起来一看，都指向同一个问题：在 AI 让一切变得更快、更容易之后，我们到底在追什么？---**

AI for Designers: 5-week Bootcamp

https://youtu.be/aQyHwoGfy50 Max Kryzhanovskiy, President and CEO of MOS Creative, is driven by a desire to set an example for his children and show what's possible through technology, persistence, and innovation. As the leader of a tech-forward agency that builds websites, apps, and AI-enabled platforms, Max helps businesses move from idea to execution by creating digital products that solve real problems and scale over time. We explore Max's MVP Framework — Define the problem, Determine target market, Prototype the product, Build the MVP, Test and obtain feedback, Iterate — a practical approach for transforming ideas into scalable digital products. Max explains why founders should avoid overbuilding too early, how AI is accelerating prototyping and development, and why businesses must balance automation with authentic human connection. — Drive Growth Using AI Agents with Max Kryzhanovskiy  Good day, dear listeners. Steve Preda here with the Management Blueprint Podcast, and my guest today is Max Kryzhanovskiy, the President and CEO of MOS Creative, a company that builds websites and apps that drive growth. They were also the first company in Baltimore to launch a mobile site. Welcome to the show, Max.  Thank you for having me.  Let me ask you this—what is a mobile site? Is it a mobile phone site, or is it something different?  I mean, now it probably doesn't matter as much anymore, because everybody obviously has a website that works on a smartphone screen—or a responsive websites. But before mobile websites came out—or I should say, when smartphones first came out—we had to adjust for smaller screens. We were all used to bigger screens on a computer, and then once we started having different screen sizes come out before responsive, we were the first company to have a mobile website in Baltimore. And we actually built a web application specifically to create them ourselves, and then also went to market to offer it to other clients as well. So a mobile website is just like it sounds, a website that’s specifically designed for mobile.  That’s cool. So it sounds like you are very much a tech-forward company, and you are at the edge of technology. And as we were logging on, you said that you would be recording this on your phone because you actually have AI agents running on your computer. Does that mean you have AI agents as part of your team? What kind of agents do you have? Is it still an experiment, or is it already in execution mode?  It's in execution mode, but we're always experimenting. We like to think we're ahead of the curve, but with AI, we're all experimenting to a certain extent, right? Something new comes out, we try it out, see if it works, and see how it can be applied to your business—what kind of outcomes it can give you. So I'm all about AI. It's amazing. It's an amazing tool. But I think AI is becoming a lot more than we thought it was going to be—and also a lot less at the same time. Meaning, when AI launched—for example, when ChatGPT came out to the broader market—I mean, obviously AI had been around for a while—but when ChatGPT launched its chatbot platform publicly, we were amazed by how much work it could done. So it went from zero to a hundred. “Oh my God, it can do all of this,” right? But now, for example, with the more recent models—4.5, 5.0—the improvements are much smaller.  It's not a hundred percent or a thousand percent better anymore. Now it's maybe five or ten percent better, but the cost keeps increasing. I just read somewhere that even Claude said Claude Code won't be included much longer as part of the regular plan. So now it's only in the $200 higher-tier plan, plus you have to buy additional tokens. So it's really becoming more like, “Hey, yeah, we can do this for you—but you're going to end up paying something similar to what you'd pay a team.” At first, it was more like, “Let's get into the market. Let's get a lot of people interested.” But now, obviously, they have a lot of money behind them—investors, VCs, public market pressure—and they need to bring in revenue. So I think things are going to change very soon. AI is going to become a lot more expensive because the infrastructure and resources it requires are expensive. So eventually, those costs are going to be passed on to users. Yeah. And I noticed that ChatGPT started to do some ads as well. They’re probably going to go that direction, and who knows what that’s going to bring. But that's not our topic today. Today, it's about something else—frameworks. But before I go to the framework question, I'd like to ask you: what is your personal “why,” and how are you manifesting it at MOS Creative? Well, I'm a family man, so my “why” is to see my kids grow up to be amazing human beings—and hopefully to show them a great example of what can be accomplished in sports and in business. So my “why” is also to be a good person. Success can mean different things to different people, but for me, I love the hunt to get to a certain level of success. And then it's kind of like—us as humans, or at least a lot of people—we reach a certain level of success and we don't really celebrate it. It's more like, “Okay, let's get to the next level.” So my “why” is to show my kids that anything is possible if they really want it. Why I got into this space—it was exciting. You could see how quickly technology was moving, the kind of innovation that was possible, and it excited me. So that was one of the main reasons I got into technology. But the other reason was because I was in a different business, and we created technology that helped us grow. And I thought, “Oh wow, this is a completely different way to scale a business.” So technology became the direction we took. Yeah, I love it. I think inspiring our kids is a huge driver for many people, and it totally makes sense. Technology is exciting. I'd like to switch gears here and ask my other common question on this podcast, because this podcast is all about frameworks—business frameworks—how we can help listeners understand things, simplify things, and see different perspectives. So my question to you is: what is your favorite shortcut to success—or framework? And I don't mean “shortcut” in a negative sense, but rather a framework that allows you to understand things differently, make decisions, serve clients, and create valuable outcomes. Whatever it is—something that has worked for you, and is simple enough that you can explain it to listeners in three to five steps. Well, I believe in always being open to learning. It's not specifically a framework—it's more of a mindset: understanding that we don't know everything, especially now, with how quickly things are changing. I mean, a lot of people say that AI is going to make humanity a little dumber than we are. But actually, I learn a lot from it as well. If I'm doing something and I think, “Oh, this is a great way to speed up the process,” then I use it. So let's say, for example, a client asks me a question. There are different ways to approach it. If I already know the answer because I have specific experience with it, I can answer it, right? That doesn't always mean the answer is going to be correct.  I can research it, or I can get an answer from AI and then verify it through research and experience to make sure the outcome is actually what it says it's going to be. The learning part is making sure you're always open to figuring out whether the steps you've taken before are the right steps—or whether they can be optimized. I'm a big believer that everything can be optimized, especially now. There's almost no question that can't be answered quickly. Maybe there are some deep philosophical questions—but for the most part, especially in business, work, or even life, you can get answers very quickly. For example, I had a kind of vertigo-type feeling, and I was wondering what exactly it was. I entered specific prompts into ChatGPT, and it actually broke things down really well for me. Then I went to a doctor. First, I checked with a friend of mine who's a nurse, and she said, “This is probably what you have.” And she started asking me questions. I thought, “This is funny—these are exactly the same questions ChatGPT asked me.” And her husband said, “You know what? That proves that medicine is basically a set of questions. As you answer one question, it leads to the next.” So it's like a dynamic questionnaire. And by the time I got to the doctor, I already had a good idea of what it potentially was, and I knew what questions to ask so I could understand the next steps to fix it.  Yeah.  So what I'm saying is there’s always a way to improve. I'm a big believer in that. It doesn't matter what you're doing, because in this age, everything moves very fast—regardless of the business you're in. That's true. It's interesting that you say ChatGPT can answer any question. It's true—sometimes it hallucinates, but it still gives you an answer. Yesterday, I went to a presentation, and the president of Great Game of Business talked about this. He said, “Today, the answer is everywhere. So it's not a lack of answers—it's a lack of good questions.” So what we really have to come up with are good questions to ask. That's the bigger challenge now—not finding the answer. And I thought that was a really interesting insight. I agree. It's the same thing, right? It relates to prompts as well. If you have a good prompt, you're going to get a better answer. If you ask a good question, you're going to get a better answer. So yeah, I agree with you. Listen, AI isn't a complete solution, but it's a huge help—especially if you're just starting out. Yeah. So what drives your business? Is it technology? Is it trends? Is it something else? What drives it?  It's kind of a mix between technology and growth marketing. What that means is we work with clients all the way from ideation to scaling. We've also had several clients successfully exit. So clients come to us and say, “I have an idea. How do I take it to the next step?” Obviously now, there are AI builders and AI platforms that can help take a high-level idea and turn it into some kind of prototype—or at least a basic flow. But ideally, we work with clients from the idea stage all the way through design, development, launch, and driving traffic to the product. So the perfect client fits into that category. They might have an idea for a web application, mobile application, or software product.  They come to us and they're not really sure what the next steps are—or they've done some research For example, I spoke to a prospective client the other day. She worked with a developer who tried to build the product using an AI builder. For some reason, something didn't work out, and now she's back at square one. So now we have to review what she actually wants to build, determine the best approach, and figure out what phase one, phase two, and phase three should look like. So that's kind of how we work. For our clients, it's not just, “Let us develop it for you.” It's also about the creative side, the messaging, and the user experience. It's about making sure that when someone downloads the app—or visits the website or web application—it serves its purpose. It's a problem-solving product. It needs to solve a problem so users keep coming back again and again. And then we help grow it to new audiences. That's when it starts to scale and become exponential. Does that make sense? Yeah. So I’m wondering, you work from the idea forward, or you work from the outcome backwards? What’s the approach?  That's a great question. Not everyone knows the outcome right away. When someone has both an idea and a clear outcome, it works better, right? Because then you can help them get to that outcome. But overall, the outcomes are usually very high-level. You know: “I want to build this web application or software because I'm targeting this audience.” Okay—but what does that really mean? What problem are you solving? To be honest with you, ninety percent of people don't really know what problems they should be solving at the initial stage. So, talking about frameworks, we work with them to define which problems they should solve first. Because most startups—or even profitable companies trying to add new technology into their workflow or business—often don't know what one or two problems they should solve for the MVP before going all in. Yeah. Okay, so step one is to define the problem. What's step two?  Make sure you have the right audience for that problem. That's a big issue. A lot of times, people try to serve everyone. You don't want to go too broad, and you don't want to go too narrow. If you go too narrow, you're going to hit a ceiling before you even go to market.  So you determine the audience for the problem you're trying to solve, right?  Correct.  And then what's the next step?  Once you determine the audience and define the problem, the next best step is to create some kind of prototype and actually take it to that audience to test for product-market fit. Meaning: get feedback. Again, it doesn't have to be a fully working product. But go to that audience and get feedback like: “Yes, this solves my problem,” and “Yes, I would pay for it.” Or even better—for them to actually exchange some money to join a waitlist or gain access to an early version of the product, so they can test it and provide feedback. That's the best-case scenario. Because once you have that input, it becomes much easier to make adjustments. It doesn't matter whether those adjustments are in the design or in the actual working product—you're refining it for that niche audience. Yeah, that makes sense. So you design the prototype or minimum viable product, then you test it and get feedback. Then what do you do?  Well, I want to clarify something. Designing a prototype and having a minimum viable product can be two separate things.  Okay.  You can design a prototype. Again, it can be designed in Figma, using an AI builder, or even just as a workflow or user flow. Obviously now, things are a little different because you can build prototypes much faster. That doesn't mean they're going to be production-ready. But a minimum viable product is usually focused on solving one or two specific problems for that market. It's a problem-solving product that actually works—meaning it's much closer to being production-ready. Yeah.  So those are two separate things. There's a very big difference between them.  Yeah, because now you have vibe coding, and with tools like Lovable—or whatever platform you're using—you can create a prototype quickly. But it's not necessarily going to work, and then you still have to build the actual working product. Correct. Yes, I agree. Then you test it, expose it to the target market, and gather feedback. And then what do you do? Do you iterate? What's the next step? You iterate, yeah. So at that point, ideally, you have product-market fit, you've received great feedback from users, and—best-case scenario—they've even paid you some money. Then you either expand on what has already been built, or you go all in: invest more money into it and start building a production-ready product. And once you have that, you may realize that you also need to improve the user interface. That happens a lot—especially if you vibe-coded it. The output usually isn't the best when it comes to user interface design or user experience. So you may need to redesign the interface, properly develop it, and then take a production-ready application to market. And then it goes back into the cycle of iteration. Meaning, you keep gathering feedback. This is why I often recommend not adding too many features in the beginning. Focus on one or two core features—one or two main user flows within those features. That's it. Forget about everything else. Yeah. And then you can add features later.  You can always add features later. Most of the time, if you add too many features in the beginning, you'll probably end up cutting at least 40% of them because people just won't use them. And I'm not talking about core features like sign-up, sign-in, forgot password, onboarding, authentication—that kind of stuff. Obviously, you need those. But you still have to figure out who your audience is. Do you need SMS login? Do you need email login? Do you need both? Do you need social logins? You have to make sure you clearly understand your audience—but you don't need everything all at once. You may eventually need all of it, but not in the beginning. Yeah, that's true. So you've worked with other businesses, which means you're primarily a business-to-business agency, right?  Business-to-business, business-to-government—we've also built business-to-consumer apps as well. But usually, our client is a business-to-business.  Yeah. So here's my question: In B2B, how do you gain people's trust so they'll even engage with your product? I understand there's a funnel—but how do you get businesses into the top of that funnel? How do you create that initial trust so they engage? What does it take? Many things. Content helps, obviously. Creating content like this, creating videos—I create videos on a regular basis talking about what's out there, what's possible, what's good, what's bad. Kind of the everyday life of an agency, and the type of work we do. We also post projects on different directories and platforms. A lot of previous clients come back to us, and we get many client referrals. We rank pretty well for SEO and AEO, so a lot of people find us through ChatGPT. Especially because that's one of the services we offer. People find us when searching for things like “best app developers” or “best website designers” in our specific area. We're not targeting nationwide rankings—that's much harder and a much longer-term strategy. But in our area—Maryland, Howard County, Columbia—we rank very high.  And what does it take to rank high in AEO—in AI search?  It's the same approach we take to rank in Google. Google obviously owns Gemini, and now there's Google AI Overview. It's really a real-estate play. If you have a website that's properly structured for Google—with some adjustments for semantic search, like adding question-and-answer content to every page, especially product and service pages—you improve your chances significantly. You also need a properly configured robots.txt file with clear descriptions, so when search crawlers reach your site, they can immediately understand the structure and know where to go. When you see sources cited in AI search, that's exactly what those systems are reading from your site.  You also need the right technical setup: Your website has to be fast. You need proper H1, H2, and H3 structure across the site. So overall, it's about having a properly structured website. If you follow strong SEO fundamentals, with additional improvements specifically for AEO and GEO—because now it's not just SEO anymore, it's SEO, AEO, and GEO—you'll usually appear in ChatGPT, Google AI Overview, Gemini, Perplexity, and other AI search tools. And your Google Business Profile and Google Maps listing are properly optimized—which has changed a lot recently on Google's side as well—you'll also show up more often in local AI search results. So isn't it true that AI search looks for different kinds of signals than traditional SEO? I've heard, for example, that backlinks are less important in AI search than they used to be. They're not as important for AI search, but backlinks still carry a lot of weight. Again, you have to think about this as two separate systems, right? There's Google Search—with Google AI Overview and featured snippets—and then there's Google Maps. You don't need a website just to appear on Google Maps. You mainly need a properly optimized Google Business Profile. And you can still show up in AI search that way. Having a website does help, because it sends another signal to Google, but it's not as critical. The most important thing—and I'll answer your question for both cases—is consistency and structure. For Google Maps, if you have a properly maintained Google Business Profile with constant updates—blog posts, videos, photos, and business updates—that teaches Google AI what your business does. So you want updated product pages, images, descriptions, and location details if you're location-based.  All of that educates Google, which helps you rank higher on Google Maps. And like I said, Google Maps ranks very well in AI search. Now, if you also have a website, that's even better. And on your website, it helps to embed your Google Map as well, because that reinforces another signal from Google Maps. For example, some of our clients have multiple locations, so we include Google Maps with all their locations on the site—and that helps. Then you also create location pages, just like you create product pages or service pages. Google—and AI systems in general—don't really rank entire websites. They rank individual pages. That's why top-of-funnel content is usually blog posts or educational content answering someone's problem. Then that written or video content leads users to a service page or product page. That's basically how it works. Does that make sense? Yeah, that's very interesting. So if I want to increase my AI ranking… one of my clients told me that if your clients post about you on Reddit, that can be really powerful and help drive AI search visibility. Is that true? Reddit and Quora are very powerful. Very powerful. They rank very high. Listen, I'll give you a simple example that anybody can use. If you go to Quora or Reddit and look at the questions people are asking—for example, let's say you search for “app development”—you can filter by questions and literally see what people are asking. If you answer those questions in a natural way, related to your service or product, and include a backlink—not in a salesy way, but naturally—that's a very strong backlink. And speaking of backlinks: they're still relevant. Maybe they don't carry as much weight as they used to, but they're still very valuable.  Because when Google or AI systems evaluate content—and when you search in ChatGPT, Claude, or Gemini and see sources—those sources are essentially citations and backlinks. So if your website has strong citations and is properly structured, it absolutely helps you get discovered. You just need to make sure everything is set up correctly so Google—or any other search system—understands what your content means. But yes, to answer your question directly: Reddit and Quora are excellent for visibility because they're high-authority websites with massive traffic and very strong domain ratings. Yeah. That’s great. So Google Maps, Reddit, Quora, they are big drivers. That’s great.  Huge drivers. I mean, listen, there are many others—but social media has become huge over the past two years. Before, if you made a Reel on Instagram, you wouldn't be able to find it through Google search. But in the past couple of years, they opened that up. Why do you think they did that? Because they understand the value of content. Just like YouTube—where you can find videos through specific keywords—they want Instagram videos to be discoverable through Google Search and AI search. And then those searches lead people back to their platform. If someone who isn't already an Instagram user discovers content they like—a creator they like—they may sign up for Instagram because of it. So yeah, all of this ties back to backlinks and discoverability. It's really about how you use those backlinks. I mean, YouTube has been a huge driver for people looking for answers or trying to learn almost anything. So yeah, that's kind of how it works. It's one big spiderweb. Yes. It’s interesting. So basically, the more content I have and the more content other people post about me in credible sites, whether it’s Reddit, Quora, YouTube, social media, and they all point to my website or web pages, then the more it’s going to be discoverable by AI. That’s kinda makes sense.  You're definitely going to become more discoverable. But again, if it's just “Steve Preda,” that alone may not be valuable unless someone is specifically searching for your name. Now, if people are responding to or discussing how to apply a specific framework—and someone is searching for that framework that relates to your content—then it becomes relevant. Does that make sense?  Yeah. Yeah, understand. Yeah. Absolutely. Let me ask you this. If you could have a magic wand and fix one thing inside your company in the next 12 months, what would that be?  That’s an interesting question. I don’t know. I think I'd be very interested in applying more AI agents so they can help drive the business and support more growth. Overall, I just want healthy growth—making sure we're happy with the work we're doing, and that our clients are happy with the work we deliver. Because that leads to better outcomes, longer-term relationships, and healthier growth for the company. I mean, my ultimate goal at some point is probably to grow the company and eventually sell it. If we're happy with what we're doing, and our clients are happy with the work we're delivering, I think that growth will happen organically. Yeah. And what do you need to make the company sellable in your perspective?  Having strong, scalable systems—and AI is going to help with a lot of that.  So do you believe that a company with only AI employees—at the extreme—could still become a very valuable company? No, I'm not saying we should rely only on AI, and I'm definitely not planning to let go of any employees. What I'm saying is that AI can help with certain smaller tasks that sometimes get missed or forgotten. That's a perfect fit for AI. For example, even during conversations—if a project manager is handling several clients at once—we usually need updates on what was discussed. Yes, AI can record the conversation, but more importantly: what are the actionable next steps? And from those action items, what has already been completed, and what still needs to be done? Those are the kinds of things AI agents can help with—tasks that don't necessarily require a human. That way, time isn't wasted and can instead be used more effectively to make sure things are getting done and that we're reaching the outcome you mentioned earlier. What is your opinion about controlling AI agents? What is the level of risk? Not just about someone maybe doing a prompt injection and kind of hijacking your agents, but losing control of the agents in terms of complexity. So do you see a risk there that someone could kind of unleash these agents and somehow not be able to control them, or the quality of their work? Could they not control that? Or something changes and the agents get impacted—maybe a software update or something like that? Is this a thing, or is that not a concern? I think there should definitely always be guardrails. For example, right now we're building a platform with AI to gather RFPs, review them, score them, and actually create outputs—like the structure of the RFP. But before they get submitted, an actual person reviews them. I think there should always be final approval by a human—unless it becomes such a perfect system. I mean, it's software, right? At a certain point, can something go wrong? Yes. Especially with updates—unless you own the full process from beginning to end. Yeah, I think there's always a risk, but there's always a risk with software.  There should definitely be some guardrails, no doubt about it. I don't think it should be the last step before a human approves it and actually—for this RFP example—submits the response to whatever platform. I think a human should always review and approve it to make sure everything is working properly. But I think you can save a lot of time. For example, instead of us doing two or three RFPs a month, we can do ten or fifteen. I mean, the quality isn't really changing. It's structure. It's answering what they're asking for. So if it fits the criteria we're looking for, we still spend time reviewing it. I mean, we got an RFP the other day that was 150 pages. It would probably take two days just to read it. And at a certain point, you're like, “You know what? This isn't a good fit.” So it saves time. It just creates more efficiency. But there should definitely be guardrails and structure for sure, and a human should be involved in the loop. That I agree with you on. Okay. It's a big topic. One of the thoughts is that at some point AI is talking to AI. Like in hiring—you see these big recruiting companies using AI to filter resumes, and then applicants use AI to write resumes that fit what the filters are looking for. And at some point, the authenticity or credibility of those resumes begins to fade because it's all prearranged. So then the whole purpose of filtering employees starts to diminish. Do you think this kind of thing might happen with RFPs too? Maybe. Very possible. I wouldn't be surprised if it's not happening already. Yeah, I mean, it's definitely very possible. There are already several platforms that find RFPs. They work a little differently. We're building specifically for our own purpose. I do want to document the process to kind of show, “Hey, here's what can be done.” But yeah, it's very possible, for sure. Listen, if you're relying on a regular process to get a job, then you're probably not going to get the job. There are a lot more people looking for work right now. I don't know if you heard about Microsoft—and I think Tesla too—but companies are letting people go left and right. Microsoft is offering long-term employees buyouts. And by long-term employees, I mean people who are probably older and maybe not as knowledgeable or experienced with AI.  It's like, “Hey, let us buy you out so you can retire a little earlier.” So this is happening. If you're going through the same regular hiring process as everyone else, you're competing against 500 or 1,000 other people for the same job. Obviously, it's an employer's market right now, not an employee's market. If you're trying to get a job, it shouldn't just be through the regular process. It should be through people you know. Networking is going to have even more value. Personal connections matter, and people knowing, “Hey, this person actually spoke to me the right way.” You should also know how to use AI, because that's going to give you an edge in getting a job. But actually speaking to someone should happen through networking and connections. Yeah, that's my feeling too—that human interaction is actually going to increase dramatically in value. Because authenticity… that's really the only way to verify authenticity: being face-to-face with someone, a real physical person. That's fascinating. Yeah. But I'll tell you—like I said, I post videos on a regular basis. My mom asked me the other day, “Max, are you using AI, or is it really you?” I said, “No, it's really me. It's not AI.” So it's funny because AI is getting so good that you're not always sure what's real anymore. And even with RFPs—it's not just about submitting proposals or resumes. Personal and human connection is going to become more valuable than ever. If I personally knew every buyer putting out an RFP, I'd rather talk to them directly, one hundred percent. Because it becomes a completely different process.  Yeah, that's spot on. Love it. So, great information. I love the framework: define the problem, determine the audience, create a prototype, build the MVP, test it, and then iterate. That's how you build a digital product—whether it's a website or an app. So if you're out there looking for a solution, Max Kryzhanovskiy and MOS Creative may have the solution for you. So if people would like to connect with Max Kryzhanovskiy and MOS Creative, where can they reach you? People can reach us through our website: www.moscreative.com. They can also find me on LinkedIn under Max Kryzhanovskiy or MOS Creative. They can fill out a form on our website or email us at info@moscreative.com. Fantastic. So if you want an AI-driven platform, definitely reach out to Max. So Max, thank you for coming and sharing your ideas. And I love that you have such a strong vision for AI and that you're actively experimenting within your company, which means your clients will benefit from that as well. And if you enjoyed this conversation, then stay tuned, because every week a successful entrepreneur comes on the show and shares their ideas and frameworks. So thanks for coming, Max—and thank you for listening. Thank you. Important Links: Max's LinkedIn Max's website Max's email: info@moscreative.com

AI for Designers is officially open for enrollment for just a couple of days (or until it's sold out)- In this special episode of Future of UX, I'm answering all the biggest questions about my live bootcamp AI for Designers.We talk about:how the bootcamp workswhat's includedhow much time you actually needAI workflows for designersvibe coding, AI UX, research, prototyping & content generationthe live workshops and communitywhy AI skills are becoming essential for designersI also share more about this cohort's exclusive extras, including:an exclusive Figma collaboration workshopan AI Leadership workshop with Penny Blackmoreupdated workflows, assignments, and live sessions

What if the most important skill for building AI products has nothing to do with evals, technical background, or knowing how to write a prompt? What if it is the ability to design systems that can handle what you never planned for?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Apurva Garware, who has built and scaled products across Amazon, Microsoft, and Upwork, to make the case that systems thinking is the defining skill of the next era of product management. Apurva explains why non-determinism forces PMs to stop thinking in features and start designing the guardrails, agent contracts, and escalation points that govern how a system behaves at runtime, when no one is watching. They explore a three-phase framework for governing AI systems across design, deployment, and production; heuristics for deciding what to hand to agents versus escalate to humans; and a sharp insight about the two products every AI-native company is actually building: the customer-facing product, and the internal operational system that drives margin and velocity. Marc and Ben also share their own experience calibrating an agentic workflow at Supra, grounding the conversation in practice.If you are a PM trying to find your footing in the AI era without a deeply technical background, a founder wrestling with when to reach for AI versus simpler deterministic automation, or a product leader who wants to build more discipline into how your team ships AI products, this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Ryan Burke, VP of Worldwide Sales at Crogl, joins Sam Jacobs, AJ Bruno, and Asad Zaman on the new economics of enterprise cyber risk. Topics include Anthropic's Mythos model, AI for the security operations center, why vibe-coded apps are far more likely to have security issues, why Claude Design tanked Figma's stock, and what the Elon Musk versus OpenAI lawsuit signals for AI governance. Key takeaways: AI has crashed the cost of running sophisticated attacks, putting nation-state-grade tooling in the hands of low-skill operators. As Ryan Burke, VP of Worldwide Sales at Crogl, put it on Anthropic's Mythos model: "Mythos has lowered the cost to like the dollar menu equivalent of...running an attack...so more people can do it." Enterprises are staring down a multi-year patching backlog that runs from now until the end of time. Non-technical teams in finance, ops, and HR are shipping internal tools using Replit and Claude, and almost none of them are securing what they build. Ryan Burke flagged the research: "vibe-coded software is almost 3 times as likely to have security issues." When the employee who built the agent quits, the agent stays behind with no owner, no documentation, and quiet access to systems it never should have had in the first place. For founders eyeing an exit, security has joined revenue, IP, and hitting your numbers as a non-negotiable diligence pillar. As Ryan Burke explained: "lack of security can kill an acquisition...a fourth pillar now is you're secure." Acquirers like JPMorgan Chase will not buy a fintech startup that turns into a vector for attackers to walk straight into their environment. The market case for NRR-fortress legacy SaaS may be weaker than the last decade made it look. As Asad Zaman, CEO of Sales Talent Agency, argued: "there was a generation of software companies that had signs that they had really good customer relationships...but their customers felt more like prisoners." If AI makes switching cheap and a new generation of software actually delights users, the moats around system-of-record incumbents start to compress fast. Connect with the hosts and guest:  Host: Sam Jacobs, CEO at Pavilion - https://www.linkedin.com/in/samfjacobs/  Host: AJ Bruno, CEO at QuotaPath - https://www.linkedin.com/in/ajbruno3/  Host: Asad Zaman, CEO at Sales Talent Agency - https://www.linkedin.com/in/azaman1/  Guest: Ryan Burke, VP Worldwide Sales at Crogl - https://www.linkedin.com/in/ryan-burke-bos/ Topline is more than a YouTube Channel:  Subscribe to Topline Newsletter: https://toplinemedia.substack.com/  Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast  Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters:  00:00 Introducing Ryan Burke 03:14 Anthropic Mythos and Cyber Risk 04:20 How Attackers Use AI at Scale  07:00 Dollar Menu Attacks Explained  10:41 AI for the Security Ops Center  14:53 Why Claude Tanks Figma's Stock  18:30 Sam's Advice on Falling Stocks  20:50 Are Legacy SaaS Companies Back?  24:04 The Vibe-Coding Risk Surface  27:56 Quiz Pro: Cybersecurity Edition  33:46 Replit Apps Inside Enterprises  40:18 Security as the M&A Fourth Pillar  44:17 Personal Data and Digital Legacy  47:24 Bulls vs Bears: Elon vs OpenAI  52:03 Will ServiceNow Hit $32B?

In this episode of the Grow A Small Business Podcast, host Troy Trewin interviews Gulliver Moore shares how he scaled Sunday Treat alongside his co-founder Oliver Clubb from a freelance director/DP partnership into a £2M creative agency serving global brands like Google, Disney, and Revolut. In this episode, Gulliver reveals how consistent 20% year-over-year growth, smart hiring, strong company culture, and high-performing video marketing helped the agency expand to a 14-person team with clients across the US and Europe. He also explains how Sunday Treat is adapting to AI, building viral content strategies, and maintaining creativity while scaling fast. Gulliver shares honest lessons about leadership, delegation, difficult management decisions, and why founders should never delegate hiring. The conversation is packed with insights on business growth, branding, team building, and creating a sustainable agency in today's competitive digital landscape.  Why would you wait any longer to start living the lifestyle you signed up for? Balance your health, wealth, relationships and business growth. And focus your time and energy and make the most of this year. Let's get into it by clicking here. Troy delves into our guest's startup journey, their perception of success, industry reconsideration, and the pivotal stress point during business expansion. They discuss the joys of small business growth, vital entrepreneurial habits, and strategies for team building, encompassing wins, blunders, and invaluable advice. And a snapshot of the final five Grow A Small Business Questions:  What do you think is the hardest thing in growing a small business? Gulliver Moore shared that the hardest part of growing a business is consistently delivering on big promises while maintaining quality and trust with clients. What's your favorite business book that has helped you the most? Gulliver Moore said his favorite business book is Radical Candor because it deeply influenced how he manages people, gives feedback, and builds an honest company culture. He also highly recommended The Making of a Manager for its practical advice on leadership and team management. Are there any great podcasts or online learning resources you'd recommend to help grow a small business? Gulliver Moore recommended podcasts like Hard Fork, The Vergecast, and Today in Focus to stay updated on technology, AI, and current events. He also emphasized learning through experimentation, especially with AI tools, social media content creation, and hands-on business experience rather than relying heavily on formal coaching or consultants. During the conversation, host Troy Trewin additionally recommended Marketing School, Uncensored CMO, and Everyday AI for marketing and business growth insights. What tool or resource would you recommend to grow a small business? Gulliver Moore recommended using Claude AI for brainstorming, strategy, copywriting, and improving workflows with AI. He also highlighted Monday.com as a powerful CRM and project management system for organizing teams and client work, while Figma was his preferred platform for creating visually engaging presentations and creative assets. He emphasized that combining strong systems, consistency, and AI tools can significantly improve productivity and business growth. What advice would you give yourself on day one of starting out in business? Gulliver Moore said he would tell himself to trust the process, stay patient, and focus on consistently hiring great people. He emphasized that long-term success comes from building a strong team culture, trusting your instincts during hiring, and sticking with the journey even when growth feels slow or uncertain. Book a 20-minute Growth Chat with Troy Trewin to see if you qualify for our upcoming course. Don't miss out on this opportunity to take your small business to new heights! Enjoyed the podcast? Please leave a review on iTunes or your preferred platform. Your feedback helps more small business owners discover our podcast and embark on their business growth journey.     Quotable quotes from our special Grow A Small Business podcast guest: Hiring the right people is the most important investment you can make in your business — Gulliver Moore You don't need to control everything — great teams do amazing work when you trust them — Gulliver Moore Consistency in your process will eventually create the growth you're looking for — Gulliver Moore       

Change management decides whether your MES or digital transformation project lasts, or quietly gets shut off six months after go live.Vlad Romanov and Dave Griffith sit down with Logan Terry, who leads digital transformation at LSI, to dig into change management as the deciding factor in any automation or MES rollout. Logan defines change management as a methodical approach to moving an individual, team, or organization from a current state to a desired future state. The closer a system sits to where decisions are actually made, the more change management it requires, which is why MES is the single hardest place to land a project successfully.Much of the episode digs into why change management is rarely scoped properly. In competitive RFPs, the integrator who includes a robust change management line item often loses to the lowest bid, and end users frequently do not know how to evaluate that line item even when it is offered. Logan starts every client engagement with a direct question: what does your continuous improvement practice look like internally? If the client cannot sustain the change after handover, the project is on borrowed time no matter how clean the FAT and SAT looked.Logan walks through one of the most useful failure stories on the show this year. His team delivered a technically perfect OEE dashboard for a production line. Six to nine months later, every terminal was shut off. The postmortem surfaced two missed details. Maintenance was never folded into the design, and a single failed photo eye broke throughput calculations with no manual reconciliation path, which destroyed operator trust in the data. The second miss was behavioral. Showing a 30 percent OEE against a 90 percent ideal demotivates the floor, while reframing the same number as 80 percent of a realistic 36 percent target turned out to be a cleaner motivator.Looking forward, Logan sees vendors moving away from monolithic 14 function MES suites toward modular, use case specific deployments, which compresses change management scope from twenty five workflows to five or six. On AI, he argues that managing generative agents in production is closer to managing a team of people than managing software, with continuous validation replacing one time qualification. He cites the line that AI does not make bad data worse, it makes it more convincing. LSI now uses AI assisted coding agents and React based prototypes to shrink design cycles from three or four weeks of Figma work down to three or four days.About Logan TerryLogan Terry leads digital transformation at LSI, a multinational systems integrator with roughly 400 resources across 13 North American locations and offices in Asia Pacific. A mechanical engineer by training, Logan spent a decade in PLC, HMI, and SCADA development before moving into digital transformation consulting and joining LSI in late 2024. His work spans advanced SCADA, MES, analytics, and BI integrations.LSI: https://www.logicalsysinc.com/Timestamps0:00 Introduction2:15 Logan's background and the LSI digital transformation practice7:25 Defining change management9:00 Why MES requires the most change management13:00 How young engineers stumble into change management24:30 Starting with decisions and workflows before technology35:00 Internal CI capability as a project gating factor43:30 OEE dashboard turned off six months after go live46:30 Behavioral psychology of how operators read numbers54:50 Modular MES replacing monolithic platforms58:00 Generative AI and continuous validation1:11:00 AI assisted prototyping shrinking design cyclesAbout Your HostsVladimir Romanov is a co-host of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to de-risk modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladimirromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Digital Transformation in Manufacturing: https://www.joltek.com/blog/digital-transformation-in-manufacturingManufacturing Execution Systems and Business Strategy: https://www.joltek.com/blog/manufacturing-execution-systems-business-strategyDave Griffith is a co-host of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub

There's a scene in the Steve Jobs biopic where Steve Wozniak asks Jobs what he actually does. Wozniak understood his own role clearly: he was an engineer. He wrote code. He built things. But Jobs? Jobs described himself as the conductor of an orchestra. I've been thinking about that exchange a lot lately, because I think it captures exactly where we're all heading. AI isn't turning us into supercharged doers. It's turning us into conductors, and that requires a completely different mindset. The problem nobody talks about I've been coaching a number of people on integrating AI into their workflows recently, and I keep running into the same pattern. The people who aren't getting time savings from AI aren't failing because they don't understand what it can do. They're not failing because they lack access to the right tools. They're failing because they're fundamentally disorganized. AI is only as useful as the foundation it's built on. If your work processes are messy, your context is scattered, and your task management is a loose collection of mental notes and sticky tabs, AI can't do much for you. It needs structure to work from. I hear this complaint constantly: “AI has been mis-sold to me. I'm not saving any time.” But it hasn't been mis-sold. It's just that AI can only deliver on its promise if there's an organized workflow underneath it. Build that first, and the time savings follow. That's why I've written before about building AI playbooks and developing proper AI skills. These aren't nice-to-haves. They're the infrastructure that lets AI actually work. The conductor problem But here's the deeper shift, the one that's genuinely harder to adapt to. When you're doing tactical work, you're usually focused on one or two tasks at a time. You go deep, you finish a thing, you move on. It's cognitively manageable. A conductor doesn't work like that. A conductor holds the entire orchestra in mind simultaneously: what the strings are doing, where the brass comes in, what the percussion is building toward. They're not playing any of the instruments. They're managing the relationships between all of them. In a world of AI agents, we're going to be managing multiple projects running in parallel, all moving faster than any human team would. We're task-switching constantly. We're accountable for outputs we didn't directly produce. And we have to resist the urge to dive in and do the work ourselves, because that's precisely where we get bogged down. The design leader parallel This isn't a new challenge, as it happens. Design leaders face exactly this transition when they move from senior practitioner to managing a team. I've watched a lot of talented designers struggle with that shift. They get promoted because they're brilliant at the work, and then they spend the next year quietly sneaking back into Figma because they can't let go of doing. They micromanage their reports. They redesign things that were already fine. They can't operate at the level of abstraction that leadership requires. Working with AI agents is going to feel very similar. The temptation to wrestle with the AI until it produces exactly the output you had in your head, rather than accepting a good result and moving on, is going to be real. Learning to let go of that control is a skill in itself. The good news is that unlike a team of designers, you can't upset an AI agent by micromanaging it. But you can waste enormous amounts of time doing it, and that defeats the whole point. AI burnout is already real There's one more aspect of this I want to flag, because I don't think it gets talked about enough. When you're managing a team of agents all moving at AI speed, the cognitive load is significant. You're context-switching constantly across multiple workstreams. Things are completing faster than you can review them. It's relentless in a way that managing a human team simply isn't. This is what's increasingly being called AI burnout. Learning to pace yourself, to batch your reviews, to build in breathing room: these are the organizational skills that will separate people who thrive in an AI-augmented world from those who burn out in it. Where to start If I had to distill this to one practical thing: start building the habits of a manager now, before the agents fully take over. Get organized. Build the infrastructure that AI needs to work from. Practice delegating, even to imperfect tools, rather than doing everything yourself. Work on your ability to hold multiple projects in your head without losing the thread on any of them. If you want help working through that transition, I offer coaching specifically for this. It's something I'm increasingly focused on, because I think it's one of the most valuable things I can help people with right now. I'm also running a workshop with Smashing Magazine in July. Modern UX Practitioner covers a lot of this ground in a more structured way, if that's more your style. The shift from doer to conductor is coming whether we prepare for it or not. The people who handle it best will be the ones who start thinking like managers now.

We hit the Centennial! In our 100th episode, Davy and PJ look back on 4 years of our design systems chatter, before diving deep into the future of design systems: a community-driven, machine-readable schema that moves us past the "Figma-as-Source-of-Truth" era.

What happens to the product development process when the lines between who builds, who designs, and who decides start to disappear?In this special episode of Supra Insider, recorded as part of the Blurring Lines series with Aster AI, Ben Erez sits down with Rishin Banker, VP of Product at Maven, to explore how a 25-person team is rethinking product development in real time. Rishin opens with a concrete shift: Maven went from two to three concurrent projects to five to six, same headcount, smaller pods, more decision-making at the team level. The unlock wasn't hiring. It was front-loading strategy so more people could move into the build phase at once.They explore how Maven's head of design shipped a full marketing page to production end-to-end, why months of foundational design system work made that possible, and where Figma still fits. Rishin also gets into the tensions he's navigating, unexpected handoffs, competing priorities when people build in silos, and the difference between projects that can live in their own container versus ones that need specialist input from the start.If you're a product leader restructuring your team for the current moment, a designer or PM excited about building more but unsure how to navigate the role blurring, or curious how a lean startup is actually operationalizing these changes day to day, this episode is for you.A special thanks Alex Pavlou and our friends at Aster AI for hosting this session!All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

Before UX, before Figma, before any of this . Patricia studied biology. And that lens changes how she sees what's happening in design right now.Because this isn't just another tech shift.It's evolution.In this episode, she breaks down why AI isn't just changing tools or workflows — it's creating a real divide between designers who adapt and those who don't. And why this moment feels less like a trend… and more like a sorting process.What you'll learn in this episodeWhy AI in design is not a “tool shift” but an evolutionary momentThe real meaning behind Darwin's idea of adaptation — and why it applies to UX todayWhy you're not competing with AI, but with a version of yourself that uses it betterHow companies like Stripe, Notion, and Figma are already working differentlyThe growing gap between designers experimenting with AI… and those fully rebuilding their workflowsThe 3 patterns of designers who are actually adaptingThey redesign their workflowsNot just adding AI as a side tool — but rethinking how work gets done from the ground up.They move up to orchestrationLess time producing. More time deciding, directing, and shaping outcomes.They build instead of briefMoving from static specs to real, working prototypes using AI-powered tools.Why this mattersAdaptation compounds.The difference between starting now vs. waiting isn't linear — it accelerates. Over time, it creates entirely different kinds of designers.This isn't about being better or worse.It's about moving… or staying still while everything else shifts.3 things you can try this weekRebuild one part of your workflow with AI at the center (not on the side)Study real workflows (not tutorials) — for example from “How I AI”Build one prototype in code instead of Figma using tools like Claude Code or v0AI for Designers: 5-week Bootcamp

Elad Gil (@eladgil) is CEO of Gil & Co, a multi-stage investment firm, holding company, and operating company working on the world's most advanced technologies. Elad is a serial entrepreneur, operating executive, and investor or advisor to private companies, including AirBnB, Anduril, Coinbase, Figma, Instacart, OpenAI, SpaceX, and Stripe. He was previously VP of Corporate Strategy at Twitter and started mobile at Google. He was the founder and CEO of Mixerlabs and Color. Elad is the author of the bestseller High Growth Handbook: Scaling Startups from 10 to 10,000 People.This episode is brought to you by:Matic the intelligent robot vacuum and mop that navigates obstacles and needs no babysitting: MaticRobots.com/TimAG1 all-in-one nutritional supplement: DrinkAG1.com/TimEight Sleep Pod Cover 5 sleeping solution for dynamic cooling and heating: EightSleep.com/Tim Helix Sleep premium mattresses: HelixSleep.com/TimTimestamps[00:00:00] Start.[00:02:21] What's the “AI personal IPO” that just quietly happened across Silicon Valley?[00:05:28] Tens to hundreds of millions per researcher: What top AI pay packages actually look like.[00:06:44] The compute ceiling: Why Korean memory fabs are the unlikely bottleneck throttling every AI lab on earth.[00:11:11] From zero to $30B run rate: The fastest revenue ramps in the history of capitalism.[00:17:24] The dot-com survival rate was one in 100. Buckle up, AI founders.[00:20:35] Your value-maximizing window: Why the next 12–18 months may be as good as it gets.[00:21:32] Durable advantage — and why the AI market is an oligopoly (for now).[00:24:12] Exit options for AI founders: labs, hyperscalers, vertical players, and the underrated merger of equals.[00:28:11] Math, biology, and intuitive leaps: Elad's pre-investing background.[00:29:42] Elad's revisionist genesis story.[00:30:50] Go where the cluster is: 91% of global AI private market cap lives in a 10×10 mile square.[00:33:20] The accidental investor: Patrick Collison walks, Airbnb intros, and deals that just happened.[00:34:37] Want money? Ask for advice. Want advice? Ask for money.[00:35:00] The High Growth Handbook: Tactical guide, not bedtime reading.[00:35:41] Market first, team second — with a Perplexity-and-Anduril asterisk.[00:37:43] Smoke in the distance: AlexNet and the transformative GPT-3 moment.[00:45:15] AI cold-reading: Feeding photos to the model and getting eerily accurate personality reads.[00:48:56] Has Elad ever done a retrospective on his own investing?[00:52:13] Power laws are terrifying: 10 companies, 80% of returns, two decades.[00:55:53] Avoiding science projects, and how SPACs accidentally saved hard tech investing.[00:59:20] The one-belief framework: Coinbase = crypto index. Stripe = e-commerce index. That's the whole memo.[01:00:54] Due diligence theater vs. the one question that actually matters.[01:02:13] The four-year vest is a relic: How venture capital ate growth investing.[01:07:16] Boards as in-laws: You can't fire them, so choose wisely.[01:09:47] “Valuation is temporary. Control is forever.” — Naval Ravikant, as quoted by Elad, as relayed to you.[01:11:30] How great companies actually grew: toolbars, name-targeted ads, and billions in distribution spend.[01:15:36] Selling software vs. selling labor hours: The real shift generative AI made.[01:18:40] Spotting a great market: regulatory shifts, technology shifts, and Hashi getting bought by IBM.[01:21:28] Fake TAM, real TAM, and the Coke CEO who realized he wasn't in the soda business.[01:22:47] Right now, consensus is just correct. Save the contrarianism for later.[01:25:15] Market entry vs. market disruption: SpaceX launched rockets, then disrupted the internet.[01:26:16] How Elad learns: X, papers, 20-minute calls with the right people — and four AI models running in parallel.[01:27:15] Deep dive: ADHD, autism, and why diagnostic rates soared without more people actually having it.[01:33:40] Longevity for realists: sleep, creatine, and maybe rapamycin when the real drugs arrive.[01:40:30] Ibogaine, anesthesia, and the next frontier of bioelectric medicine.[01:45:15] Elad's first-ever 10-year plan — and why making one changes everything.[01:46:53] Parting thoughts.*For show notes and past guests on The Tim Ferriss Show, please visit tim.blog/podcast.For deals from sponsors of The Tim Ferriss Show, please visit tim.blog/podcast-sponsorsSign up for Tim's email newsletter (5-Bullet Friday) at tim.blog/friday.For transcripts of episodes, go to tim.blog/transcripts.Discover Tim's books: tim.blog/books.Follow Tim:Twitter: twitter.com/tferriss Instagram: instagram.com/timferrissYouTube: youtube.com/timferrissFacebook: facebook.com/timferriss LinkedIn: linkedin.com/in/timferrissSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

The NIA boys discuss Adobe vs. Figma vs. Canva vs. Claude DesignTimestamps(00:00:00) - Intro(00:03:28) - Adobe vs Figma breakdown(00:14:03) - Canva(00:17:12) - Claude Design(00:23:26) - Claude Design will eat Figma(00:26:33) - Claude Design Demo(00:36:54) - Positioning of Canva vs Figma vs Adobe(00:54:27) - Jack's Design Stack What Is Not Investment Advice?Every week, Jack Butcher, Bilal Zaidi & Trung Phan discuss what they're finding on the edges of the internet + the latest in business, technology and memes.Subscribe + listen on your fav podcast app:Apple: https://pod.link/notadvicepod.appleSpotify: https://pod.link/notadvicepod.spotifyOthers: https://pod.link/notadvicepodListen into our group chat on Telegram:https://t.me/notinvestmentadviceLet us know what you think on Twitter:http://twitter.com/bzaidihttp://twitter.com/trungtphanhttp://twitter.com/jackbutcherhttp://twitter.com/niapodcast Hosted on Acast. See acast.com/privacy for more information.

Presales teams are being asked more and more to do post-sales work. This isn't a fad or something we're doing in the short term. This is a significant change for the broader profession of presales and solutions consulting. What was once occasional support for implementations or customer renewals is now becoming formal job responsibilities for solutions consultants across the industry.   In this episode, Jack Cochran sits down with Shamil Turner, Global Technical Solutions Leader at Figma and Presales Collective advisory board member, to explore why this shift is happening and what it means for the future of presales.   Shamil brings a unique perspective to this conversation: he was the first SE hired at Figma, built their entire solutions engineering organization from the ground up, and has now transitioned into a post-sales technical leadership role. Together they discuss the strengths that SEs bring to the table that has a profound impact on a company-customer relationships AFTER the sale has happened, and how this plays out in a new role that has been emerging over the past year, the Forward Deployed Engineer. Whether you're a leader making organizational decisions or an IC just getting started, this conversation will help you understand how the world of solutions is evolving. Thank you to Saleo for sponsoring this episode! Follow Us Connect with Jack Cochran: https://www.linkedin.com/in/jackcochran/  Connect with Shamil Turner: https://www.linkedin.com/in/shamil-turner/  Links and Resources Mentioned Join Presales Collective Slack: https://www.presalescollective.com/slack  Sol/Con 2026 (Chicago, August 2026): https://www.presalescollective.com/solcon-2026  Presales Collective Podcast: https://www.presalescollective.com/podcast Saleo: https://saleo.io  Key Topics Covered The Growing Trend of Presales Teams Doing Post-Sales Work Historical Context: How SEs Have Always Supported Post-Sales Forward Deployed Engineer Roles in Consumption-Based Models Why Presales Skills Are Valuable for Customer Onboarding Shamil's Journey from First SE at Figma to Global Technical Solutions Leader The Convergence of Pre and Post-Sales Functions Career Implications for Presales Professionals at All Levels What This Means for the Future of Solutions Organizations  

The AI model that was too dangerous to release just got breached. Anthropic entered the design software market. And OpenAI dropped its biggest model yet, just six weeks after the last one. This week, the NSA is using Anthropic's Mythos despite the Pentagon blacklisting the company, Claude Design takes on Figma and sends its stock down 7%, Yelp transforms into an agentic consumer app, Mythos gets accessed by an unauthorized Discord group, and OpenAI fires back with GPT-5.5. If you are a founder, operator, or executive trying to keep up with AI, this is your weekly five-minute briefing every Tuesday. Stories Covered This Week: NSA uses Anthropic's Mythos Preview despite the Pentagon declaring the company a supply chain risk Anthropic launches Claude Design, a prompt-to-prototype design tool that sent Figma stock down 7% Yelp's upgraded AI assistant can now book restaurants, doctors, and more in one conversation Anthropic investigates unauthorized access to Mythos through a third-party vendor environment OpenAI releases GPT-5.5, scoring 88.7% on SWE-bench with a 60% drop in hallucinations vs GPT-5.4 Episode Timestamps: 00:00 Intro 00:20 NSA uses Anthropic's Mythos despite Pentagon blacklist 01:10 Anthropic launches Claude Design 02:00 Yelp's AI assistant goes full service 02:50 Anthropic investigates Mythos breach 03:40 OpenAI drops GPT-5.5 04:30 Outro Partner Links Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: www.theaireport.ai/leaders-launch-guide Learn more about your ad choices. Visit megaphone.fm/adchoices

BONUS: AI Won't Just Change How You Work — It Will Reshape Your Organization The Global Agile Summit is around the corner, and the AI in Organizations track is one you don't want to miss. In this episode, track co-hosts Michael Dougherty and Michał Parkoła walk us through what they've built — from the thinking behind the track name to the sessions that stood out, and why this isn't just another AI conference lineup. Why "AI in Organizations" — Not Just "AI" "AI will not only be useful to existing organizations, but it will reshape organizations in a very significant way, the same way cars reshaped cities."   Michael and Michał drew a deliberate line with the track name. Michael points out that AI has been around for decades — it didn't start with ChatGPT. The real shift now is AI agents scaling to enterprise level, replacing automation that used to require specialized tools. Claude Enterprise holds about 29% of the enterprise AI market, Gemini around 15%. But Michał pushes the framing further: the first-order effect is applying AI to existing work. The second-order effect — the one he's most interested in — is how AI will reshape organizations themselves. New species of companies will emerge, smaller teams will achieve what used to require hundreds of people, and some existing organizations won't survive the transition. That's the conversation this track is designed to start. Filtering the Signal From the Slop "There was a bit of AI slop in the submissions. There was a lot of talk that, unfortunately, was meta-talk — there was no real value that I could glean."   When session submissions came in, Michael was disappointed by how many were surface-level — big promises with no practical takeaway. The ones that stood out were practitioners showing what they actually do. Dave Westgarth, for example, demonstrated how he uses AI with Lovable and Claude embedded in Miro whiteboards to enhance real team interactions. On Michał's side, the standout was Max Pirata, who challenged the "vibe coding is slop" narrative. His argument: the quality of large-scale software has never depended on the infallibility of individual engineers — it depends on disciplined engineering processes. The same applies to agentic engineering. Your first attempt at vibe coding will be rough, but there are ways to apply engineering discipline to AI-assisted development. That's what Max will be talking about at the summit. Prototyping at the Speed of Thought — And the Human Bottleneck "Now I've got 20 prototypes that I can choose from. Which ones are the best? Which ones do I need to clear out? Product managers now have a different game they play."   Two sessions capture opposite sides of the AI-in-organizations tension. Dave Westgarth's "Vibe UX: Prototyping at the Speed of Thought" shows how vibe coding lets you build full working systems instead of Figma mockups — so fast that the bottleneck shifts from creation to selection. Product managers and product owners now face a new challenge: clearing the closet of AI-generated options rather than validating a single bet. On the other side, Shawn Wallack's session — "Even With AI, Your System Will Never Be Better Than Its People" — brings the counterpoint. Michael explains the systems-thinking angle: AI does what you tell it, fast and accurately, but that speed reveals human bottlenecks everywhere else. He shares the cautionary example of AI declining twice the insurance claims humans did, with the human-in-the-loop rubber-stamping instead of actually checking — leading to a class action lawsuit. The lesson: AI doesn't remove the need for human judgment, it makes it more critical. Gojko Adzic on Spec-Driven Development and Building AI Products "True to his roots, he is exploring spec-driven development now, which is one of the popular threads in agentic engineering."   Gojko Adzic — the author of Specification by Example and Impact Mapping — brings heavyweight credibility to the track. Michał reveals that while Gojko is exploring spec-driven development in the context of agentic engineering, the interview focused more on his hands-on experience building his own AI products. For attendees, this means real practitioner insights from someone who literally wrote the book on how specifications drive software quality — now applying those principles in an AI-first world. From Beginner to Builder — Who This Track Is For "My favorite case would be people who will quit their jobs and start new companies that will be able to achieve wonderful things with much smaller teams than we would otherwise imagine possible."   The track is designed to meet people wherever they are. Pierre Beaning covers the basics of using Claude for beginners. Jason Little — who Michael describes as a "techno nerd" and "grand poobah" — shows how to build and scale multi-agent systems for business. The spectrum runs from "I've only used AI to plan a vacation" to "I'm orchestrating agent teams." But Michał's vision for the ideal attendee is bolder: someone who walks away ready to start a company. Michael backs this up with the story of an AI unicorn — $1.8 billion valuation, one guy and his brother, in the pharmaceutical industry, just a few months old. Hype? Maybe. But Michał's pragmatic take lands it: "If you make a few million, even if it dies later, that's not such a bad thing." The goal of the track is to blow away the fog — throw flares into key spots so people can sketch a map of what's possible and decide which areas deserve a follow-up. About Michael Dougherty Michael Dougherty is the Co-author of Shift: From Product to People, leadership coach with 30+ years helping organizations adopt people-centered, agile ways of working. Co-owner of the Global Agile Summit.   You can link with Michael Dougherty on LinkedIn and find out more at shiftingpeople.com. About Michał Parkoła Michał Parkoła is an Agile practitioner based in Warsaw, Poland. Previously hosted the Value-Centric Product Development track at Agile Online Summit 2024. He is building Tapestry, an AI planning assistant.   You can link with Michał Parkoła on LinkedIn and check out Tapestry at growwithtapestry.com.

What if the reason you feel like you never have enough time isn't actually a time problem at all?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Molly Siemers, an executive coach for product leaders who spent over two decades building mission-driven products at companies like Kiva, Change.org, and Blurb before launching her coaching practice, Product Craft Works. Molly opens by naming what she's watching in real time: product leaders are running faster than ever, layoffs are everywhere, and the pressure to adopt AI on top of everything else is creating a new kind of cognitive overload. Coaching, she argues, has never been more necessary, not because people need tactics, but because most people are solving the wrong problem.They explore the difference between time and capacity, why the best senior leaders seem unflappable, and how personal capacity is something you build, not something you find by rearranging your calendar. Molly walks through the integral coaching methodology she trained in, the threshold practice she gives clients to start tapping into body and emotional intelligence, and the three-step framework she keeps returning to: notice, decide, act. The conversation then turns personal, with Marc and Ben reflecting on agency, identity, and what happens when you look around and realize you've built a job you hate, or, on the flip side, a life that actually works.If you're a product leader feeling overwhelmed and can't figure out why, someone navigating a career transition and struggling with identity, or a founder or operator who's curious whether coaching might actually be worth it, this episode is for you.All episodes of the podcast are also available on Spotify, Apple and YouTube.New to the pod? Subscribe below to get the next episode in your inbox

This week, we discuss agents taking over at Google Cloud Next, Apple's new CEO, and Cursor getting acquired (sort of). Plus, Coté's e-waste has no exit strategy. Watch the YouTube Live Recording of Episode 569 Runner-up Titles I love throwing stuff in the trash. Dillo dirt's a thing. BurgerOps. Thomas opens for Richard. Department of “No” people Starfish Stomach Model Enterprise — come into me The Organization will Assimilate it Gold plaques all around He can let his freak flag fly Take the first billion dollar offer Rundown Google Next Welcome to Google Cloud Next26 Google's AI adoption — Steve Yegge X Post Tanzu Platform 10.4: a private cloud platform for AI harnesses (or, "agentic AI") Apple becomes a $4 Trillion under Tim Cook Cursor Watch Cursor in talks to raise $2B+ at $50B valuation as enterprise growth surges There's No Time for SpaceX to Buy Cursor SpaceX says it can buy Cursor later this year for $60 billion or pay $10 billion for 'our work together' Relevant to your Interests Poland street sees humanoid robot chasing boars in unusual AI showcase Someone planted backdoors in dozens of WordPress plug-ins used in thousands of websites Snapchat owner cuts 16% of global staff in latest round of job cuts Email for agents - Cloudflare Email Service now in public beta DeployBar — Free CI monitoring. Unsolicited platypus included. Let them tinker - hacking developer resistance to sound enterprise architecture and platforms China's DeepSeek is raising funds at $10 billion valuation, The Information reports Sources: Cursor in talks to raise $2B+ at $50B valuation as enterprise growth surges AI chipmaker Cerebras files to go public after scrapping IPO plans last year Amazon to invest up to $25B in Anthropic as part of expanded cloud partnership - SiliconANGLE Amazon to invest up to $25 billion more in Anthropic; Claude developer to spend more than $100 billion on AWS AI technology Amazon and Anthropic expand strategic collaboration Anthropic CPO leaves Figma's board after reports he will offer a competing product OpenAI loses multiple executives in latest leadership shakeup Scoop: NSA using Anthropic's Mythos despite Defense Department blacklist The scientific case for being nice to your chatbot Anthropic's redesigned Claude Code desktop app lets you burn through tokens even faster OpenAI's Codex Mac app adds three key features that go beyond agentic coding Introducing Claude Opus 4.7 Anthropic Sponsors WebRTC.ventures – Real-time communication & Voice AI integration WeAreDevelopers World Congress North America Sept 23–25, San José, CA Use Code DEVPOD26 — 15% off, stacks with group rates for 4+ Listener Feedback Subscribe to Failover New Nonsense Struggling shoe retailer Allbirds makes bizarre pivot from shoes to AI, stock explodes more than 700% Allbirds Stock Now Crashing as Reality Sets in About Its Delusional AI Pivot Conferences DevOpsDays Austin, May 5-6, 2026 DevOpsDays + AI Nashville, May 14-15, 2026 KCD Texas, May 15, 2026, use code MEDIA_THANK_YOU for free pass WeAreDevelopers Europe, July 8-10, 2026 Berlin, Coté speaking. DevOpsDays Graz, Sept 4-5, 2026 DevOpsDays Dallas, Sept 28-29, 2026 DevOpsDays Rockies, Sept. 22 – 23, 2026, Discount Code: 26DODSWEDEFTALK WeAreDevelopers NA, Sept 23-25, 2026, Discount Code: DEVPOD26 DevOpsDays Vilnius, Sep 30 - Oct 1. 2006 DevOpsDays Istanbul, October 24th, 2026 - Coté keynoting. VMware User Groups (VMUGs): Toronto (May 12-14, 2026) Dallas (June 9-11, 2026) Orlando (October 20-22, 2026) SDT News & Community Join our Slack community Email the show: questions@softwaredefinedtalk.com Free stickers: Email your address to stickers@softwaredefinedtalk.com Follow us on social media: Twitter, Threads, Mastodon, LinkedIn, BlueSky Watch us on: Twitch, YouTube, Instagram, TikTok Book offer: Use code SDT for $20 off "Digital WTF" by Coté Sponsor the show Sponsor more podcasts with Failover Media Recommendations Brandon: Claude /team-onboarding The Junior Dev Crisis: Who Inherits the Code When AI Does the Work Matt: Resident Alien Coté: and

nFactorial Intelligence - еженедельные новости из мира стартапов и ИИ В новом эпизоде nFactorial Intelligence мы обсуждаем самые горячие новости прошедшей недели из мира стартапов, искусственного интеллекта и бигтеха. Поговорим о потенциальной мега-сделке между SpaceX и Cursor, разберем причины ухода Тима Кука с поста CEO Apple и обсудим, как применять агентные AI-модели в малом и среднем бизнесе.  Мы также поговорим о психологии чемпионов на примере Роджера Федерера, который стал легендой, выигрывая всего 54% очков, и Майкла Фелпса с его методом тотальной визуализации успеха. Кроме того, обсудим упущенный венчурный портфель Сэма Бэнкмана-Фрида на $114 млрд, лекцию гениального математика Теренса Тао в Принстоне об ИИ и вспомним, как Instacart превратил потерю главного клиента в лице Whole Foods в историю огромного роста. Не обойдем стороной и жизнь комьюнити, включая мастер-классы по генерации музыки в Suno в nFactorial Club и свежий выпуск nFactorial Podcast с Нурдаулетом Базылбеком. Этот выпуск будет особенно полезен фаундерам стартапов, инвесторам, IT-специалистам и всем, кто хочет узнать больше о вайб-кодинге, понять тренды венчурного рынка и научиться применять мышление топовых спортсменов в своей карьере. Рекомендации Присоединиться в nFactorial Club - https://hi.nfactorial.club/ - Подписаться на email-рассылку nFactorial Weekly - https://nfactorial-school.kit.com

In this episode, we highlight how Claude Design is creating revenue opportunities in the design landscape. Learn the ways it's competing with Figma and Adobe.

George Kailas, CEO and founder of Prospero.ai, talks about how he sees market dynamics shaping around the AI narrative. He points to headlines surrounding Claude and Figma's (FIG) sell-off to highlight how disruption fears continue to linger. George notes Allbirds' (BIRD) sudden AI pivot as an example of companies chasing the trend to capitalize on tech. ======== Schwab Network ========Empowering every investor and trader, every market day.Options involve risks and are not suitable for all investors. Before trading, read the Options Disclosure Document. http://bit.ly/2v9tH6DSubscribe to the Market Minute newsletter - https://schwabnetwork.com/subscribeDownload the iOS app - https://apps.apple.com/us/app/schwab-network/id1460719185Download the Amazon Fire Tv App - https://www.amazon.com/TD-Ameritrade-Network/dp/B07KRD76C7Watch on Sling - https://watch.sling.com/1/asset/191928615bd8d47686f94682aefaa007/watchWatch on Vizio - https://www.vizio.com/en/watchfreeplus-exploreWatch on DistroTV - https://www.distro.tv/live/schwab-network/Follow us on X – https://twitter.com/schwabnetworkFollow us on Facebook – https://www.facebook.com/schwabnetworkFollow us on LinkedIn - https://www.linkedin.com/company/schwab-network/About Schwab Network - https://schwabnetwork.com/about

What if AI isn't just a tool to plug into your business — but a reason to redesign the entire thing? In this episode, Jeff Mains sits down with Steven Wunker, managing director of New Markets Advisors and bestselling author of AI and the Octopus Organization: Building the Super Intelligent Firm. Steven has been working in AI since 2012 and has advised dozens of Fortune 500 companies on how to unlock real growth through transformation — not just optimization.Steven challenges the "AI magic dust" approach most companies default to — sprinkling AI on top of existing workflows for marginal gains — and makes the case for something far more powerful: using AI to take over entire classes of tasks, redistribute decision-making to the front lines, and redesign how organizations actually work. Whether you're a SaaS founder thinking about your product roadmap or a leader rethinking your org structure, this episode will challenge you to think way bigger.Key Takeaways4:13 — AI is the biggest shift of our lifetimes — bigger than smartphones Steve has been in AI since 2012 and helped launch one of the first smartphones in 1999. He says this is still bigger — not just in breadth of adoption, but in depth: changing strategies, org structures, and roles within companies.7:14 — Stop using AI as "magic dust" Sprinkling AI on top of existing workflows only yields marginal gains. The real transformation happens when AI takes over entire tasks that humans won't do (too tedious), shouldn't do (not the best use of their skills), or can't do (too high volume). That's when organizations must fundamentally rethink how work gets done.9:55 — The Octopus Organization: distributed intelligence in action The octopus has nine brains — one central brain and one in each arm. Each arm can sense, think, and act independently while remaining contextually aware of the whole. That biological model is the blueprint for how AI-powered organizations should be structured: parallel execution, distributed decision-making, and strategic focus at the center.11:25 — Why authority hasn't truly been devolved — and how AI finally changes that For 40 years, leaders have talked about flattening orgs and devolving decision-making. It hasn't happened for two reasons: humans resist giving up authority, and front-line workers have lacked the contextual awareness to make good autonomous decisions. AI solves the second problem — and also gives leaders visibility to veto in near real-time rather than always having to pre-approve.16:48 — Map the "work chart," not the org chart Microsoft calls it the "work chart" — how work actually flows through the organization, cross-functionally, in reality. That's what needs to be mapped and redesigned. Layering AI onto the org chart misses the point entirely. Change happens workflow by workflow, tranche by tranche.26:29 — Three questions every leader must answer right nowHow does the competitive landscape change? (Include DIY and AI-native startups)What makes you special in an AI world?How do you get work done — what behaviors, culture, and structure do you need?32:19 — Everyone in management is now a change manager It doesn't matter how technical your role is — if you have people reporting to you, you must become a change manager. That skill can no longer be confined to a C-suite priesthood. Psychological safety for AI adoption and rethinking how good work is incentivized are critical.32:58 — The LUCK framework for strategic serendipity Derived from workforce survey research, four patterns that separate successful AI adopters:L — Leverage help (stay connected, workflows are increasingly cross-functional)U — Unexpected connections (be open to signals outside the average case)C — Control chaos (build systems to absorb the disruption coming)K — Know what's missing (AI is only as good as its data; humans must fill the gaps)34:57 — Don't chase glamorous AI use cases first IBM's Watson failed spectacularly by targeting cancer diagnoses — the world's best oncologists didn't need it. The win? Recording doctor-patient conversations so doctors can actually practice medicine instead of typing. Low risk, high utility, high return. Start there.38:05 — The most valuable AI use cases are unglamorous Things humans won't do: take notes after every meeting and distribute them. Things humans shouldn't do: type during patient consultations. Things humans can't do: transcribe and summarize 40 simultaneous three-person breakout groups and track individual commitments. AI can do all of this — none of it is flashy, all of it is high-value.28:10 — Build in AI optionality from the start Upwork re-engineered their stack with an AI optionality layer — flexible to swap between small LLMs, large LLMs, agents, or other AI systems. You can't predict where AI goes. Build optionality in. Don't make bespoke bets you can't unwind.Tweetable Quotes"AI has this ability to take over certain tasks entirely — things humans wouldn't do, shouldn't do, or simply can't do at scale. That's when it gets truly transformative." — Steven Wunker"We've been talking about devolving authority and de-siloing organizations since 'In Search of Excellence' in the 1980s. It just hasn't happened. AI finally changes the equation." — Steven Wunker"The octopus is 300 million years old — 70 million years older than the dinosaurs — and it has survived because it is so darn adaptable. We need to be like that." — Steven Wunker"AI magic dust — sprinkling it on top of what you're currently doing — will get you marginal improvements. That's nice. But it won't fundamentally change how organizations work." — Steven Wunker"Don't be Adobe in the face of Figma. That has already played out. It would be very easy for that to play out again in innumerable SaaS markets unless we think transformatively." — Steven Wunker"Every person in any management position is now a change manager. It doesn't matter how specialized your technical skill is." — Steven Wunker"Features are only as good as their adoption." — Steven Wunker"AI is only as good as the data that's in it — so it's the role of the human to think about what's NOT in that AI system that needs to complete the picture." — Steven WunkerSaaS Leadership Lessons1. Redesign the work, don't just automate it. The companies that win with AI aren't the ones that add AI features — they're the ones that fundamentally rethink how work flows through the organization. Map your "work chart" (how work actually happens cross-functionally) and redesign it workflow by workflow. Layering AI on your existing org chart is the surest path to becoming the next Kmart.2. Your installed base is an asset — but only if you act transformatively. Existing SaaS companies have something AI startups don't: data, customer relationships, and deep domain context. That is an enormous advantage — but only if you think transformatively. AI-native disruptors are watching your market. Your data moat only protects you if you use it to reimagine what you build, not just improve what you have.3. Prioritize low-risk, high-utility AI use cases first. Resist the temptation to prove what AI can do with your most complex, high-stakes problem. Start where the utility is obvious and the risk is low. Prove value there. Build trust with customers and your team. Then expand. IBM's Watson at MD Anderson is a $62M cautionary tale. The doctor who gets to practice medicine instead of typing is the win.4. Build optionality into your AI architecture. You cannot predict where AI capabilities are heading. Large models, small models, agents, new paradigms — the landscape is shifting too fast to make permanent bets. Build your product and internal systems with an optionality layer that stays flexible. Businesses that hard-code their AI assumptions will face expensive rebuilds. Those who build for adaptability will compound their advantage.5. Transform your go-to-market alongside your product. AI transformation isn't just a product problem — it's a sales, marketing, and customer success problem. The companies that win aren't just selling software; they're selling a changed way of getting something done. That means customer success becomes more important, not less. Sales cycles involve more change management. Proving economic value requires new evidence. Think Workfront, not the feature-obsessed competitor it acquired.6. Make change management everyone's job. The old model — change management as a C-suite discipline — is dead. In an AI-first organization, every manager at every level must develop the skills to lead people through uncertainty, redesign workflows, and create psychological safety for new ways of working. If you're building or leading a SaaS company, start developing these muscles now — in your leaders, your managers, and yourself.Guest Resourcesswunker@newmarketsadvisors.comBook: AI and the Octopus Organization: Building the Super Intelligent Firm — Available on Amazon in all formats (print, ebook, audio)Book Website:

Claude Design launched April 17, 2026 and it's Anthropic's most designer-relevant release yet. In this episode, Patricia walks through three real experiments: a branded design system integration, an interactive infographic, and a social media carousel. She breaks down what actually works, what doesn't, and what designers need to know before they dive in.Key Learnings:Claude Design produces generic output without a design system load your brand assets firstIt's token-heavy: the Max plan is where it becomes genuinely usefulExports to Canva, PDF, PPTX, and HTML. Adobe and direct Figma integration aren't there yetStill in research preview: no audit logs, no usage tracking treat it as a sandbox, not a production pipelinePrompting is closer to writing a design brief than sending a chat messageAI for Designers: 5-week Bootcamp

In this episode Scott Becker shares a quick look at steep declines for Figma, Lucid Motors, and Klarna, highlighting how once high-flying companies are struggling with sharp losses and market pressure.

In this episode Scott Becker shares a quick look at steep declines for Figma, Lucid Motors, and Klarna, highlighting how once high-flying companies are struggling with sharp losses and market pressure.

Episode 2 of the weekly HyperChange podcast. This week we discuss how XMoney is poised to compete with Robinhood and Coinbase and how this is a great example of how fast things are moving in the era of HyperChange. Companies are getting disrupted in a flash. Stocks like Figma and Adobe are getting crushed as Claude rolls out Design. Does assuming companies will be around in a decade make sense anymore? Also I discuss Tesla's valuation and we do an earnings preview covering everything from Robotaxi to the financials and Tesla Semi. Startup of the week is Pop-up Bagel .. disrupting the bagel industry lol. 0:00 Intro Rambling1:10 XMoney disrupting RobinHood & Coinbase 6:30 AI Is Spooking The Stock Market (Years To Payback)13:44 Elon Musk Pitches UBI (Universal High Income)20:10 How To Value Tesla Stock22:06 Tesla Q1 26 Earnings Preview (Is Elon Sandbagging?)28:23 Optimus V3 Reveal29:39 Robotaxi/Cybercab Updates33:07 Projecting Tesla's Financials36:03 Startup of the Week: Pop Up BagelsMy X:   / gfilche  HyperChange Patreon :)   / hyperchange   Disclaimer: I'm long Tesla & SpaceX & Lemonade, nothing in this show is financial advice.

BONUS: From 3,000 Scripts to 3 Tools - Building AI-Last Software With Conversational AI Pioneer Peter Swimm In this special BONUS episode, Peter Swimm—conversational AI veteran, creator of BotKit (the open-source chatbot framework that powered Slack and Teams bots), and former Principal Product Manager at Microsoft Copilot Studio—shares what 25+ years in tech taught him about working with AI. From his brutal experiment of running an entire business on voice-based AI for a week, to why he treats AI more like R2-D2 than C-3PO, Peter offers a grounded, practical perspective on where AI fits in software development teams. From BotKit to Copilot Studio: A Front-Row Seat to the AI Evolution "We had the number one bot in the Slack app store, because there were only 8 bots, and ours used regex. To show you how far we've come."   Peter's journey into conversational AI started with a newspaper ad and a creative writing background. When Slack launched its API, Peter and BotKit co-creator Ben Brown immediately saw that building bots wasn't just a technical challenge—it was a social and creative one, like writing scripts for plays that interface with people in their daily lives. That insight powered BotKit into becoming the backbone of Slack and Teams bots, and eventually led to Microsoft acquiring the company. Peter spent years inside Microsoft shaping Copilot Studio, working on connectors that bridge the gap between APIs and real-world work. But the experience also gave him a healthy dose of perspective: he can show you slide decks from 2016 that promise the same things today's AI pitches promise, always saying "within 5 years." That pattern recognition shapes his practical, no-hype approach. The 3,000 Scripts Experiment: Why AI-Last Beats AI-First "At the end of the day, if I've been prompting all day, I should have a computer program that works offline, that works without a subscription. Otherwise, I didn't really make anything."   Peter ran a week-long experiment trying to run his entire business using only voice-based conversational AI. The result: 3,000 generated scripts. After static code analysis, he discovered it was really only 5 programs made thousands of times—and those 5 programs were really just 2 or 3 core abilities. He deleted 36 gigabytes of generated code and kept 50 megabytes of what actually worked. This brutal compression led him to an "AI-last" philosophy: build reliable runtime software that works confidently in one click, then use AI only for exploration, connection-making, and creative riffing. The payoff is striking—within 3 weeks of a given application, his team sees a 90% reduction in AI usage in the first week, dropping to 0% within 13 days, because once a computer program does everything you need, you don't need AI anymore. R2-D2, Not C-3PO: How to Think About AI on Your Team "I think of our AI use more like R2-D2 than C-3PO. R2-D2 doesn't talk—bonus points. He doesn't interject his fear. He saves your butt. He's silent until you need him, and visible when you need him."   Peter's Star Wars analogy captures his team's philosophy on AI integration. AI should be like a smarter linter—a quiet, capable tool that handles the boring, repetitive tasks so humans can focus on creativity and shipping. His team treats AI as a "super junior" with infinite time: set it up as if it invented Python, have it write buy-the-book code with unit tests, and then a human reviews and accepts (or rejects) the output. The tooling isn't consistent enough to ship autonomously or commit directly into the codebase—even frontier providers don't fully understand what their models do. The practical benefit is enormous for setup and configuration: what used to be a painful, arcane process of tracking down dozens of AWS or Azure docs becomes a 20-minute "hello world" that's actually a working proof of concept. Your job isn't to become an expert at cloud services—it's to ship product. The Biggest Mistake: Automating Broken Processes at AI Speed "All it does is automate all the mistakes you made, all the way, at AI speed."   When asked about the most common mistake organizations make with AI, Peter is blunt: they port their existing infrastructure into AI-governed systems instead of rebuilding from the ground up. Companies with a self-inflated opinion of their processes think AI is just a million-person force multiplier—so they'll ship faster. But if your process was broken before AI, you'll just generate broken output at unprecedented scale. That 3,000-script experiment proved this firsthand. Peter's recommendation: rebuild from the bolts up. Start with AI-last architecture where reliable, offline-capable software handles the core, and AI is reserved for the edges—filling gaps, translating between systems, and making connections that don't exist yet. SaaS Is Bloated: The Case for AI Transformation Layers "The one thing AI is good at is transforming between boundaries."   Peter's team has been divesting from SaaS providers, replacing the patchwork of middleware subscription plans that forced everyone to copy and paste between CMS, Excel, meeting notes, and email. His approach: product people use Notion, developers use GitHub, and the two cross-sync without needing Jira as an arbitration layer. Everyone tracks work in the tool they already live in. AI's real superpower here is translation—between APIs, between languages, between formats. Peter sees a future where small translation layers between CRUD operations replace the bloated, one-size-fits-all SaaS tools that are "built for 99% of users with generalized features nobody uses." His team also freed themselves from tools like Figma: the designer works in their preferred graphics program, the developer in their preferred IDE, and AI arbitrates the differences. Teams, Velocity, and Reinvesting the AI Dividend "5 to 7 people is still good, because you need a diverse set of people who are intensely focused on certain areas. But they should be allotted that savings in time to ship all the things that get cut."   Peter pushes back on the idea that AI changes the ideal team size. The 5-to-7 person team still works—what should change is what those people do with the time they save. Instead of loading teams onto more projects or increasing portfolio velocity, reinvest the AI productivity dividend into quality: ship with unit tests from day one, ship WCAG-compliant from day one, and stop cutting features to hit deadlines. Version 1.0 should no longer need an immediate 1.1 follow-up. Peter also challenges the notion that AI eliminates the need for experienced practitioners—velocity metrics become meaningless when a 6-week coding plan finishes in 25 minutes. What matters is using the saved time to make software genuinely better. The Future: Demo-First Development and Solid Releases "I can show you a working demo of the thing at the first meeting, and you can pay for it. And then we can make it better than your dreams."   Peter sees AI transforming the consulting and product development lifecycle from "launch, listen, and learn" to "listen, iterate, and launch." As a consultant, he now brings working demos to first meetings instead of $20,000 six-week proposals. Clients see the product in motion and immediately identify improvements—before money changes hands. This shifts the power dynamic: products iterate toward quality before launch, not after. Peter envisions a future where we ship solid releases that iterate in quality, with interfaces that show users only what's relevant to them instead of "90,000 buttons that don't apply to me."   About Peter Swimm   Peter Swimm is a conversational AI veteran with 25+ years in tech — from managing data centers to building Botkit (the open-source chatbot framework that powered Slack and Teams bots), to serving as Principal Product Manager at Microsoft Copilot Studio. He's the founder of Toilville, a consultancy helping businesses build conversational AI solutions.   You can link with Peter Swimm on LinkedIn and visit his website at peterswimm.com.

In this Conversion Monthly, Danny McMillan is joined by Dorian and Matt Kostan (no Sim this episode — he's on holiday) for a live, practical session on building brand-quality design systems fast and for free. Dorian opens with a tight crash course in the three design fundamentals that separate professional Amazon listings from amateur ones: font pairing, grid and layout, and colour theory. He then demos Google Stitch live, building a full design system from a wooden utensil listing in real time. Danny shows a more automated route — using Perplexity to control Stitch autonomously and generate a complete brand kit from just a product title, bullet points, and a reference image. Matt rounds it off with a live Product Pinion split test of the new designs against the original listing — and the results deliver the session's sharpest lesson. The big takeaway: pretty is not enough. Information + design working together is what converts. Key Topics Google Stitch for brand design — Free AI design tool that generates full brand guidelines, font pairings, and mockups from reference images and prompts 3 design fundamentals every seller should know — Font pairing, grid and layout, colour theory with a contrasting action colour Perplexity + Stitch autonomous workflow — Danny demos letting Perplexity control Stitch end-to-end with zero manual input to generate a full brand kit Coolers.co — Free colour palette tool with a visualiser and AI colour bot (Matt) UX and design laws applied to Amazon — Miller's Law, Fitts' Law, Jacob's Law, Occam's Razor translated into listing and brand site decisions Product Pinion live split test — New designed variants vs the original listing, with real shopper results in under 10 minutes Live test result — The original information-heavy image outperformed the prettier redesigns early on; lesson: strip information at your peril Timestamps [00:00] Intro — Danny opens, Sim is out, format overview [00:48] Dorian: Why most Amazon listings lack design consistency [02:00] The 3 design principles: font pairing, grid/layout, colour theory [04:30] Font pairing explained — serif vs sans-serif, how world-class brands use them [07:00] Colour theory — complementary colours plus one contrasting action colour [08:30] Live Google Stitch demo — wooden utensil set, design system generated from brand brief + images [10:00] Stitch output: colour palette, font pairings, layout mockups [12:17] Matt: brand guidelines used to cost $1,000+ — now free in Stitch [13:00] Dorian: live Figma iteration — cleaning up the infographic using new design system fonts [17:00] Matt: information hierarchy lesson — measurements vs benefits on infographics [19:30] Dorian: "mouse text" and anchoring — what to leave in, what to strip out [20:33] Matt: Coolers.co overview — free colour palette generator and visualiser [22:00] Matt: UX/UI design principles applied to Product Pinion and Amazon listings [25:12] Danny: Perplexity + Stitch autonomous brand kit demo — Z Kitchen brand from scratch [27:00] Z Kitchen outputs: design system, A+ content, infographic, lifestyle mockups, packaging concepts [31:00] How to iterate inside Stitch — refine vs reimagine, varying only specific elements, up to 5 variants [36:00] Danny: UX design laws — Miller's Law, Fitts' Law, Jacob's Law, Occam's Razor [40:00] Danny: Typography slides — spacing systems, layout balance, font families [43:32] Dorian: reveals three redesigned variants ready for split test [44:35] Matt: launches live Product Pinion test — 50 shoppers, cooking category targeting [47:33] Live results coming in — original listing leading over new designs [48:00] Dorian: "pretty is one thing but the information has to be there" [49:00] Danny: design and information are two separate layers — both are required [51:30] Product Pinion API + Claude integration teaser [52:36] Final results and wrap-up — test completed in ~10 minutes with 50 real shoppers [53:44] Closing thoughts and Seller Sessions Live preview (26 days out) Key Takeaways Three principles separate professional listings from amateur ones — font pairing (serif + sans-serif), grid and layout (hierarchy: 1, 2, 3), and colour (complementary base + one contrasting action colour). Google Stitch is the best free tool right now for design mockups — unlike image generators (Gemini, GPT), Stitch understands design principles and generates layout-aware mockups you can iterate on. Pretty does not convert on its own — the live test showed the original, information-heavy image outperforming the cleaner redesigns early. Design is a layer on top of strong product information, not a replacement for it. Perplexity can run Stitch autonomously — paste a product title, bullet points, and a reference image; let it loop through Stitch without touching anything; come back to a full brand kit. You can test design variations with 50 real shoppers in under 10 minutes — Product Pinion lets you run image split tests with category-targeted shoppers, get qualitative feedback, and iterate the same day. Nano Banana outputs in Stitch cannot be regenerated — switch to one of the standard models if you need variation or refinement controls. AI gets you to the concept stage fast — use Stitch to generate the direction, then hand to a designer for finishing. Revision cycles and meetings shrink dramatically. Notable Quotes "If everything is important, nothing really is." — Dorian "The hardest thing is to make something simple, elegant, and something that people get instantly." — Dorian "Pretty is one thing, but the information has to be there. I didn't put the information there — and it's not doing well." — Dorian (on live split test results) "Most people don't necessarily know good design, but they know what they like. It's more of a feel — they go, that looks a bit cheap, or that looks really good." — Danny McMillan "It's never been easier and faster to become a world-class brand on design. Plug in your details, get a design guide going, and you can really up your brand in a very short period of time." — Matt Kostan "The breakout brands from the Amazon community — we haven't had enough of them crossing over. Now that gap's closed." — Danny McMillan Resources Mentioned Google Stitch — Free AI design tool; generates brand guidelines, font pairings, mockups, A+ content concepts, and layout variations. Up to 3,000 generations per day (free) Figma — Design tool used by Dorian to pull Stitch outputs and refine layouts manually Adobe Color (color.adobe.com) — Colour palette exploration and complementary colour tool; used in the live demo for the wood/blue beach-forest palette Coolers.co — Free colour palette generator with AI colour bot and real-world visualiser Pinterest — Recommended for browsing font pairing inspiration Nano Banana 2 — Image generation model available inside Stitch; note: regeneration/variation controls don't work on Nano Banana outputs Perplexity — Used to autonomously control Google Stitch via browser automation, building a full brand kit end-to-end from a single prompt Product Pinion — Consumer research and split testing tool by Matt Kostan; image tests with real shoppers, category targeting, results in minutes. Product Pinion API + Claude integration in development. Guest Info Dorian — Design and conversion specialist, Seller Sessions Conversion Monthly co-host Matt Kostan — Founder of Product Pinion, consumer research and split testing for Amazon sellers

Colleen shipped the Geocodio CLI (with a Claude Code agent skill!) and Michele had a nightmare about Figma. That's kind of the vibe this week. We also talk about what it's like building AI tools, when Claude decides to stop working, and which AI tools we reach for day to day. Plus, Michele's organizing a Larabelles event at Laracon US to help more women and non-binary folks feel welcome at tech conferences, and Colleen has some strong opinions about what actually works. Also discussed: Ghosts (BBC version), Ted Lasso, and going to the gym. You know, our normal weird stuff.

In this episode of Run the Numbers, CJ sits down with Praveer Melwani, CFO of Figma, to unpack the financial model behind one of tech's most iconic product-led companies. They cover viral growth, forecasting without a traditional sales pipeline, AI credit pricing, margin tradeoffs, and the metric Praveer believes matters most in the long run: free cash flow per share.—SPONSORS:EY works with high-growth tech companies to navigate the messy realities of scaling—from regulatory requirements to IPO readiness. By helping teams get it right early and often, EY lets founders stay focused on building while reducing risk as they grow. Learn more at https://www.ey.com/techstartupsSpendHound is a SaaS spend management platform built for finance and procurement teams that want visibility and leverage in every deal. By tracking all your software, benchmarking pricing across thousands of vendors, and surfacing contracts and renewals, SpendHound helps you stop overpaying and negotiate with confidence. Trusted by teams at ZoomInfo and Hootsuite. Get started at https://www.spendhound.comBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsAleph is a modern FP&A platform built for teams that want more than another planning tool. By connecting your ERP, CRM, and other systems into one trusted data layer with AI workflows, Aleph helps you move faster with real-time insights. Get a personalized demo at https://www.getaleph.com/runRightRev is an automated revenue recognition platform built for teams that have outgrown spreadsheets and billing tool workarounds. It handles high-volume subscriptions, usage-based contracts, and mid-cycle upgrades, so you can scale without scrambling at month-end. For RevRec that keeps your books clean, visit https://www.rightrev.com/CJRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cj—LINKS: Mostly Talent: https://mostlymetrics.typeform.com/to/cLTxtAsNGuest: https://www.linkedin.com/in/praveer-melwani/Company: https://www.figma.com/CJ: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—TIMESTAMPS:0:00 Preview and intro1:21 Welcome and guest intro2:55 From banking to Dropbox to Figma5:04 Inflection points: building trust early8:36 TAM expansion thinking10:31 Sponsors — EY | SpendHound | Brex13:38 Two-thirds of Figma users aren't designers14:36 Forecasting product led growth16:22 Cohorts and NDR discovery17:39 LTV to CAC philosophy18:57 Product signals for account expansion20:22 How Figma achieved hypergrowth with 90%+ margins22:14 AI expands TAM: time to hit the gas23:22 AI credit pricing model24:50 Sponsors — Aleph | RightRev | Rillet28:13 Outcome based pricing consideration29:00 Where AI margins settle: gross profit dollars30:08 Free cash flow per share as north star31:04 NDR and pricing volatility32:13 Bundled vs. unbundled seats34:50 Being engaged in sales to understand admin pain35:17 IPO day experience36:27 Keeping employees focused beyond the stock price37:19 Employee stock pressure and lockup reality39:37 Kitchen cabinet of advisors41:44 How to ask better questions of advisors41:47 Lightning round41:50 Advice to younger self42:47 Finance software stack44:09 Claude WTF moment: forecasting model throughput45:13 Craziest expense story: the haircut45:59 Credits

Show DescriptionWhat is Sanity and who is it for in the age of AI, managing content with AI, how Simen Svale uses AI agents alongside Sanity, designing a MCP, design with Pencil vs Figma, how Inngest works, and how Simen keeps AI agents busy all day. Listen on WebsiteWatch on YouTubeGuestsSimen SvaleGuest's Main URL • Guest's SocialCo-founder/CTO at Sanity.io Links The Content Operating System for the AI era | Sanity Coding Agents & Complexity Budgets | Lee Robinson Message from Sanity Pencil – Design on canvas. Land in code. AI and backend workflows, orchestrated at any scale SponsorsSanity.ioThe back-end built for AI content operations. Power web, mobile, and agentic applications at scale.

I sit down with Amir, who's back on the pod, and we walk through the full stack of taking a business idea from zero to a validated, A/B-tested landing page in a single session. I use Idea Browser's new MCP integration with Claude Code to pull project context, generate a lead magnet concept, design a landing page in Paper, and then wire up analytics and live experiments through HumbleLytics — all without writing a single line of front-end code manually. We cover the tools, the workflow, and why this stack creates massive arbitrage for marketers and builders right now. Timestamps 00:00 – Intro and Episode Preview 02:30 – Building a Growth Strategy with Idea Browser 06:10 – Designing Landing Pages in Paper 08:38 – Refining Copy, Layout, and Components in Paper 20:06 – Deploying Landing Page and Adding HumbleLytics Analytics 28:38 – Running A/B Experiment on the Headline 32:44 – The Arbitrage Opportunity and Closing Thoughts Links Mentioned: Amir's Agentic Marketing Skill: https://startup-ideas-pod.link/amir_marketing_skill Key Points Idea Browser now connects to Claude Code as an MCP, letting you pull project context, growth strategies, and skills directly into the terminal for building and iterating on business ideas. Paper replaces the traditional Figma-to-developer handoff by letting you design, iterate, and refine landing pages visually — all connected to Claude Code so changes stay in sync. HumbleLytics enables no-code A/B experiments that dynamically update page content without deploying new code, so you can test headlines, CTAs, and layouts in real time. Storing performance context (A/B results, revenue data, growth metrics) back into Idea Browser compounds your results over time because every future decision is informed by past data. This full stack — Idea Browser, Paper, Claude Code, HumbleLytics — creates a significant arbitrage opportunity right now because almost nobody is using it at this level. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND AMIR ON SOCIAL Humblytics: https://humblytics.com/?via=community X/Twitter: https://x.com/amirmxt Youtube: https://www.youtube.com/@amirmxt

In this potluck episode of Syntax, Wes and Scott answer your questions about AI struggles with CSS and design workflows, learning vs relying on AI, debugging web performance, beginner soldering setups, navigating AI-era job interviews, Figma dev mode, modern API choices, and more. Show Notes 00:00 Welcome to Syntax! 00:55 Why AI struggles with CSS and design workflows 10:50 How much AI should you use when learning to code? 18:41 Debugging performance: tools and team workflows Ep 585: Fundamentals × What Makes a Website Slow? Ep 874: Fast Apps - Easy Perf Wins Ep 897: Making Your App Feel Faster Than It Really Is Ep 972: These Things Make Your App Feel Like Crap on Mobile 23:52 Brought to you by Sentry.io 26:26 Beginner soldering setup and essential tools 29:54 Preparing for interviews without AI (while jobs require it) Brendan Falk on AI-native coding interviews 35:16 Thoughts on Figma dev mode and design workflows 39:20 Ice vs Thaw menu bar apps 40:27 Why AI isn't pushing us toward better APIs 44:54 Vibe rules, skills, and shipping docs for agents vibe-rules Optimizing Content for Agents 54:44 Sick Picks + Shameless Plugs Sick Picks Scott: Jury Duty Wes: USB Cable Tester Shameless Plugs Syntax YouTube Channel Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

Half of consumer question the authenticity of what they see online.