Podcasts about Figma

What if the biggest barrier to learning AI isn't the tools—it's how we approach learning itself?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Gagan Biyani, CEO and co-founder of Maven, to unpack why this moment is critical for mid-career professionals to prioritize self-learning. Gagan shares lessons from running a cohort-based learning platform and conducting 30-50 interviews with companies struggling to adopt AI. He explains why AI is like witnessing the internet as a child—you can't afford not to learn it—and why building the learning habit matters more than what you learn first.They explore the five problems companies face with AI education: trying to generalize training when every role needs different tools, listening to tinkerers instead of bridge adopters, and delegating to chiefs of staff instead of having C-level sponsors run the trainings. Gagan shares Maven's own journey—why their design team needed to rebuild the design system before AI could be useful, how they're changing team ratios from 3-4 engineers per designer to just 2, and why social media is terrible for learning anything that requires weeks of dedication.If you're a mid-career professional feeling overwhelmed by AI, a leader trying to build a culture of self-learning at your company, or wondering how to actually integrate AI into your workflows—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

Nesse episódio trouxemos as notícias e novidades do mundo da programação que nos chamaram atenção dos dias 14/02 a 20/02.☕ Café Código FontePrograme sua xícara para o sabor certo!https://cafe.codigofonte.com.br

Nesse episódio trouxemos as notícias e novidades do mundo da programação que nos chamaram atenção dos dias 14/02 a 20/02.☕ Café Código FontePrograme sua xícara para o sabor certo!https://cafe.codigofonte.com.br

"They are changing venture capital from a 30% tax to 0% tax. If Robinhood succeeds, it makes Sequoia and Andreessen's business model untenable." — Keith TeareThe Silicon Gods must have their blood. And they've finally come for the funders of disruption, the venture capitalists, who are now being disrupted by something called Public Venture Capital (PVC). That, at least, is the view of That Was The Week publisher Keith Teare, who leads his newsletter this week with Robinhood's new venture fund. This new stock-trading app for millennials is going after Sequoia and Andreessen Horowitz—not by competing on deal flow, but by charging 0% carry instead of 20-30%. Robinhood promises it blows the doors off traditional venture capital.But Keith urges caution over PVCs. Robinhood is packaging late-stage private assets—companies like Databricks that would have IPO'd years ago but are staying private longer. By the time retail investors get access, employees are already cashing out through tender offers because they think the peak is near. The poster child: Figma, which did secondaries at $12 billion after Adobe's $20 billion acquisition failed. A lot of (dumb) people bought at the top and are now slightly less stupid.Fortunately, this week's tech roundup isn't just about get-rich-quick investment schemes. We also discuss Yasha Mounk's sobering experiment: he asked AI to write a political philosophy paper and found it "depressingly good"—publishable in an academic journal. Keith reframes this supposed "death of the humanities" as automation, not democratization. The humans aren't being leveled up; they're masquerading as producers while AI does the work. But craft still matters. When technology relieves humans of the mundane, he hopes, it elevates the special.Lastly but not least, we get to the abundance debate. Peter Diamandis and Singularity University have promised something called "exponential abundance" by 2035. Keith is sympathetic. I am not. The only thing I'm willing to guarantee is that we'll still be talking abundantly about abundance in 2035. And that the Silicon Valley Gods will have their blood. Five Takeaways●      Robinhood Is Charging 0% Carry: Sequoia and Andreessen take 20-30% of profits. Robinhood takes nothing. If they scale, the traditional VC model becomes untenable.●      But You're Buying at the Top: These are late-stage assets. Employees are selling through tender offers because they think peak valuation is near. Ask the people who bought Figma at $12 billion.●      AI Is Automating the Humanities: Yasha Mounk found AI could write "depressingly good" political philosophy. This isn't democratization—it's humans masquerading as producers.●      Craft Still Retains Its Power: Technology relieves humans of the mundane—and elevates the special. Creativity that breaks through will always command attention.●      The Abundance Debate Continues: Diamandis says abundance by 2035. Keith agrees land is already abundant. Andrew calls this "such a stupid thing to say." About the GuestKeith Teare is the publisher of That Was The Week and Executive Chairman of SignalRank. He is a serial entrepreneur and longtime observer of Silicon Valley. Keith joins Keen On America every Saturday for The Week That Was.ReferencesCompanies mentioned:●      Robinhood is launching a publicly listed venture fund, raising up to $1 billion at $25/share with 0% carry. They already have $340 million in assets including Databricks.●      Figma is cited as a cautionary tale: after Adobe's failed $20 billion acquisition, it did secondaries at $12 billion—many bought at the top.●      Polymarket is a prediction market platform that Robinhood has responded to by adding prediction markets to its offerings.People mentioned:●      Yasha Mounk wrote about AI writing "depressingly good" political philosophy papers that could be published in academic journals.●      Peter Diamandis and Dr. Alexander Wisner-Gross of Singularity University argue that exponential abundance is coming by 2035.●      Packy McCormick wrote about power in the age of intelligence.About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States—hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:(00:00) - Introduction: If it's Saturday, it must be revolution (02:11) - Robinhood's venture fund announcement (03:17) - What is Robinhood's day job? (07:43) - Secondary markets and tender offers (10:33) - Democratization or late-stage risk? (14:09) - Is Robinhood just gambling? (16:08) - Private vs. public market returns (19:02) - Is finance merging with betting? (24:23) - Blowing the doors off Sequoia and Andreessen (26:27) - Yasha Mounk: AI automating the humanities (28:47) - Where does power go in the age of AI? (30:42) - Craft retains its power (31:33) - The abundance debate (34:00) - Is land abundant? Andrew loses patience (00:00) - Chapter 15 (00:00) - Chapter 16 (00:00) - Introduction: If it's Saturday, it must be revolution (02:11) - Robinhood's venture fund announcement (03:17) - What is Robinhood's day job? (07:43) - Secondary markets and tender offers (10:33) - Democratization or late-stage risk? (14:09) - Is Robinhood just gambling? (16:08) - Private vs. public market returns (19:02) - Is finance merging with betting? (24:23) - Blowing the doors off Sequoia and Andreessen (26:27) - Yasha Mounk: AI automating the humanities

Send a textInvest in pre-IPO stocks with AG Dillon & Co. Contact aaron.dillon@agdillon.com to learn more. Financial advisors only. www.agdillon.com00:00 - Intro00:02 - AG Dillon Funds closing on Mar 31, 202600:51 - OpenAI Financials $280B revenue target meets $665B cost wall03:58 - OpenAI “buys” OpenClaw, Steinberger joins OpenAI04:42 - OpenAI Series C aims to shatter records at $850B post money05:41 - OpenAI and Tata bet on India with a 100 MW to 1 GW buildout path06:29 - Grafana's $9B round talks ride a $400M ARR wave07:23 - World Labs lands Autodesk and targets a rumored $5B valuation08:18 - Temporal wants to be the load bearing layer for agent execution09:31 - Mesh Optical's $50M Series A targets the chokepoint inside AI data centers10:43 - Render's $1.5B valuation is a bet that AI apps need a new runtime11:40 - Stash acquired by Grab for $425M13:06 - Physical Superintelligence pitches a physics breakthrough factory with a 20 person team14:07 - Figma plugs Claude Code into design and risks losing the workflow15:00 - Anthropic ships Sonnet 4.6 just 12 days after Opus 4.615:26 - Stripe's Bridge wins OCC trust charter signal as stablecoin scrutiny rises16:37 - Cohere puts 70 plus languages on device with a 3.35B parameter model17:53 - ElevenLabs turns agent risk into an insurable product at $12.2B secondary19:05 - Mistral buys Koyeb and adds 16 engineers to harden its compute stack

AGENDA: 04:14 Anthropic's $30B Raise at $380B 06:18 Why SaaS Stocks Keep Getting Crushed 18:15 Wall Street's New Religion: AI Replaces Headcount  22:42 The Bear Case for Shopify: What Could Go Wrong? 31:51 Replit and Lovable are Proof Figma Missed Out: Figma; Buy or Sell?  48:42 Stripe Raises at $140BN: Is Stripe Wildly Overvalued or Adyen Undervalued?  54:36 OpenAI Buys OpenClaw 01:06:28 Thrive's $10B Growth Fund 01:09:10 Arif Janmohamed Leaves Lightspeed for New Firm 01:17:12 Workday's Founder Returns as CEO: Will it Work?  01:20:34 Which Founder Returns Next: HubSpot, Twilio, Gitlab? 01:24:03 Is Monday.com a Screaming Buy? 01:28:25 Jason and Harry Bet $200,000  

Carl Quintanilla, Jim Cramer and David Faber discussed market reaction to Walmart's Q4 beat and what new CEO John Furner said on the earnings call about consumer spending. OpenAI CEO Sam Altman and Anthropic CEO Dario Amodei refused to hold hands during a group photo shoot with tech leaders at an AI summit in India. Both men spoke exclusively to CNBC: Altman on the U.S.-China AI arms race, Amodei on AI's effect on jobs. Also in focus: OpenAI's march toward a new $100 billion funding round, more pain for software stocks, Etsy jumps on the sale of second-hand fashion app Depop to eBay, Blue Owl slides on a report about redemptions, a flashback to what Jim said about Figma on the date of its stellar public debut in July 2025.   Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Market update for Thursday February 19, 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:Bitcoin drops near 2026 lows as crypto enthusiasm fadesWalmart beats on earnings but issues cautious guidanceFigma revenue jump 40% as AI monetization acceleratesEtsy sells Depop to eBay for $1.2BCarvana stock slides after earnings missAmazon surpasses Walmart in annual revenue

Neste episódio do Semiose Podcast, recebemos Natália Lumi, líder e especialista em design, para uma conversa profunda e necessária sobre carreira, os desafios da liderança e como manter a saúde mental em um mercado cada vez mais acelerado.Natália compartilha sua trajetória completa: desde o início no design gráfico e a época de "webmaster", passando pela criação de interfaces icônicas como a do aplicativo Show do Milhão, até chegar à gestão de grandes times em empresas como Natura e Itaú.Neste episódio, conversamos sobre:- Transição de Carreira: Como o design evoluiu do gráfico para o estratégico e o papel do Design Thinking nessa mudança.- Burnout e Saúde Mental: Natália abre o jogo sobre sua experiência com o esgotamento profissional e como a autohipnose e a meditação se tornaram ferramentas essenciais para sua recuperação e equilíbrio.- O Futuro com IA: Qual será o papel do designer em um mundo onde a inteligência artificial automatiza a execução? A importância da curadoria e do olhar estratégico.- Liderança Feminina: Os desafios de se impor e conquistar respeito em ambientes de tecnologia.Link da Convidada:https://www.linkedin.com/in/natalialumi/______________________________________✅Recomendações de Conteúdo:Curso de UX/UI Design: ⁠⁠⁠⁠https://cursouidesign.com.br/Curso de Figma: ⁠⁠⁠⁠https://cursofigma.com.brFundamentos do Design Visual: ⁠⁠⁠⁠https://fundamentosdodesign.com.brEbook Heurísticas de Nielsen: ⁠⁠⁠⁠http://papodeux.com.br/conteudo/ebook-heursticas-de-nielsen✅Siga o Semiose nas Redes Sociais:Instagram: ⁠⁠⁠⁠https://www.instagram.com/semiosepodcast/⁠⁠⁠⁠⁠⁠⁠LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/company/semiosedesign/TikTok: ⁠⁠⁠⁠https://www.tiktok.com/@semiosepodcast#podcastbrasil #podcastdesign #semiosepodcast

O que acontece quando um rebranding é conduzido dentro de uma empresa verdadeiramente design-driven?Neste episódio do LadoQ, Regys Lima, Coord. de Design do Itaú, e Andres Zambra, Gerente de Marketing do Cubo Itaú, e Leo Massarelli exploram o rebranding de Cubo Itaú — não apenas como exercício de identidade visual, mas como construção estratégica conduzida com maturidade, governança e coragem.Falamos sobre processo aberto, confiança entre cliente e consultoria, feedback estruturado e o papel do ego em projetos de marca. O que muda quando o cliente entende profundamente design? Como equilibrar consistência e evolução em uma marca endossada? E por que “briefing redondo e feedback preciso” fizeram toda a diferença nesse caso?Mais do que um case, este episódio é uma reflexão sobre como o design deixa de ser estética e passa a ser sistema operacional dentro das organizações.00:00 Abertura — o que é um processo design-driven04:20 O Cubo Itaú e a evolução da marca10:00 O que significa ser uma empresa design-driven na prática16:30 Consistência + evolução (design nunca termina)22:10 Escolhendo parceiros quando o cliente entende design28:30 Processo aberto: Slack, Figma e confiança34:40 O momento tenso do logo (coragem vs. segurança)40:00 Arquitetura de marca e sistema de cores (a dose certa)45:30 Briefing redondo + feedback preciso49:00 Lições para empresas que querem ser design-driven

China's tech giants are kicking off the Lunar New Year with a wave of AI and robotics announcements. Plus, what Figma's new “Code to Canvas” partnership with Anthropic means for the future of Software. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Episode web page: https://bit.ly/4k9H4fT Episode summary: In this episode of Insights Unlocked, design and research leaders from TruStage share how they transformed UX research from an inconsistent, ad-hoc effort into a scalable, trusted practice embedded directly within their design team. Through a creative “cookbook” framework, the team built shared standards, accelerated time to insights, and increased stakeholder confidence—without sacrificing flexibility or creativity. What you'll learn Why TruStage shifted from siloed research teams to an embedded UX research model How a visual “cookbook” system helped standardize research without making it rigid The power of shared language and artifacts to build stakeholder trust and buy-in How repeatable research “meal plans” enabled faster pivots and better decision-making What it takes to scale research volume while improving quality and consistency Key themes and ideas From potluck to practice. The TruStage team describes their early research approach as a “potluck”—rich in individual expertise but lacking consistency. By designing a shared system, they moved toward a polished, repeatable research practice that stakeholders could rely on. The research cookbook framework. Using food metaphors, the team created: Recipes for designers and researchers that explain how to run specific studies Menus for stakeholders that clearly outline value, effort, and outcomes Meal plans that bundle methods together across stages of the product lifecycle This framework helped align internal teams and external partners around expectations, scope, and impact. Embedding research into everyday workflows. By building the system directly in Figma and connecting it to their agile tooling, TruStage made research easy to plan, prioritize, and execute—removing friction that previously slowed teams down. Scaling impact through trust and clarity. Clear artifacts and shared standards made research easier to explain, faster to approve, and more likely to be requested. As a result, the team more than doubled the number of research stories completed year over year and shifted from “selling” research to responding to demand. Empowering teams through co-creation. Rather than dictating a process from the top down, the team involved designers across experience levels in shaping the system. This created stronger ownership, higher adoption, and a culture where research felt both accessible and fun. Advice for teams operationalizing research Lean into tools your team already loves and uses daily Invest time in shared philosophy and language—not just templates Co-create systems with the people who will use them Treat research operations as an evolving practice, not a one-time deliverable Resources & links TruStage's website (https://www.trustage.com/) Nick Higbee on LinkedIn (https://www.linkedin.com/in/nicholas-higbee-95540425/) Benny Brooks on LinkedIn (https://www.linkedin.com/in/thebenbrooks/) Betsy Drews on LinkedIn (https://www.linkedin.com/in/betsy-drews-4a30256b/) Natalie Padilla on LinkedIn (https://www.linkedin.com/in/natalie-weiner/) Nathan Isaacs on LinkedIn (https://www.linkedin.com/in/nathanisaacs/) Learn more about Insights Unlocked: https://www.usertesting.com/podcast

What if the best way to lead product is to build it yourself first?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Chase Schwalbach, SVP of Product and Technology at Millie, to unpack a radically different approach to product leadership. Despite his title, Chase spent months as an IC, rolling up his sleeves to build healthcare infrastructure, teach himself AI eval systems, and ship a sophisticated patient chatbot, all before bringing his team in. He explains why shielding the team from early-stage messiness, moving at speed, and feeling the pain yourself leads to better products.They explore how Chase built a team of AI agents (supervisor + specialized sub-agents) from scratch, why treating prompts like deterministic code requires extreme precision, and how he taught himself evals through pure iteration. Plus, the converging worlds of PM and engineering, why technical PMs and product-minded engineers are becoming the same role, why handoffs kill velocity in an AI-native world, and what “context engineering” actually means when your codebase needs to work for both humans and AI agents.If you're a product leader wondering whether to get more hands-on, an engineer considering the jump to PM (or vice versa), or building AI systems in regulated industries like healthcare, 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

Is AI eating the software industry, or is it just making it more powerful?In this episode, we sit down with Braden Dennis, CEO and co-founder of Fiscal.ai, to discuss the shift happening in enterprise SaaS. If you've watched our videos, you know we use Fiscal's charts every single day to analyze the markets, so it was great to get Braden's perspective on where the industry is headed.We dive deep into the software apocalypse narrative and whether it's based in reality or just a market overreaction. Braden explains why maintaining software is getting easier, how his engineering team has achieved 10x productivity, and why internal AI solutions are coming for the "busy work" that off-the-shelf SaaS can't solve.Join us on Discord with Semiconductor Insider, sign up on our website: www.chipstockinvestor.com/membershipSupercharge your analysis with AI! Get 15% of your membership with our special link here: https://fiscal.ai/csi/Sign Up For Our Newsletter: https://mailchi.mp/b1228c12f284/sign-up-landing-page-short-formChapters:0:00 – Is AI Eating Software? 1:12 – Meet Braden Dennis, CEO of Fiscal.ai 1:45 – Why Software Engineering Has Changed Completely 2:40 – 2026 Outlook: Opportunities vs. Traps 3:30 – What Software is Becoming Obsolete? 4:30 – Automating the "Unsolvable" Internal Busy Work 5:15 – "Intelligence in the Sky": A New Data Layer 6:10 – Pricing Power Debate: Will Clients Pay Less? 7:45 – Broadcom & VMware Case Study8:55 – Comparing the Software Correction to 2018 Semiconductors 11:45 – Lessons on Market Cyclicality 13:55 – The Problem with Late-Stage Venture Capital 16:00 – Why We Need More Tech IPOs 18:10 – The Incentive for Founders to Stay Private 20:00 – Evaluating Figma and Adobe in the AI Age21:30 – ServiceNow: Narrative vs. Financial Reality 22:45 – Final Verdict: Being Selective in a Sell-offIf you found this video useful, please make sure to like and subscribe!*********************************************************Affiliate links that are sprinkled in throughout this video. If something catches your eye and you decide to buy it, we might earn a little coffee money. Thanks for helping us (Kasey) fuel our caffeine addiction!Content in this video is for general information or entertainment only and is not specific or individual investment advice. Forecasts and information presented may not develop as predicted and there is no guarantee any strategies presented will be successful. All investing involves risk, and you could lose some or all of your principal. #AI #SaaS #SoftwareStocks #Investing #ChipStockInvestor #FiscalAI #TechInvesting #ServiceNow #stockmarket2026 Nick and Kasey own shares of Adobe, Figma, ServiceNow

Emmanuel et Guillaume discutent de divers sujets liés à la programmation, notamment les systèmes de fichiers en Java, le Data Oriented Programming, les défis de JPA avec Kotlin, et les nouvelles fonctionnalités de Quarkus. Ils explorent également des sujets un peu fous comme la création de datacenters dans l'espace. Pas mal d'architecture aussi. Enregistré le 13 février 2026 Téléchargement de l'épisode LesCastCodeurs-Episode-337.mp3 ou en vidéo sur YouTube. News Langages Comment implémenter un file system en Java https://foojay.io/today/bootstrapping-a-java-file-system/ Créer un système de fichiers Java personnalisé avec NIO.2 pour des usages variés (VCS, archives, systèmes distants). Évolution Java: java.io.File (1.0) -> NIO (1.4) -> NIO.2 (1.7) pour personnalisation via FileSystem. Recommander conception préalable; API Java est orientée POSIX. Composants clés à considérer: Conception URI (scheme unique, chemin). Gestion de l'arborescence (BD, métadonnées, efficacité). Stockage binaire (emplacement, chiffrement, versions). Minimum pour démarrer (4 composants): Implémenter Path (représente fichier/répertoire). Étendre FileSystem (instance du système). Étendre FileSystemProvider (moteur, enregistré par scheme). Enregistrer FileSystemProvider via META-INF/services. Étapes suivantes: Couche BD (arborescence), opérations répertoire/fichier de base, stockage, tests. Processus long et exigeant, mais gratifiant.   Un article de brian goetz sur le futur du data oriented programming en Java https://openjdk.org/projects/amber/design-notes/beyond-records Le projet Amber de Java introduit les "carrier classes", une évolution des records qui permet plus de flexibilité tout en gardant les avantages du pattern matching et de la reconstruction Les records imposent des contraintes strictes (immutabilité, représentation exacte de l'état) qui limitent leur usage pour des classes avec état muable ou dérivé Les carrier classes permettent de déclarer une state description complète et canonique sans imposer que la représentation interne corresponde exactement à l'API publique Le modificateur "component" sur les champs permet au compilateur de dériver automatiquement les accesseurs pour les composants alignés avec la state description Les compact constructors sont généralisés aux carrier classes, générant automatiquement l'initialisation des component fields Les carrier classes supportent la déconstruction via pattern matching comme les records, rendant possible leur usage dans les instanceof et switch Les carrier interfaces permettent de définir une state description sur une interface, obligeant les implémentations à fournir les accesseurs correspondants L'extension entre carrier classes est possible, avec dérivation automatique des appels super() quand les composants parent sont subsumés par l'enfant Les records deviennent un cas particulier de carrier classes avec des contraintes supplémentaires (final, extends Record, component fields privés et finaux obligatoires) L'évolution compatible des records est améliorée en permettant l'ajout de composants en fin de liste et la déconstruction partielle par préfixe Comment éviter les pièges courants avec JPA et Kotlin - https://blog.jetbrains.com/idea/2026/01/how-to-avoid-common-pitfalls-with-jpa-and-kotlin/ JPA est une spécification Java pour la persistance objet-relationnel, mais son utilisation avec Kotlin présente des incompatibilités dues aux différences de conception des deux langages Les classes Kotlin sont finales par défaut, ce qui empêche la création de proxies par JPA pour le lazy loading et les opérations transactionnelles Le plugin kotlin-jpa génère automatiquement des constructeurs sans argument et rend les classes open, résolvant les problèmes de compatibilité Les data classes Kotlin ne sont pas adaptées aux entités JPA car elles génèrent equals/hashCode basés sur tous les champs, causant des problèmes avec les relations lazy L'utilisation de lateinit var pour les relations peut provoquer des exceptions si on accède aux propriétés avant leur initialisation par JPA Les types non-nullables Kotlin peuvent entrer en conflit avec le comportement de JPA qui initialise les entités avec des valeurs null temporaires Le backing field direct dans les getters/setters personnalisés peut contourner la logique de JPA et casser le lazy loading IntelliJ IDEA 2024.3 introduit des inspections pour détecter automatiquement ces problèmes et propose des quick-fixes L'IDE détecte les entités finales, les data classes inappropriées, les problèmes de constructeurs et l'usage incorrect de lateinit Ces nouvelles fonctionnalités aident les développeurs à éviter les bugs subtils liés à l'utilisation de JPA avec Kotlin Librairies Guide sur MapStruct @IterableMapping - https://www.baeldung.com/java-mapstruct-iterablemapping MapStruct est une bibliothèque Java pour générer automatiquement des mappers entre beans, l'annotation @IterableMapping permet de configurer finement le mapping de collections L'attribut dateFormat permet de formater automatiquement des dates lors du mapping de listes sans écrire de boucle manuelle L'attribut qualifiedByName permet de spécifier quelle méthode custom appliquer sur chaque élément de la collection à mapper Exemple d'usage : filtrer des données sensibles comme des mots de passe en mappant uniquement certains champs via une méthode dédiée L'attribut nullValueMappingStrategy permet de contrôler le comportement quand la collection source est null (retourner null ou une collection vide) L'annotation fonctionne pour tous types de collections Java (List, Set, etc.) et génère le code de boucle nécessaire Possibilité d'appliquer des formats numériques avec numberFormat pour convertir des nombres en chaînes avec un format spécifique MapStruct génère l'implémentation complète du mapper au moment de la compilation, éliminant le code boilerplate L'annotation peut être combinée avec @Named pour créer des méthodes de mapping réutilisables et nommées Le mapping des collections supporte les conversions de types complexes au-delà des simples conversions de types primitifs Accès aux fichiers Samba depuis Java avec JCIFS - https://www.baeldung.com/java-samba-jcifs JCIFS est une bibliothèque Java permettant d'accéder aux partages Samba/SMB sans monter de lecteur réseau, supportant le protocole SMB3 on pense aux galériens qui doivent se connecter aux systèmes dit legacy La configuration nécessite un contexte CIFS (CIFSContext) et des objets SmbFile pour représenter les ressources distantes L'authentification se fait via NtlmPasswordAuthenticator avec domaine, nom d'utilisateur et mot de passe La bibliothèque permet de lister les fichiers et dossiers avec listFiles() et vérifier leurs propriétés (taille, date de modification) Création de fichiers avec createNewFile() et de dossiers avec mkdir() ou mkdirs() pour créer toute une arborescence Suppression via delete() qui peut parcourir et supprimer récursivement des arborescences entières Copie de fichiers entre partages Samba avec copyTo(), mais impossibilité de copier depuis le système de fichiers local Pour copier depuis le système local, utilisation des streams SmbFileInputStream et SmbFileOutputStream Les opérations peuvent cibler différents serveurs Samba et différents partages (anonymes ou protégés par mot de passe) La bibliothèque s'intègre dans des blocs try-with-resources pour une gestion automatique des ressources Quarkus 3.31 - Support complet Java 25, nouveau packaging Maven et Panache Next - https://quarkus.io/blog/quarkus-3-31-released/ Support complet de Java 25 avec images runtime et native Nouveau packaging Maven de type quarkus avec lifecycle optimisé pour des builds plus rapides voici un article complet pour plus de detail https://quarkus.io/blog/building-large-applications/ Introduction de Panache Next, nouvelle génération avec meilleure expérience développeur et API unifiée ORM/Reactive Mise à jour vers Hibernate ORM 7.2, Reactive 3.2, Search 8.2 Support de Hibernate Spatial pour les données géospatiales Passage à Testcontainers 2 et JUnit 6 Annotations de sécurité supportées sur les repositories Jakarta Data Chiffrement des tokens OIDC pour les implémentations custom TokenStateManager Support OAuth 2.0 Pushed Authorization Requests dans l'extension OIDC Maven 3.9 maintenant requis minimum pour les projets Quarkus A2A Java SDK 1.0.0.Alpha1 - Alignement avec la spécification 1.0 du protocole Agent2Agent - https://quarkus.io/blog/a2a-java-sdk-1-0-0-alpha1/ Le SDK Java A2A implémente le protocole Agent2Agent qui permet la communication standardisée entre agents IA pour découvrir des capacités, déléguer des tâches et collaborer Passage à la version 1.0 de la spécification marque la transition d'expérimental à production-ready avec des changements cassants assumés Modernisation complète du module spec avec des Java records partout remplaçant le mix précédent de classes et records pour plus de cohérence Adoption de Protocol Buffers comme source de vérité avec des mappers MapStruct pour la conversion et Gson pour JSON-RPC Les builders utilisent maintenant des méthodes factory statiques au lieu de constructeurs publics suivant les best practices Java modernes Introduction de trois BOMs Maven pour simplifier la gestion des dépendances du SDK core, des extensions et des implémentations de référence Quarkus AgentCard évolue avec une liste supportedInterfaces remplaçant url et preferredTransport pour plus de flexibilité dans la déclaration des protocoles Support de la pagination ajouté pour ListTasks et les endpoints de configuration des notifications push avec des wrappers Result appropriés Interface A2AHttpClient pluggable permettant des implémentations HTTP personnalisées avec une implémentation Vert.x fournie Travail continu vers la conformité complète avec le TCK 1.0 en cours de développement parallèlement à la finalisation de la spécification Pourquoi Quarkus finit par "cliquer" : les 10 questions que se posent les développeurs Java - https://www.the-main-thread.com/p/quarkus-java-developers-top-questions-2025 un article qui revele et repond aux questions des gens qui ont utilisé Quarkus depuis 4-6 mois, les non noob questions Quarkus est un framework Java moderne optimisé pour le cloud qui propose des temps de démarrage ultra-rapides et une empreinte mémoire réduite Pourquoi Quarkus démarre si vite ? Le framework effectue le travail lourd au moment du build (scanning, indexation, génération de bytecode) plutôt qu'au runtime Quand utiliser le mode réactif plutôt qu'impératif ? Le réactif est pertinent pour les workloads avec haute concurrence et dominance I/O, l'impératif reste plus simple dans les autres cas Quelle est la différence entre Dev Services et Testcontainers ? Dev Services utilise Testcontainers en gérant automatiquement le cycle de vie, les ports et la configuration sans cérémonie Comment la DI de Quarkus diffère de Spring ? CDI est un standard basé sur la sécurité des types et la découverte au build-time, différent de l'approche framework de Spring Comment gérer la configuration entre environnements ? Quarkus permet de scaler depuis le développement local jusqu'à Kubernetes avec des profils, fichiers multiples et configuration externe Comment tester correctement les applications Quarkus ? @QuarkusTest démarre l'application une fois pour toute la suite de tests, changeant le modèle mental par rapport à Spring Boot Que fait vraiment Panache en coulisses ? Panache est du JPA avec des opinions fortes et des défauts propres, enveloppant Hibernate avec un style Active Record Doit-on utiliser les images natives et quand ? Les images natives brillent pour le serverless et l'edge grâce au démarrage rapide et la faible empreinte mémoire, mais tous les apps n'en bénéficient pas Comment Quarkus s'intègre avec Kubernetes ? Le framework génère automatiquement les ressources Kubernetes, gère les health checks et métriques comme s'il était nativement conçu pour cet écosystème Comment intégrer l'IA dans une application Quarkus ? LangChain4j permet d'ajouter embeddings, retrieval, guardrails et observabilité directement en Java sans passer par Python Infrastructure Les alternatives à MinIO https://rmoff.net/2026/01/14/alternatives-to-minio-for-single-node-local-s3/ MinIO a abandonné le support single-node fin 2025 pour des raisons commerciales, cassant de nombreuses démos et pipelines CI/CD qui l'utilisaient pour émuler S3 localement L'auteur cherche un remplacement simple avec image Docker, compatibilité S3, licence open source, déploiement mono-nœud facile et communauté active S3Proxy est très léger et facile à configurer, semble être l'option la plus simple mais repose sur un seul contributeur RustFS est facile à utiliser et inclut une GUI, mais c'est un projet très récent en version alpha avec une faille de sécurité majeure récente SeaweedFS existe depuis 2012 avec support S3 depuis 2018, relativement facile à configurer et dispose d'une interface web basique Zenko CloudServer remplace facilement MinIO mais la documentation et le branding (cloudserver/zenko/scality) peuvent prêter à confusion Garage nécessite une configuration complexe avec fichier TOML et conteneur d'initialisation séparé, pas un simple remplacement drop-in Apache Ozone requiert au minimum quatre nœuds pour fonctionner, beaucoup trop lourd pour un usage local simple L'auteur recommande SeaweedFS et S3Proxy comme remplaçants viables, RustFS en maybe, et élimine Garage et Ozone pour leur complexité Garage a une histoire tres associative, il vient du collectif https://deuxfleurs.fr/ qui offre un cloud distribué sans datacenter C'est certainement pas une bonne idée, les datacenters dans l'espace https://taranis.ie/datacenters-in-space-are-a-terrible-horrible-no-good-idea/ Avis d'expert (ex-NASA/Google, Dr en électronique spatiale) : Centres de données spatiaux, une "terrible" idée. Incompatibilité fondamentale : L'électronique (surtout IA/GPU) est inadaptée à l'environnement spatial. Énergie : Accès limité. Le solaire (type ISS) est insuffisant pour l'échelle de l'IA. Le nucléaire (RTG) est trop faible. Refroidissement : L'espace n'est pas "froid" ; absence de convection. Nécessite des radiateurs gigantesques (ex: 531m² pour 200kW). Radiations : Provoque erreurs (SEU, SEL) et dommages. Les GPU sont très vulnérables. Blindage lourd et inefficace. Les puces "durcies" sont très lentes. Communications : Bande passante très limitée (1Gbps radio vs 100Gbps terrestre). Le laser est tributaire des conditions atmosphériques. Conclusion : Projet extrêmement difficile, coûteux et aux performances médiocres. Data et Intelligence Artificielle Guillaume a développé un serveur MCP pour arXiv (le site de publication de papiers de recherche) en Java avec le framework Quarkus https://glaforge.dev/posts/2026/01/18/implementing-an-arxiv-mcp-server-with-quarkus-in-java/ Implémentation d'un serveur MCP (Model Context Protocol) arXiv en Java avec Quarkus. Objectif : Accéder aux publications arXiv et illustrer les fonctionnalités moins connues du protocole MCP. Mise en œuvre : Utilisation du framework Quarkus (Java) et son support MCP étendu. Assistance par Antigravity (IDE agentique) pour le développement et l'intégration de l'API arXiv. Interaction avec l'API arXiv : requêtes HTTP, format XML Atom pour les résultats, parser XML Jackson. Fonctionnalités MCP exposées : Outils (@Tool) : Recherche de publications (search_papers). Ressources (@Resource, @ResourceTemplate) : Taxonomie des catégories arXiv, métadonnées des articles (via un template d'URI). Prompts (@Prompt) : Exemples pour résumer des articles ou construire des requêtes de recherche. Configuration : Le serveur peut fonctionner en STDIO (local) ou via HTTP Streamable (local ou distant), avec une configuration simple dans des clients comme Gemini CLI. Conclusion : Quarkus simplifie la création de serveurs MCP riches en fonctionnalités, rendant les données et services "prêts pour l'IA" avec l'aide d'outils d'IA comme Antigravity. Anthropic ne mettra pas de pub dans Claude https://www.anthropic.com/news/claude-is-a-space-to-think c'est en reaction au plan non public d'OpenAi de mettre de la pub pour pousser les gens au mode payant OpenAI a besoin de cash et est probablement le plus utilisé pour gratuit au monde Anthropic annonce que Claude restera sans publicité pour préserver son rôle d'assistant conversationnel dédié au travail et à la réflexion approfondie. Les conversations avec Claude sont souvent sensibles, personnelles ou impliquent des tâches complexes d'ingénierie logicielle où les publicités seraient inappropriées. L'analyse des conversations montre qu'une part significative aborde des sujets délicats similaires à ceux évoqués avec un conseiller de confiance. Un modèle publicitaire créerait des incitations contradictoires avec le principe fondamental d'être "genuinely helpful" inscrit dans la Constitution de Claude. Les publicités introduiraient un conflit d'intérêt potentiel où les recommandations pourraient être influencées par des motivations commerciales plutôt que par l'intérêt de l'utilisateur. Le modèle économique d'Anthropic repose sur les contrats entreprise et les abonnements payants, permettant de réinvestir dans l'amélioration de Claude. Anthropic maintient l'accès gratuit avec des modèles de pointe et propose des tarifs réduits pour les ONG et l'éducation dans plus de 60 pays. Le commerce "agentique" sera supporté mais uniquement à l'initiative de l'utilisateur, jamais des annonceurs, pour préserver la confiance. Les intégrations tierces comme Figma, Asana ou Canva continueront d'être développées en gardant l'utilisateur aux commandes. Anthropic compare Claude à un cahier ou un tableau blanc : des espaces de pensée purs, sans publicité. Infinispan 16.1 est sorti https://infinispan.org/blog/2026/02/04/infinispan-16-1 déjà le nom de la release mérite une mention Le memory bounded par cache et par ensemble de cache s est pas facile à faire en Java Une nouvelle api OpenAPI AOT caché dans les images container Un serveur MCP local juste avec un fichier Java ? C'est possible avec LangChain4j et JBang https://glaforge.dev/posts/2026/02/11/zero-boilerplate-java-stdio-mcp-servers-with-langchain4j-and-jbang/ Création rapide de serveurs MCP Java sans boilerplate. MCP (Model Context Protocol): standard pour connecter les LLM à des outils et données. Le tutoriel répond au manque d'options simples pour les développeurs Java, face à une prédominance de Python/TypeScript dans l'écosystème MCP. La solution utilise: LangChain4j: qui intègre un nouveau module serveur MCP pour le protocole STDIO. JBang: permet d'exécuter des fichiers Java comme des scripts, éliminant les fichiers de build (pom.xml, Gradle). Implémentation: se fait via un seul fichier .java. JBang gère automatiquement les dépendances (//DEPS). L'annotation @Tool de LangChain4j expose les méthodes Java aux LLM. StdioMcpServerTransport gère la communication JSON-RPC via l'entrée/sortie standard (STDIO). Point crucial: Les logs doivent impérativement être redirigés vers System.err pour éviter de corrompre System.out, qui est réservé à la communication MCP (messages JSON-RPC). Facilite l'intégration locale avec des outils comme Gemini CLI, Claude Code, etc. Reciprocal Rank Fusion : un algorithme utile et souvent utilisé pour faire de la recherche hybride, pour mélanger du RAG et des recherches par mots-clé https://glaforge.dev/posts/2026/02/10/advanced-rag-understanding-reciprocal-rank-fusion-in-hybrid-search/ RAG : Qualité LLM dépend de la récupération. Recherche Hybride : Combiner vectoriel et mots-clés (BM25) est optimal. Défi : Fusionner des scores d'échelles différentes. Solution : Reciprocal Rank Fusion (RRF). RRF : Algorithme robuste qui fusionne des listes de résultats en se basant uniquement sur le rang des documents, ignorant les scores. Avantages RRF : Pas de normalisation de scores, scalable, excellente première étape de réorganisation. Architecture RAG fréquente : RRF (large sélection) + Cross-Encoder / modèle de reranking (précision fine). RAG-Fusion : Utilise un LLM pour générer plusieurs variantes de requête, puis RRF agrège tous les résultats pour renforcer le consensus et réduire les hallucinations. Implémentation : LangChain4j utilise RRF par défaut pour agréger les résultats de plusieurs retrievers. Les dernières fonctionnalités de Gemini et Nano Banana supportées dans LangChain4j https://glaforge.dev/posts/2026/02/06/latest-gemini-and-nano-banana-enhancements-in-langchain4j/ Nouveaux modèles d'images Nano Banana (Gemini 2.5/3.0) pour génération et édition (jusqu'à 4K). "Grounding" via Google Search (pour images et texte) et Google Maps (localisation, Gemini 2.5). Outil de contexte URL (Gemini 3.0) pour lecture directe de pages web. Agents multimodaux (AiServices) capables de générer des images. Configuration de la réflexion (profondeur Chain-of-Thought) pour Gemini 3.0. Métadonnées enrichies : usage des tokens et détails des sources de "grounding". Comment configurer Gemini CLI comment agent de code dans IntelliJ grâce au protocole ACP https://glaforge.dev/posts/2026/02/01/how-to-integrate-gemini-cli-with-intellij-idea-using-acp/ But : Intégrer Gemini CLI à IntelliJ IDEA via l'Agent Client Protocol (ACP). Prérequis : IntelliJ IDEA 2025.3+, Node.js (v20+), Gemini CLI. Étapes : Installer Gemini CLI (npm install -g @google/gemini-cli). Localiser l'exécutable gemini. Configurer ~/.jetbrains/acp.json (chemin exécutable, --experimental-acp, use_idea_mcp: true). Redémarrer IDEA, sélectionner "Gemini CLI" dans l'Assistant IA. Usage : Gemini interagit avec le code et exécute des commandes (contexte projet). Important : S'assurer du flag --experimental-acp dans la configuration. Outillage PipeNet, une alternative (open source aussi) à LocalTunnel, mais un plus évoluée https://pipenet.dev/ pipenet: Alternative open-source et moderne à localtunnel (client + serveur). Usages: Développement local (partage, webhooks), intégration SDK, auto-hébergement sécurisé. Fonctionnalités: Client (expose ports locaux, sous-domaines), Serveur (déploiement, domaines personnalisés, optimisé cloud mono-port). Avantages vs localtunnel: Déploiement cloud sur un seul port, support multi-domaines, TypeScript/ESM, maintenance active. Protocoles: HTTP/S, WebSocket, SSE, HTTP Streaming. Intégration: CLI ou SDK JavaScript. JSON-IO — une librairie comme Jackson ou GSON, supportant JSON5, TOON, et qui pourrait être utile pour l'utilisation du "structured output" des LLMs quand ils ne produisent pas du JSON parfait https://github.com/jdereg/json-io json-io : Librairie Java pour la sérialisation et désérialisation JSON/TOON. Gère les graphes d'objets complexes, les références cycliques et les types polymorphes. Support complet JSON5 (lecture et écriture), y compris des fonctionnalités non prises en charge par Jackson/Gson. Format TOON : Notation orientée token, optimisée pour les LLM, réduisant l'utilisation de tokens de 40 à 50% par rapport au JSON. Légère : Aucune dépendance externe (sauf java-util), taille de JAR réduite (~330K). Compatible JDK 1.8 à 24, ainsi qu'avec les environnements JPMS et OSGi. Deux modes de conversion : vers des objets Java typés (toJava()) ou vers des Map (toMaps()). Options de configuration étendues via ReadOptionsBuilder et WriteOptionsBuilder. Optimisée pour les déploiements cloud natifs et les architectures de microservices. Utiliser mailpit et testcontainer pour tester vos envois d'emails https://foojay.io/today/testing-emails-with-testcontainers-and-mailpit/ l'article montre via SpringBoot et sans. Et voici l'extension Quarkus https://quarkus.io/extensions/io.quarkiverse.mailpit/quarkus-mailpit/?tab=docs Tester l'envoi d'emails en développement est complexe car on ne peut pas utiliser de vrais serveurs SMTP Mailpit est un serveur SMTP de test qui capture les emails et propose une interface web pour les consulter Testcontainers permet de démarrer Mailpit dans un conteneur Docker pour les tests d'intégration L'article montre comment configurer une application SpringBoot pour envoyer des emails via JavaMail Un module Testcontainers dédié à Mailpit facilite son intégration dans les tests Le conteneur Mailpit expose un port SMTP (1025) et une API HTTP (8025) pour vérifier les emails reçus Les tests peuvent interroger l'API HTTP de Mailpit pour valider le contenu des emails envoyés Cette approche évite d'utiliser des mocks et teste réellement l'envoi d'emails Mailpit peut aussi servir en développement local pour visualiser les emails sans les envoyer réellement La solution fonctionne avec n'importe quel framework Java supportant JavaMail Architecture Comment scaler un système de 0 à 10 millions d'utilisateurs https://blog.algomaster.io/p/scaling-a-system-from-0-to-10-million-users Philosophie : Scalabilité incrémentale, résoudre les goulots d'étranglement sans sur-ingénierie. 0-100 utilisateurs : Serveur unique (app, DB, jobs). 100-1K : Séparer app et DB (services gérés, pooling). 1K-10K : Équilibreur de charge, multi-serveurs d'app (stateless via sessions partagées). 10K-100K : Caching, réplicas de lecture DB, CDN (réduire charge DB). 100K-500K : Auto-scaling, applications stateless (authentification JWT). 500K-10M : Sharding DB, microservices, files de messages (traitement asynchrone). 10M+ : Déploiement multi-régions, CQRS, persistance polyglotte, infra personnalisée. Principes clés : Simplicité, mesure, stateless essentiel, cache/asynchrone, sharding prudent, compromis (CAP), coût de la complexité. Patterns d'Architecture 2026 - Du Hype à la Réalité du Terrain (Part 1/2) - https://blog.ippon.fr/2026/01/30/patterns-darchitecture-2026-part-1/ L'article présente quatre patterns d'architecture logicielle pour répondre aux enjeux de scalabilité, résilience et agilité business dans les systèmes modernes Il présentent leurs raisons et leurs pièges Un bon rappel L'Event-Driven Architecture permet une communication asynchrone entre systèmes via des événements publiés et consommés, évitant le couplage direct Les bénéfices de l'EDA incluent la scalabilité indépendante des composants, la résilience face aux pannes et l'ajout facile de nouveaux cas d'usage Le pattern API-First associé à un API Gateway centralise la sécurité, le routage et l'observabilité des APIs avec un catalogue unifié Le Backend for Frontend crée des APIs spécifiques par canal (mobile, web, partenaires) pour optimiser l'expérience utilisateur CQRS sépare les modèles de lecture et d'écriture avec des bases optimisées distinctes, tandis que l'Event Sourcing stocke tous les événements plutôt que l'état actuel Le Saga Pattern gère les transactions distribuées via orchestration centralisée ou chorégraphie événementielle pour coordonner plusieurs microservices Les pièges courants incluent l'explosion d'événements granulaires, la complexité du debugging distribué, et la mauvaise gestion de la cohérence finale Les technologies phares sont Kafka pour l'event streaming, Kong pour l'API Gateway, EventStoreDB pour l'Event Sourcing et Temporal pour les Sagas Ces patterns nécessitent une maturité technique et ne sont pas adaptés aux applications CRUD simples ou aux équipes junior Patterns d'architecture 2026 : du hype à la réalité terrain part. 2 - https://blog.ippon.fr/2026/02/04/patterns-darchitecture-2026-part-2/ Deuxième partie d'un guide pratique sur les patterns d'architecture logicielle et système éprouvés pour moderniser et structurer les applications en 2026 Strangler Fig permet de migrer progressivement un système legacy en l'enveloppant petit à petit plutôt que de tout réécrire d'un coup (70% d'échec pour les big bang) Anti-Corruption Layer protège votre nouveau domaine métier des modèles externes et legacy en créant une couche de traduction entre les systèmes Service Mesh gère automatiquement la communication inter-services dans les architectures microservices (sécurité mTLS, observabilité, résilience) Architecture Hexagonale sépare le coeur métier des détails techniques via des ports et adaptateurs pour améliorer la testabilité et l'évolutivité Chaque pattern est illustré par un cas client concret avec résultats mesurables et liste des pièges à éviter lors de l'implémentation Les technologies 2026 mentionnées incluent Istio, Linkerd pour service mesh, LaunchDarkly pour feature flags, NGINX et Kong pour API gateway Tableau comparatif final aide à choisir le bon pattern selon la complexité, le scope et le use case spécifique du projet L'article insiste sur une approche pragmatique : ne pas utiliser un pattern juste parce qu'il est moderne mais parce qu'il résout un problème réel Pour les systèmes simples type CRUD ou avec peu de services, ces patterns peuvent introduire une complexité inutile qu'il faut savoir éviter Méthodologies Le rêve récurrent de remplacer voire supprimer les développeurs https://www.caimito.net/en/blog/2025/12/07/the-recurring-dream-of-replacing-developers.html Depuis 1969, chaque décennie voit une tentative de réduire le besoin de développeurs (de COBOL, UML, visual builders… à IA). Motivation : frustration des dirigeants face aux délais et coûts de développement. La complexité logicielle est intrinsèque et intellectuelle, non pas une question d'outils. Chaque vague technologique apporte de la valeur mais ne supprime pas l'expertise humaine. L'IA assiste les développeurs, améliore l'efficacité, mais ne remplace ni le jugement ni la gestion de la complexité. La demande de logiciels excède l'offre car la contrainte majeure est la réflexion nécessaire pour gérer cette complexité. Pour les dirigeants : les outils rendent-ils nos développeurs plus efficaces sur les problèmes complexes et réduisent-ils les tâches répétitives ? Le "rêve" de remplacer les développeurs, irréalisable, est un moteur d'innovation créant des outils précieux. Comment creuser des sujets à l'ère de l'IA générative. Quid du partage et la curation de ces recherches ? https://glaforge.dev/posts/2026/02/04/researching-topics-in-the-age-of-ai-rock-solid-webhooks-case-study/ Recherche initiale de l'auteur sur les webhooks en 2019, processus long et manuel. L'IA (Deep Research, Gemini, NotebookLM) facilite désormais la recherche approfondie, l'exploration de sujets et le partage des résultats. L'IA a identifié et validé des pratiques clés pour des déploiements de webhooks résilients, en grande partie les mêmes que celles trouvées précédemment par l'auteur. Génération d'artefacts par l'IA : rapport détaillé, résumé concis, illustration sketchnote, et même une présentation (slide deck). Guillaume s'interroge sur le partage public de ces rapports de recherche générés par l'IA, tout en souhaitant éviter le "AI Slop". Loi, société et organisation Le logiciel menacé par le vibe coding https://www.techbuzz.ai/articles/we-built-a-monday-com-clone-in-under-an-hour-with-ai Deux journalistes de CNBC sans expérience de code ont créé un clone fonctionnel de Monday.com en moins de 60 minutes pour 5 à 15 dollars. L'expérience valide les craintes des investisseurs qui ont provoqué une baisse de 30% des actions des entreprises SaaS. L'IA a non seulement reproduit les fonctionnalités de base mais a aussi recherché Monday.com de manière autonome pour identifier et recréer ses fonctionnalités clés. Cette technique appelée "vibe-coding" permet aux non-développeurs de construire des applications via des instructions en anglais courant. Les entreprises les plus vulnérables sont celles offrant des outils "qui se posent sur le travail" comme Atlassian, Adobe, HubSpot, Zendesk et Smartsheet. Les entreprises de cybersécurité comme CrowdStrike et Palo Alto sont considérées plus protégées grâce aux effets de réseau et aux barrières réglementaires. Les systèmes d'enregistrement comme Salesforce restent plus difficiles à répliquer en raison de leur profondeur d'intégration et de données d'entreprise. Le coût de 5 à 15 dollars par construction permet aux entreprises de prototyper plusieurs solutions personnalisées pour moins cher qu'une seule licence Monday.com. L'expérience soulève des questions sur la pérennité du marché de 5 milliards de dollars des outils de gestion de projet face à l'IA générative. Conférences En complément de l'agenda des conférences de Aurélie Vache, il y a également le site https://javaconferences.org/ (fait par Brian Vermeer) avec toutes les conférences Java à venir ! La liste des conférences provenant de Developers Conferences Agenda/List par Aurélie Vache et contributeurs : 12-13 février 2026 : Touraine Tech #26 - Tours (France) 12-13 février 2026 : World Artificial Intelligence Cannes Festival - Cannes (France) 19 février 2026 : ObservabilityCON on the Road - Paris (France) 6 mars 2026 : WordCamp Nice 2026 - Nice (France) 18 mars 2026 : Jupyter Workshops: AI in Jupyter: Building Extensible AI Capabilities for Interactive Computing - Saint-Maur-des-Fossés (France) 18-19 mars 2026 : Agile Niort 2026 - Niort (France) 20 mars 2026 : Atlantique Day 2026 - Nantes (France) 26 mars 2026 : Data Days Lille - Lille (France) 26-27 mars 2026 : SymfonyLive Paris 2026 - Paris (France) 26-27 mars 2026 : REACT PARIS - Paris (France) 27-29 mars 2026 : Shift - Nantes (France) 31 mars 2026 : ParisTestConf - Paris (France) 31 mars 2026-1 avril 2026 : FlowCon France 2026 - Paris (France) 1 avril 2026 : AWS Summit Paris - Paris (France) 2 avril 2026 : Pragma Cannes 2026 - Cannes (France) 2-3 avril 2026 : Xen Spring Meetup 2026 - Grenoble (France) 7 avril 2026 : PyTorch Conference Europe - Paris (France) 9-10 avril 2026 : Android Makers by droidcon 2026 - Paris (France) 9-11 avril 2026 : Drupalcamp Grenoble 2026 - Grenoble (France) 16-17 avril 2026 : MiXiT 2026 - Lyon (France) 17-18 avril 2026 : Faiseuses du Web 5 - Dinan (France) 22-24 avril 2026 : Devoxx France 2026 - Paris (France) 23-25 avril 2026 : Devoxx Greece - Athens (Greece) 6-7 mai 2026 : Devoxx UK 2026 - London (UK) 12 mai 2026 : Lead Innovation Day - Leadership Edition - Paris (France) 19 mai 2026 : La Product Conf Paris 2026 - Paris (France) 21-22 mai 2026 : Flupa UX Days 2026 - Paris (France) 22 mai 2026 : AFUP Day 2026 Lille - Lille (France) 22 mai 2026 : AFUP Day 2026 Paris - Paris (France) 22 mai 2026 : AFUP Day 2026 Bordeaux - Bordeaux (France) 22 mai 2026 : AFUP Day 2026 Lyon - Lyon (France) 28 mai 2026 : DevCon 27 : I.A. & Vibe Coding - Paris (France) 28 mai 2026 : Cloud Toulouse 2026 - Toulouse (France) 29 mai 2026 : NG Baguette Conf 2026 - Paris (France) 29 mai 2026 : Agile Tour Strasbourg 2026 - Strasbourg (France) 2-3 juin 2026 : Agile Tour Rennes 2026 - Rennes (France) 2-3 juin 2026 : OW2Con - Paris-Châtillon (France) 3 juin 2026 : IA–NA - La Rochelle (France) 5 juin 2026 : TechReady - Nantes (France) 5 juin 2026 : Fork it! - Rouen - Rouen (France) 6 juin 2026 : Polycloud - Montpellier (France) 9 juin 2026 : JFTL - Montrouge (France) 9 juin 2026 : C: - Caen (France) 11-12 juin 2026 : DevQuest Niort - Niort (France) 11-12 juin 2026 : DevLille 2026 - Lille (France) 12 juin 2026 : Tech F'Est 2026 - Nancy (France) 16 juin 2026 : Mobilis In Mobile 2026 - Nantes (France) 17-19 juin 2026 : Devoxx Poland - Krakow (Poland) 17-20 juin 2026 : VivaTech - Paris (France) 18 juin 2026 : Tech'Work - Lyon (France) 22-26 juin 2026 : Galaxy Community Conference - Clermont-Ferrand (France) 24-25 juin 2026 : Agi'Lille 2026 - Lille (France) 24-26 juin 2026 : BreizhCamp 2026 - Rennes (France) 2 juillet 2026 : Azur Tech Summer 2026 - Valbonne (France) 2-3 juillet 2026 : Sunny Tech - Montpellier (France) 3 juillet 2026 : Agile Lyon 2026 - Lyon (France) 6-8 juillet 2026 : Riviera Dev - Sophia Antipolis (France) 2 août 2026 : 4th Tech Summit on Artificial Intelligence & Robotics - Paris (France) 20-22 août 2026 : 4th Tech Summit on AI & Robotics - Paris (France) & Online 4 septembre 2026 : JUG Summer Camp 2026 - La Rochelle (France) 17-18 septembre 2026 : API Platform Conference 2026 - Lille (France) 24 septembre 2026 : PlatformCon Live Day Paris 2026 - Paris (France) 1 octobre 2026 : WAX 2026 - Marseille (France) 1-2 octobre 2026 : Volcamp - Clermont-Ferrand (France) 5-9 octobre 2026 : Devoxx Belgium - Antwerp (Belgium) Nous contacter Pour réagir à cet épisode, venez discuter sur le groupe Google https://groups.google.com/group/lescastcodeurs Contactez-nous via X/twitter https://twitter.com/lescastcodeurs ou Bluesky https://bsky.app/profile/lescastcodeurs.com Faire un crowdcast ou une crowdquestion Soutenez Les Cast Codeurs sur Patreon https://www.patreon.com/LesCastCodeurs Tous les épisodes et toutes les infos sur https://lescastcodeurs.com/

In the second Executive Function episode, Brett sits down with Ryan Lucas, VP of Design at Rippling. Before Rippling, Ryan led design at Retool and co-founded multiple startups, bringing a rare founder's perspective to design leadership. A trained industrial designer, Ryan traces the roots of modern software design back 2,000 years to make the case that products must be useful, usable, and desirable - and above all, used. In today's episode, we discuss: Why design leaders who stop designing stop leading The four pillars every design manager must master How to delegate when you're a perfectionist Why leaders need strong opinions How to scale good judgment What Rippling's operating system teaches about speed and commitments References: Airbnb: https://www.airbnb.com/ Amazon: https://www.amazon.com/ Apple: https://www.apple.com/ Asana: https://www.asana.com/ Brian Chesky: https://www.linkedin.com/in/brianchesky/ CrossFit: https://www.crossfit.com/ Figma: https://www.figma.com/ Honeywell: https://www.honeywell.com/ Liz Sanders: https://www.linkedin.com/in/sandersliz/ Nest: https://store.google.com/category/google_nest Notion: https://www.notion.so/ Parker Conrad: https://www.linkedin.com/in/parkerconrad/ Patrick Collison: https://www.linkedin.com/in/patrickcollison/ Retool: https://retool.com/ Rippling: https://www.rippling.com/ Stripe: https://www.stripe.com/ Where to find Ryan: LinkedIn: https://www.linkedin.com/in/ryanwlucas/ 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 Intro 00:08 What design actually does at a software company 01:40 The roots of design: from industrial design to software 03:29 Useful, usable, desirable — and used 04:49 How design relates to engineering, product, and marketing 08:15 Measuring success as a design leader 12:40 The gap between director and VP-level design leadership 14:23 Why great design leaders jump up and down in altitude 19:26 The four pillars every design manager must master 21:34 Over-indexing on quality and the perfectionist trap 25:11 When lowering the quality bar actually cost the business 27:53 How to build judgment through pattern matching 31:25 How Ryan's design team differs from the rest 34:31 Why Figma is not the source of truth 36:32 How Ryan spends his week: recruiting, crits, and staff meetings 38:39 The "Do/Try/Consider" framework 42:12 The most important decisions of the past year 44:05 Should one-on-ones exist? 46:45 How to scale judgment 50:49 What to look for when hiring your first design leader 54:54 Advice for young designers who want to lead 58:24 Demanding yet supportive: A balanced management style 01:02:43 What Rippling's operating system teaches about execution

I have been thinking a lot about AI lately, and specifically about whether we should be worried about our over-reliance on it. Because if I am being completely honest with myself, I use AI for absolutely everything now. Every email that comes in gets pasted into Claude for analysis. Every project brief gets discussed with it. Every piece of writing gets shaped by it. When Claude goes down, my entire workflow grinds to a halt.So should I be worried about this dependency? Should you?After spending the last few weeks working through this question, I have landed somewhere that might be useful to share. Because I think the conversation about AI is happening right now in organizations everywhere, and the dividing line between those who embrace it and those who resist it matters more than most people realize.The dependency questionWhen I first noticed how reliant I had become on AI, my immediate reaction was concern. I started thinking about all the things that could go wrong. What if Claude disappeared tomorrow? What if I was outsourcing too much of my thinking? What if I was losing critical skills?But then I started looking at all the other dependencies in my working life:If the internet goes down, work stopsIf the power goes off, my life stops.If AWS servers fail (which seems to happen every other week), half the tools I rely on become uselessIf Figma stops working, design work haltsJust one more dependencyWe have built our entire professional lives on top of dependencies we barely think about anymore. AI is just one more in that stack.The question is not really whether we should be dependent on it, because that ship has already sailed for most of us. The question is what kind of dependency we are building.The thinking questionThe more interesting concern for me is whether AI makes us stop thinking. I have heard this worry from a lot of people, and I understand where it comes from. Because when you watch someone paste a problem into ChatGPT and blindly implement whatever comes back, it does look like they have outsourced their brain.But I think this misunderstands what most of us are actually doing with AI.Three layers of thinkingThere are different levels of thinking that happen in any given day:Strategic thinking about project direction, what problems need solving, what approach makes senseAnalytical thinking about whether an idea is sound, whether evidence supports a conclusion, whether a design solves the actual problemMundane thinking about how to word an email, how to structure a document, how to format a proposalAI as a thinking partnerWhat I have found is that AI handles that bottom layer beautifully. When a client sends me a long rambling email with five different questions buried in three paragraphs of context, I no longer spend mental energy untangling it. I paste it into Claude and say, "Summarize the key questions here." Then I think about my answers. I tell Claude what I think about each point. Sometimes I ask for its perspective on one or two where I am genuinely uncertain, not because I cannot think through it myself, but because having a sounding board helps me think better.When I worked in an agency, I had colleagues for this. I would turn to Marcus or Chris and say, "What do you think about this?" I do not have that anymore. AI fills that gap. It does not replace my thinking. It helps me think more clearly by taking away the low-level cognitive load and giving me something to bounce ideas against.The value questionWhere this gets really interesting is in what it lets me deliver to clients.The landing page playbook exampleI worked on a project recently where a client wanted to improve the conversion rate of their landing pages. They had a budget that, in the past, would have stretched to maybe three or four sample landing pages and a conversation about why I built them that way. That would have been useful, but limited. They would have had some examples to work from, but not much guidance on how to replicate the approach themselves.With AI, I was able to create an entire playbook. Detailed guidelines for every component. Design principles explained with examples. A system they could use again and again. I delivered probably four times the value in about a third of the time it would have taken me before. The strategic thinking was all mine. The understanding of what makes landing pages convert came from 30 years of doing this work. But the documentation, the articulation, the packaging of that knowledge into something comprehensive and usable came from working with AI.Why clients still need expertiseMost of my clients will not do this work themselves, even with AI:They do not know what questions to askThey do not have the pattern recognition that comes from seeing hundreds of projectsThey cannot evaluate whether the output is actually good or just sounds convincingThey haven't the time to review and iterate upon the output to improve things.That is what they are paying me for. AI does not replace that expertise. It amplifies what I can do with it.The real conversationI think what bothers me most about the anti-AI sentiment I see is that it misses the point. People post about "AI slop" and declare they are "AI-free" as if that is some kind of badge of honor.The conversation should not be about whether to use AI. That question has already been answered by the market. The conversation should be about how to use it well. How to maintain the strategic thinking while leveraging the tool. How to keep the human insight while letting the machine handle the grunt work. How to deliver more value in less time without sacrificing quality.Because in my experience, the people who need UX professionals are not suddenly going to do it themselves just because AI exists. They still do not have the time. They still do not know what questions to ask. They still cannot evaluate quality. What changes is that the UX professionals who embrace AI can deliver significantly more value than those who resist it.The symbiosis advantageI am not threatened by AI. I am empowered by it:It lets me hold far more complexity in my head than I could beforeIt lets me process larger amounts of informationIt lets me deliver more refined, more thorough, more valuable workAll the things AI does badly (high-level strategy, judging quality, understanding human needs, driving projects forward) are exactly the things clients need me for.So I am leaning into this dependency. Deliberately. Because it allows me to deliver more value in less time. My clients get better work, delivered faster, for the same investment. That is why I am in business. AI has become another tool in my arsenal, like Figma or analytics platforms or any of the other things I rely on to do my job well.

Avichal Garg is co-founder of Electric Capital, one of crypto's most respected early-stage funds, and an early backer of Solana, Kraken, Figma, and Bitwise.THE SHIFT NEWSLETTER

SaaStr 841: Going From Blobs to Billions. Clay's Co-Founder Breaks Down Inbound, Outbound, and AI-Powered Sales. Clay's Co-Founder Varun Anand takes the stage at SaaStr to break down how the company went from paying for claymation blobs before generating any revenue to powering growth workflows for companies like Cursor, Anthropic, and Figma. He explains why brand has always been core to Clay's identity, how their CFO roast videos and creative campaigns are actually capturing mindshare in a world where B2B marketing is painfully boring, and why he pushes back on the "use AI for everything" mentality that's taken over the industry. Varun does a full live demo building an inbound qualification workflow from scratch using real audience volunteers, walking through everything from lead enrichment and waterfall data sourcing to AI-powered scoring, personalized meme generation, research brief creation, and CRM updates. He also brings audience members on stage to do live growth hacking for their actual business problems. Beyond the product, this session goes deep on hiring. Varun shares the origin story of the GTM Engineer role, how it went from an internal job title for Clay's non-traditional sales team to the most in-demand position in B2B SaaS, and what he actually looks for when evaluating candidates (hint: it's creativity, not a traditional sales background). He talks about Clay's take-home process, work trials, why they hire generalists who commit to specific roles, and the surprising backgrounds of some of their best hires. Whether you're building out your go-to-market motion, thinking about how to use AI without losing what makes your brand unique, or just trying to figure out what a GTM Engineer actually does, this session covers it all. --------------------- This episode is Sponsored in part by HappyFox: Imagine having AI agents for every support task — one that triages tickets, another that catches duplicates, one that spots churn risks. That'd be pretty amazing, right? HappyFox just made it real with Autopilot. These pre-built AI agents deploy in about 60 seconds and run for as low as 2 cents per successful action. All of it sits inside the HappyFox omnichannel, AI-first support stack — Chatbot, Copilot, and Autopilot working as one. Check them out at happyfox.com/saastr   ---------------------   Hey everybody, the biggest B2B + AI event of the year will be back - SaaStr AI in the SF Bay Area, aka the SaaStr Annual, will be back in May 2026.    With 68% VP-level and above, 36% CEOs and founders and a growing 25% AI-first professional, this is the very best of the best S-tier attendees and decision makers that come to SaaStr each year.     But here's the reality, folks: the longer you wait, the higher ticket prices can get. Early bird tickets are available now, but once they're gone, you'll pay hundreds more so don't wait.    Lock in your spot today by going to podcast.saastrannual.com to get my exclusive discount SaaStr AI SF 2026. We'll see you there.

Davy and PJ discuss the "liability" of stale documentation and why AI still needs human oversight. They explore using Figma's API to automate visual updates and kill the "pain in the butt" of manual maintenance.

Maxima is building AI agents that automate enterprise accounting while maintaining the auditability and control standards finance teams require. In a recent episode of BUILDERS, we sat down with Yogi Goel, CEO and Co-Founder of Maxima, to explore his eight-year journey at Rubrik from Series C through IPO, and how those lessons shaped his approach to solving the 70-80% of finance time currently wasted on manual work. Topics Discussed: Why Rubrik's approach—entering stagnant markets with first-principles thinking—became Maxima's blueprint Securing $3K-$5K POC commitments from Figma mockups before writing code Why Scale AI and Rippling rejected a point solution and demanded 3-4 modules from day one The compound startup model: building multiple products simultaneously to meet buyer expectations How 17% of CFOs are adopting AI tools today (vs 51% in software development) Why finance teams view AI agents as "digital college freshmen" who need proof of work Hiring from YouTube Studios, Apple, and Robinhood instead of legacy finance software companies How NetSuite World conference booth sizes revealed the data integration infrastructure gap The $3K-$5K validation threshold that proved finance pain was urgent enough to pay pre-product GTM Lessons For B2B Founders: Demand generation unlocks engineering potential: Yogi learned from his Rubrik mentors: "focus on demand and if you have great engineers then they will solve the problems." Maxima built products in 2-3 months they didn't initially know were technically feasible—because customer demand pulled the engineering team forward. For founders with strong technical teams, customer demand should drive the roadmap, not engineering's comfort zone. Trust your engineers to solve hard problems when customers are waiting. $3K-$5K is the pre-product validation threshold: Before writing any code, Yogi secured POC commitments at this price point based solely on Figma mockups. This isn't about revenue—it's about proving urgency. Verbal interest means nothing. Small pilot commitments mean "we'll try it someday." But $3K-$5K pre-product means "this problem is urgent enough to pay before seeing a working solution." Use this threshold to separate real pain from polite interest. Sophisticated buyers will reject your narrow MVP: Scale AI and Rippling told Maxima explicitly: "If you will only build this one thing, we will not buy. You have to commit to building three, four modules." Conventional wisdom says start narrow, but enterprise buyers with complex workflows won't adopt point solutions that create new integration headaches. When sophisticated buyers articulate their real buying criteria, ignore the startup playbook. Yogi built a "compound startup" with 4-5 modules from day one because that's what the market demanded. Target acute pain over easy access: Early-stage companies (10-30 people) were easier to reach but finance wasn't urgent enough. At that scale, it's "build product, ship product"—finance operations aren't broken enough to warrant urgent attention. Companies at 500-1,000+ employees have finance teams drowning in manual work that prevents strategic contribution. Target where pain justifies urgent action and budget exists, not where calendar access is easiest. Hire intensity and first-principles thinking over domain knowledge: Maxima deliberately hired zero engineers from legacy finance software companies. Their frontend engineer came from YouTube Studios. Others came from Apple, Robinhood, Netflix—none with financial product experience. Yogi's three hiring criteria: "incredible intensity, huge confidence in themselves, and fast thinking mode." Domain expertise creates pattern-matching to old solutions. First-principles thinking creates breakthrough products. One team member didn't finish high school but is "one of the best out there." Make AI explainable or finance teams won't adopt: Finance teams adopted faster than expected because Maxima showed every calculation step. "If they can prove by looking at the Math, you know, 18 plus 88 plus 36 is X. And I can see the step of the work, they are willing to give it to them." This isn't about fancy UX—it's about auditor-grade proof of work. Finance professionals won't trust black box outputs. Build transparency into the product architecture, not as an afterthought. This explainability became Maxima's competitive moat. Conference booth sizes reveal infrastructure gaps: At NetSuite World, the largest booths weren't ERP vendors or payment processors—they were data integration companies. This single observation validated that enterprises are desperately solving data fragmentation problems. Companies manually download from Stripe, Snowflake, Salesforce weekly to build Excel pivots. Maxima invested in upstream integrations as core infrastructure from day one. Use industry conferences to validate where companies are spending money on workarounds—that's where infrastructure gaps exist. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

What if the best product decision is saying “no” to what everyone else is building?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Alexander Danilowicz, founder and CEO of Magic Patterns, to unpack why his AI prototyping tool is the only one refusing to add backend features—even when competitors like Lovable, Bolt, and v0 are racing in that direction. Alex explains how focusing exclusively on front-end code leads to higher quality prototyping, why many use cases don't actually need a database, and how product teams at large companies can't risk connecting production data to prototyping tools anyway.They explore what it takes to maintain conviction when investors, customers, and the entire market seem to be moving the opposite way. Alex shares how using your own product daily keeps you honest about what's actually broken, why real user feedback looks different from “fake” feature requests (like “add dark mode”), and how a strong co-founding relationship helps you resist temptation when external pressure mounts.If you're a product leader wrestling with feature requests that don't align with your vision, trying to figure out when to follow the market versus when to trust your gut, or building tools in the AI coding space, 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

The panel looks at Adobe's past dominance, current challenges, and uncertain future as AI tools and lower-cost alternatives reshape the creative landscape. Chuck Joiner, David Ginsburg, Eric Bolden, Marty Jencius, Web Bixby, Jim Rea, and Jeff Gamet cover how generative AI, subscription fatigue, collaboration gaps, and competitors like Affinity, Canva, and Figma are changing who really needs Adobe services such as Creative Cloud, while reflecting on historical tech shifts and whether Adobe's next chapter has already been written. A documentary recommendation wraps up this session. MacVoices is supported by Incogni. Take your personal data back with Incogni! Get 60% off an annual plan at https://incogni.com/chuck and use code “chuck" at checkout. Show Notes: Chapters: 00:00 Adobe's past, present, and AI disruption01:12 How AI fits into professional creative workflows03:09 Adobe's difficulty pivoting in a fast-moving market04:29 Desktop publishing history: PageMaker, Quark, and InDesign07:09 Public perception of AI “replacing” Adobe tools09:26 Photoshop Elements and missed marketing opportunities12:41 Subscription fatigue and rising alternatives14:04 Collaboration challenges and Canva/Affinity momentum17:45 Shift from print-centric tools to digital workflows22:13 Designers leaving Creative Cloud behind25:12 Adobe's legacy status and future positioning31:31 The Thinking Game documentary recommendation Links:Adobe's stock has slumped more than 45% since the end of 2023, reflecting analyst concerns over the threat of AI-driven disruption to SaaS companieshttps://www.bloomberg.com/news/articles/2026-01-13/adobe-analysts-turn-most-bearish-since-2013-as-ai-threat-looms The Thinking Game | Full documentary | Tribeca Film Festival official selectionhttps://www.youtube.com/watch?v=d95J8yzvjbQ Guests: Web Bixby has been in the insurance business for 40 years and has been an Apple user for longer than that.You can catch up with him on Facebook, Twitter, and LinkedIn, but prefers Bluesky. Eric Bolden is into macOS, plants, sci-fi, food, and is a rural internet supporter. You can connect with him on Twitter, by email at embolden@mac.com, on Mastodon at @eabolden@techhub.social, on his blog, Trending At Work, and as co-host on The Vision ProFiles podcast Jeff Gamet is a technology blogger, podcaster, author, and public speaker. Previously, he was The Mac Observer's Managing Editor, and the TextExpander Evangelist for Smile. He has presented at Macworld Expo, RSA Conference, several WordCamp events, along with many other conferences. You can find him on several podcasts such as The Mac Show, The Big Show, MacVoices, Mac OS Ken, This Week in iOS, and more. Jeff is easy to find on social media as @jgamet on Twitter and Instagram, jeffgamet on LinkedIn., @jgamet@mastodon.social on Mastodon, and on his YouTube Channel at YouTube.com/jgamet. David Ginsburg is the host of the weekly podcast In Touch With iOS where he discusses all things iOS, iPhone, iPad, Apple TV, Apple Watch, and related technologies. He is an IT professional supporting Mac, iOS and Windows users. Visit his YouTube channel at https://youtube.com/daveg65 and find and follow him on Twitter @daveg65 and on Mastodon at @daveg65@mastodon.cloud. Dr. Marty Jencius has been an Associate Professor of Counseling at Kent State University since 2000. He has over 120 publications in books, chapters, journal articles, and others, along with 200 podcasts related to counseling, counselor education, and faculty life. His technology interest led him to develop the counseling profession ‘firsts,' including listservs, a web-based peer-reviewed journal, The Journal of Technology in Counseling, teaching and conferencing in virtual worlds as the founder of Counselor Education in Second Life, and podcast founder/producer of CounselorAudioSource.net and ThePodTalk.net. Currently, he produces a podcast about counseling and life questions, the Circular Firing Squad, and digital video interviews with legacies capturing the history of the counseling field. This is also co-host of The Vision ProFiles podcast. Generally, Marty is chasing the newest tech trends, which explains his interest in A.I. for teaching, research, and productivity. Marty is an active presenter and past president of the NorthEast Ohio Apple Corp (NEOAC). Jim Rea built his own computer from scratch in 1975, started programming in 1977, and has been an independent Mac developer continuously since 1984. He is the founder of ProVUE Development, and the author of Panorama X, ProVUE's ultra fast RAM based database software for the macOS platform. He's been a speaker at MacTech, MacWorld Expo and other industry conferences. Follow Jim at provue.com and via @provuejim@techhub.social on Mastodon. Support:      Become a MacVoices Patron on Patreon     http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:     http://macvoices.com      Twitter:     http://www.twitter.com/chuckjoiner     http://www.twitter.com/macvoices      Mastodon:     https://mastodon.cloud/@chuckjoiner      Facebook:     http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:     http://www.facebook.com/macvoices/      MacVoices Group on Facebook:     http://www.facebook.com/groups/macvoice      LinkedIn:     https://www.linkedin.com/in/chuckjoiner/      Instagram:     https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes     Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

The panel looks at Adobe's past dominance, current challenges, and uncertain future as AI tools and lower-cost alternatives reshape the creative landscape. Chuck Joiner, David Ginsburg, Eric Bolden, Marty Jencius, Web Bixby, Jim Rea, and Jeff Gamet cover how generative AI, subscription fatigue, collaboration gaps, and competitors like Affinity, Canva, and Figma are changing who really needs Adobe services such as Creative Cloud, while reflecting on historical tech shifts and whether Adobe's next chapter has already been written. A documentary recommendation wraps up this session. MacVoices is supported by Incogni. Take your personal data back with Incogni! Get 60% off an annual plan at https://incogni.com/chuck and use code "chuck" at checkout. Show Notes: Chapters: 00:00 Adobe's past, present, and AI disruption 01:12 How AI fits into professional creative workflows 03:09 Adobe's difficulty pivoting in a fast-moving market 04:29 Desktop publishing history: PageMaker, Quark, and InDesign 07:09 Public perception of AI "replacing" Adobe tools 09:26 Photoshop Elements and missed marketing opportunities 12:41 Subscription fatigue and rising alternatives 14:04 Collaboration challenges and Canva/Affinity momentum 17:45 Shift from print-centric tools to digital workflows 22:13 Designers leaving Creative Cloud behind 25:12 Adobe's legacy status and future positioning 31:31 The Thinking Game documentary recommendation Links: Adobe's stock has slumped more than 45% since the end of 2023, reflecting analyst concerns over the threat of AI-driven disruption to SaaS companies https://www.bloomberg.com/news/articles/2026-01-13/adobe-analysts-turn-most-bearish-since-2013-as-ai-threat-looms The Thinking Game | Full documentary | Tribeca Film Festival official selection https://www.youtube.com/watch?v=d95J8yzvjbQ Guests: Web Bixby has been in the insurance business for 40 years and has been an Apple user for longer than that.You can catch up with him on Facebook, Twitter, and LinkedIn, but prefers Bluesky. Eric Bolden is into macOS, plants, sci-fi, food, and is a rural internet supporter. You can connect with him on Twitter, by email at embolden@mac.com, on Mastodon at @eabolden@techhub.social, on his blog, Trending At Work, and as co-host on The Vision ProFiles podcast Jeff Gamet is a technology blogger, podcaster, author, and public speaker. Previously, he was The Mac Observer's Managing Editor, and the TextExpander Evangelist for Smile. He has presented at Macworld Expo, RSA Conference, several WordCamp events, along with many other conferences. You can find him on several podcasts such as The Mac Show, The Big Show, MacVoices, Mac OS Ken, This Week in iOS, and more. Jeff is easy to find on social media as @jgamet on Twitter and Instagram, jeffgamet on LinkedIn., @jgamet@mastodon.social on Mastodon, and on his YouTube Channel at YouTube.com/jgamet. David Ginsburg is the host of the weekly podcast In Touch With iOS where he discusses all things iOS, iPhone, iPad, Apple TV, Apple Watch, and related technologies. He is an IT professional supporting Mac, iOS and Windows users. Visit his YouTube channel at https://youtube.com/daveg65 and find and follow him on Twitter @daveg65 and on Mastodon at @daveg65@mastodon.cloud. Dr. Marty Jencius has been an Associate Professor of Counseling at Kent State University since 2000. He has over 120 publications in books, chapters, journal articles, and others, along with 200 podcasts related to counseling, counselor education, and faculty life. His technology interest led him to develop the counseling profession 'firsts,' including listservs, a web-based peer-reviewed journal, The Journal of Technology in Counseling, teaching and conferencing in virtual worlds as the founder of Counselor Education in Second Life, and podcast founder/producer of CounselorAudioSource.net and ThePodTalk.net. Currently, he produces a podcast about counseling and life questions, the Circular Firing Squad, and digital video interviews with legacies capturing the history of the counseling field. This is also co-host of The Vision ProFiles podcast. Generally, Marty is chasing the newest tech trends, which explains his interest in A.I. for teaching, research, and productivity. Marty is an active presenter and past president of the NorthEast Ohio Apple Corp (NEOAC). Jim Rea built his own computer from scratch in 1975, started programming in 1977, and has been an independent Mac developer continuously since 1984. He is the founder of ProVUE Development, and the author of Panorama X, ProVUE's ultra fast RAM based database software for the macOS platform. He's been a speaker at MacTech, MacWorld Expo and other industry conferences. Follow Jim at provue.com and via @provuejim@techhub.social on Mastodon. Support:      Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:      http://macvoices.com      Twitter:      http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices      Mastodon:      https://mastodon.cloud/@chuckjoiner      Facebook:      http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:      http://www.facebook.com/macvoices/      MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice      LinkedIn:      https://www.linkedin.com/in/chuckjoiner/      Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

In the episode of Redefining AI, host Lauren Hawker Zafer speaks with Sangeet Paul Choudary, the bestselling author of Platform Revolution and the 2025 Thinkers50 Strategy Award winner for his latest book, Reshuffle.Sangeet argues that we are currently repeating the early mistakes of the Cloud era, viewing AI through the narrow lens of productivity and intelligence benchmarks (like GPT-5) rather than the structural reorganization of work itself. Lauren and Sangeet dive deep into why the next 18 months will bring a massive "narrative correction" as organizations move from asking what AI is to what it does to their capital allocation and organizational architecture.In this episode, you will learn: The Intelligence Trap: Why focusing on brute-force AI performance is a distraction from true system restructuring.The Workforce Split: How to lead through the divide of "Blind Believers" and "Blind Rejectors."The Reshuffle Framework: Why AI is the "missing glue" for complex systems and how to redistribute work now that knowledge is no longer scarce.AI-Native vs. AI-Adopter: How to tell if a company is truly transforming or just "tacking on" tools (The Adobe vs. Figma distinction).Sangeet Paul Choudary breaks down the fundamental shift from AI-adopting to AI-native, and unpacks the most relevant issue in 2026:In an AI-adopting company, the person is the "node" and AI is the tool. In an AI-native company, the system is the node, and work is redistributed based on where intelligence (human or artificial) is most effective.Here is a sharp, condensed way to state that principle:The true shift isn't about augmenting individuals; it's about rethinking the architecture of the organization itself. If you assume work must still be organized around individual silos, you aren't being AI-native. Real transformation happens when you stop asking how AI helps the person and start asking how work should be redistributed and restructured now that intelligence is a decentralized utility.00:00 –  Sangeet Paul Choudary, author of Reshuffle, 2025 Thinkers50 Strategy Award winner 01:30 – The Problem with the "Intelligence-First" AI Narrative02:50 – Beyond Intelligence: How AI Restructures Organizations04:00 – The Winners and Losers of the AI Value Pie05:10 – Moving from Task-Level AI to System-Level Assumptions06:20 – Lessons from the Cloud: Why History Rhymes with AI08:00 – Adobe vs. Figma: A Case Study in Native Architecture09:40 – Reimagining Returns: Breaking the Productivity Optimization Loop11:15 – 2025 Prediction: The Tension, Transition, and Transformation Phases12:50 – Avoiding the Split: Blind Believers vs. Blind Rejectors14:10 – The 18-Month Narrative Correction: From GPT-5 Hype to ROI Reality15:30 – How to Spot a Genuinely AI-Native Company17:00 – Rethinking Organizational Design: Distributed vs. Individual Work18:40 – Why AI is a Strategy and Capital Allocation Decision (Not IT)19:50 – Closing: Aligning Sales and Leadership with the New AI Architecture 

Eighteen months ago, Tyler Cloutier appeared on the show with what sounded like an ambitious (some might say crazy) plan: build a new distributed database from scratch, then use it to power a massively multiplayer online game. That's two of the hardest problems in software, tackled simultaneously. But sometimes the best infrastructure comes from solving your own impossible problems.The game, Bitcraft, has now launched on Steam. SpacetimeDB has hit version 1.0. And Tyler returns to share what actually happened when theory met production reality. We cover the launch day performance disasters (including a cascading failure caused by logging while holding a lock), why single-threaded execution running entirely from L1 cache can outperform sophisticated multi-threaded approaches by two orders of magnitude, and how the database's reducer model - borrowed from functional programming - enables zero-downtime code deployments. We also get into how SpacetimeDB is expanding beyond games with TypeScript support and React hooks that make building real-time multiplayer web apps surprisingly simple.If you're building anything where multiple users need to see the same data update in real time - which, as Tyler points out, describes most successful applications from Figma to Facebook - SpacetimeDB's approach of treating every app as a multiplayer game might be worth understanding.--Support Developer Voices on Patreon: https://patreon.com/DeveloperVoicesSupport Developer Voices on YouTube: https://www.youtube.com/@DeveloperVoices/joinSpacetimeDB: https://spacetimedb.com/SpacetimeDB on GitHub: https://github.com/clockworklabs/SpacetimeDBOur previous episode with Tyler: https://youtu.be/roEsJcQYjd8Clockwork Labs: https://clockworklabs.io/Bitcraft Online: https://bitcraftonline.com/Bitcraft on Steam: https://store.steampowered.com/app/3454650/BitCraft_OnlineWebAssembly: https://webassembly.org/Flecs (ECS for C/C++): https://www.flecs.dev/flecs/TigerBeetle: https://tigerbeetle.com/CockroachDB: https://www.cockroachlabs.com/Google Cloud Spanner: https://cloud.google.com/spannerErlang: https://www.erlang.org/Apache Kafka: https://kafka.apache.org/Tyler Cloutier on X: https://x.com/TylerFCloutierTyler Cloutier on LinkedIn: https://www.linkedin.com/in/tylercloutier/--Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.socialKris on Mastodon: http://mastodon.social/@krisajenkinsKris on LinkedIn: https://www.linkedin.com/in/krisjenkins/0:00 Intro2:01 The Architecture of SpacetimeDB5:01 Client-Side Prediction in Multiplayer Games11:00 Reducers and Event Streaming15:00 Launching Bitcraft on Steam19:00 Debugging Launch Performance Problems26:56 Hot-Swapping Server Code Without Downtime30:01 In-Memory Tables and Query Optimization42:00 Is SpacetimeDB Only For Games?51:00 Performance Benchmarking For Web Workloads55:00 Why Single-Threaded Beats Multi-Threaded1:00:01 Multi-Version Concurrency Control Trade-offs1:05:01 Sharding Data Across Multiple Nodes1:10:56 Inter-Module Communication and Actor Models1:17:00 Replication and the Write-Ahead Log1:24:00 Supported Client Languages1:29:00 Getting Started With SpacetimeDB1:39:02 Outro

Description link: Want access to Lior Albeck's AI toolkit? Get it here: https://clickhubspot.com/eb1adb Ep. 397 If you're not building systems for creative work, are you falling behind? Kipp and Lior Albeck (CEO and Co-Founder of Weavy) dive into how AI is radically changing creative marketing and why system-building is now essential to stay competitive. Learn more on how to break down the mindshift every team needs, how to future-proof your creative assets, and the secrets behind building an AI-native company—plus, practical ways anyone can start systematizing their creative process today. Mentions Lior Albeck https://www.linkedin.com/in/lioralbeck/ Weavy https://www.weavy.ai/ Figma https://www.figma.com/ Zapier https://zapier.com/ Nano Banana https://gemini.google/overview/image-generation/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt We're creating our next round of content and want to ensure it tackles the challenges you're facing at work or in your business. To understand your biggest challenges we've put together a survey and we'd love to hear from you! https://bit.ly/matg-research Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Join our community https://landing.connect.com/matg Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

What if the thing holding you back from building a public presence is exactly what would make you stand out?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Mallory Contois, VP of Growth at Maven, to unpack why this is the perfect moment for product leaders to start sharing publicly—even if they don't feel polished, interesting, or like they have it all figured out. Mallory explains how we're leaving the era of glossy, aspirational influencer content and entering one where audiences crave authenticity, relatability, and actionable takeaways.They tackle the three biggest mindsets that hold people back: the “influencer hater” who rejects performative content, the person who doesn't think they're interesting enough, and the professional who believes their work should speak for itself. Mallory breaks down why good work alone isn't enough, why consistency beats virality, and how to find your authentic voice without trying to game algorithms or chase trends.If you're a product leader who's been holding back from sharing publicly, wondering whether anyone would find your perspective valuable, or questioning whether personal branding is worth the effort—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

Dr. Becky Kennedy is a clinical psychologist, the bestselling author of Good Inside, and the founder of a parenting platform used by millions. Known for her practical, psychology-based approach to parenting, Dr. Becky shares how the same principles that help parents raise resilient children can make you a much more effective leader. In this conversation, she breaks down why all human systems—whether families or companies—operate on the same fundamental principles, and how understanding these dynamics can make you more effective in every relationship.We discuss:1. Why repair—not perfection—defines strong leadership2. Why you need to connect before you correct to build cooperation and trust3. The “most generous interpretation” framework for handling difficult behaviors4. How to correctly set boundaries (vs. making requests)5. The power of “I believe you, and I believe in you”6. What it looks like to be a “sturdy” leader—Brought to you by:Merge—Fast, secure integrations for your products and agents: https://merge.dev/lennyMetaview—The AI platform for recruiting: https://metaview.ai/lennyFramer—Builder better websites faster: https://framer.com/lenny—Episode transcript: https://www.lennysnewsletter.com/p/dr-becky-on-the-surprising-overlap—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Dr. Becky Kennedy:• X: https://x.com/GoodInside• LinkedIn: https://www.linkedin.com/in/drbecky• Instagram: https://www.instagram.com/drbeckyatgoodinside• TikTok: https://www.tiktok.com/@drbeckyatgoodinside• Website: https://www.goodinside.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Dr. Becky Kennedy(05:14) Connecting parenting and leadership(08:40) The power of repair(11:05) Connecting before correcting(17:45) Good Inside framework at work(22:08) The most generous interpretation (MGI)(25:46) Curiosity over judgment(27:07) Understanding behavior change(31:08) What potty training can teach us about workplace behavior(34:40) Naming your intention(35:41) Sturdy leadership(40:52) How to set boundaries well(46:33) The role of leadership and consensus(50:50) The importance of being “locatable”(52:40) A powerful story of betrayal and realization(57:12) Building resilience over happiness(01:00:34) The power of the phrase “I believe you, and I believe in you.”(01:09:08) The Good Inside community and resources(01:16:22) AI corner(01:19:52) Good Inside's mission(01:22:26) Lightning round and final thoughts—Referenced:• Shreyas Doshi on pre-mortems, the LNO framework, the three levels of product work, why most execution problems are strategy problems, and ROI vs. opportunity cost thinking: https://www.lennysnewsletter.com/p/episode-3-shreyas-doshi• Radical Candor: From theory to practice with author Kim Scott: https://www.lennysnewsletter.com/p/radical-candor-from-theory-to-practice• From ChatGPT to Instagram to Uber: The quiet architect behind the world's most popular products | Peter Deng: https://www.lennysnewsletter.com/p/the-quiet-architect-peter-deng• Punch: https://en.wikipedia.org/wiki/Punch_(play)• Figma: https://www.figma.com• Andrew Hogan on LinkedIn: https://www.linkedin.com/in/ahhogan• Replit: https://replit.com• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• Lovable: https://lovable.dev• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Claude: https://claude.ai• ChatGPT: https://chatgpt.com• Secrets We Keep on Netflix: https://www.netflix.com/title/81697668• K Pop Demon Hunters on Netflix: https://www.netflix.com/title/81498621• Liberty puzzles: https://libertypuzzles.com—Recommended books:• Radical Candor: Be a Kick-Ass Boss Without Losing Your Humanity: https://www.amazon.com/Radical-Candor-Revised-Kick-Ass-Humanity/dp/1250235375• Good Inside: A Practical Guide to Resilient Parenting Prioritizing Connection Over Correction: https://www.amazon.com/Good-Inside-Guide-Becoming-Parent/dp/0063159481• Leave Me Alone!: A Good Inside Story About Deeply Feeling Kids: https://www.amazon.com/Leave-Me-Alone-Inside-Feeling/dp/1250413117• The Power of Moments: Why Certain Experiences Have Extraordinary Impact: https://www.amazon.com/Power-Moments-Certain-Experiences-Extraordinary/dp/1501147765/• The Messy Middle: Finding Your Way Through the Hardest and Most Crucial Part of Any Bold Venture: https://www.amazon.com/Messy-Middle-Finding-Through-Hardest/dp/0735218072• Creativity, Inc.: Overcoming the Unseen Forces That Stand in the Way of True Inspiration: https://www.amazon.com/Creativity-Inc-Expanded-Overcoming-Inspiration/dp/0593594649—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Spotlight Fourteen History does not repeat it rhymes. Spotlight fourteen is taken from the upcoming Redefining Episode on The Great AI Shuffle with Sangeet Paul Choudary. Sangeet Paul Choudary, author of Reshuffle, breaks down how AI is fundamentally transforming workflows, organizational structures, and business strategy. Moving beyond the idea of AI as just an intelligence tool, he explains why AI's real power lies in restructuring systems and unlocking entirely new sources of value.In this upcoming episode, Choudary explores what it means to build AI-native companies, why incumbents must rethink their identities, and how examples like Figma versus Adobe illustrate the coming shift. He also predicts a market correction and narrative reset around AI over the next 3–4 years, offering guidance for leaders on capital allocation, AI investments, and long-term strategy.The conversation dives into AI's role in regulated industries, its impact on sales and go-to-market strategies, and what executives must do now to stay competitive in an AI-driven economy.Topics include:AI-native companies, future of work, workflows, organizational design, enterprise AI, strategy, regulation, sales transformation, and innovation leadership.Who is Sangeet Paul ChoudarySangeet Choudary is the best-selling co-author of Platform Revolution and the author of the new book Reshuffle that was awarded the 2025 Thinkers50 Strategy Award for The most impactful idea in the field of strategy. He has advised CEOs at more than 40 Fortune 500 companies as well as pre-IPO tech firms. He is currently a Senior Fellow at the University of California, Berkeley, and has presented at leading global forums, including the G20 Summit, the World50 Summit, and the World Economic Forum.

In this episode, we explore Anthropic's new interactive Cloud apps and integrations with popular workplace tools like Slack, Canva, and Figma. We also discuss how Anthropic is facing unique challenges in hiring engineers because their own AI models are now outperforming human applicants in technical assessments.Chapters00:00 Anthropic's Cloud Updates01:45 Interactive Cloud Apps04:49 Model Context Protocol & Co-Work08:17 Agent Permissions & Security Concerns13:42 AI Outperforms Human Applicants See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore Anthropic's new interactive Cloud apps and integrations with popular workplace tools like Slack, Canva, and Figma. We also discuss how Anthropic is facing unique challenges in hiring engineers because their own AI models are now outperforming human applicants in technical assessments.Chapters00:00 Anthropic's Cloud Updates01:45 Interactive Cloud Apps04:49 Model Context Protocol & Co-Work08:17 Agent Permissions & Security Concerns13:42 AI Outperforms Human Applicants See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore Anthropic's new interactive Cloud apps and integrations with popular workplace tools like Slack, Canva, and Figma. We also discuss how Anthropic is facing unique challenges in hiring engineers because their own AI models are now outperforming human applicants in technical assessments.Chapters00:00 Anthropic's Cloud Updates01:45 Interactive Cloud Apps04:49 Model Context Protocol & Co-Work08:17 Agent Permissions & Security Concerns13:42 AI Outperforms Human Applicants

In this episode, we explore Anthropic's new interactive Cloud apps and integrations with popular workplace tools like Slack, Canva, and Figma. We also discuss how Anthropic is facing unique challenges in hiring engineers because their own AI models are now outperforming human applicants in technical assessments.Chapters00:00 Anthropic's Cloud Updates01:45 Interactive Cloud Apps04:49 Model Context Protocol & Co-Work08:17 Agent Permissions & Security Concerns13:42 AI Outperforms Human Applicants See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore Anthropic's new interactive Cloud apps and integrations with popular workplace tools like Slack, Canva, and Figma. We also discuss how Anthropic is facing unique challenges in hiring engineers because their own AI models are now outperforming human applicants in technical assessments.Chapters00:00 Anthropic's Cloud Updates01:45 Interactive Cloud Apps04:49 Model Context Protocol & Co-Work08:17 Agent Permissions & Security Concerns13:42 AI Outperforms Human Applicants See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode, we explore Anthropic's new interactive Cloud apps and integrations with popular workplace tools like Slack, Canva, and Figma. We also discuss how Anthropic is facing unique challenges in hiring engineers because their own AI models are now outperforming human applicants in technical assessments.Chapters00:00 Anthropic's Cloud Updates01:45 Interactive Cloud Apps04:49 Model Context Protocol & Co-Work08:17 Agent Permissions & Security Concerns13:42 AI Outperforms Human Applicants

In this episode, we explore Anthropic's new interactive Cloud apps and integrations with popular workplace tools like Slack, Canva, and Figma. We also discuss how Anthropic is facing unique challenges in hiring engineers because their own AI models are now outperforming human applicants in technical assessments.Chapters00:00 Anthropic's Cloud Updates01:45 Interactive Cloud Apps04:49 Model Context Protocol & Co-Work08:17 Agent Permissions & Security Concerns13:42 AI Outperforms Human Applicants See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

What if AI didn't just give you answers—but helped you understand how the best operators actually think?In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Casey Winters, former growth leader at Pinterest and Eventbrite, to unpack why he's building SuperMe, an AI-native professional network designed around perspective, not content or virality.Casey shares why meaningful expertise has disappeared from public platforms, how knowledge has moved into private networks, and why most AI tools miss the thing people actually want: judgment. The conversation explores how AI can responsibly capture a person's thinking from real artifacts (conversations, writing, podcasts), how trust and consent must be designed into these systems, and why scaling access to expertise doesn't mean replacing humans.They also dive into mentorship, career leverage, and why peer learning often matters more than traditional top-down advice. If you're a founder, operator, or product leader thinking deeply about AI, knowledge-sharing, and the future of professional networks, this episode offers a thoughtful and opinionated look at what comes next.Big thanks to Adam Fishman for introducing us to Casey!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

BONUS: Thinking Like an Architect in the Age of AI-Assisted Coding How can engineers leverage AI to write better code—and think like architects to build systems that truly scale? In this episode, Brian Childress, a CTO and software architect with over 15 years of experience, shares hard-won lessons from teams using AI coding tools daily, and explains why the real challenge isn't just writing code—it's designing systems that scale with users, features, and teams. The Complexity Trap: When AI Multiplies Our Problems "Most engineering projects and software engineers themselves lean more towards complexity, and I find that that complexity really is multiplied when we bring in the power of AI and its ability to write just tons and tons and tons of code."   Brian has observed a troubling pattern: AI tools can generate deeply nested components with complex data flows that technically work but are nearly impossible to understand or maintain. When teams don't guide AI through architectural decisions, they end up with code that becomes "a little too complex for us to understand what is actually going on here." The speed at which AI produces code makes understanding the underlying problem even more critical—we can solve problems quickly, but we must ensure we're solving them the right way. In this segment, we mention our longer AI Assisted Coding podcast series. Check that out for further insights and different perspectives on how our software community is learning to make better use of AI Assisted Coding tools.  Vibe Coding Has Its Place—But Know Its Limits "Vibe coding is incredibly powerful for designers and product owners who want to prompt until they get something that really demonstrates what they're trying to do."   Brian sees value across the entire spectrum from vibe coding to architect-driven development. Vibe coding allows teams to move from wireframes and Figma prototypes to actual working code much faster, enabling quicker validation with real customers. The key distinction is knowing when to use each approach:   Vibe coding works well for rapid prototyping and testing whether something has value Architect thinking becomes essential when building production systems that need to scale and be maintained What Does "Thinking Like an Architect" Actually Mean? "When I'm thinking more like an architect, I'm thinking more around how bigger components, higher level components start to fit together."   The architect mindset shifts focus from "how do I work within a framework" to "what is the problem I'm really solving?" Brian emphasizes that technology is actually the easiest part of what engineers do—you can Google or AI your way to a solution. The harder work is ensuring that the solution addresses the real customer need. An architect asks: How can I simplify? How can I explain this to someone else, technical or non-technical? The better you can explain it, the better you understand it. AI as Your Thought Partner "What it really forces us to do is to be able to explain ourselves better. I find most software engineers will hide behind complexity because they don't understand the problem."   Brian uses AI as a collaborative thought partner rather than just a code generator. He explains the problem, shares his thought process, and then strategizes back and forth—looking for questions that challenge his thinking. This approach forces engineers to communicate clearly instead of hiding behind technical jargon. The AI becomes like having a colleague with an enormous corpus of knowledge who can see solutions you might never have encountered in your career. Simplicity Through Four Shapes "I basically use four shapes to be able to diagram anything, and if I can't do that, then we still have too much complexity. It's a square, a triangle, a circle, and a line."   When helping colleagues shift from code-writing to architect-thinking, Brian insists on dead simplicity. If you can diagram a system—from customer-facing problems down to code component breakdowns, data flow, and integrations—using only these four basic shapes, you've reached true understanding. This simplification creates that "light bulb moment" where engineers suddenly get it and can translate understanding into code while in flow state. Making AI Work Culturally: Leading by Example "For me as a leader, as a CTO, I need to show my team this is how I'm using it, this is where I'm messing up with it, showing that it's okay."   Brian addresses the cultural challenge head-on: mid-level and senior engineers often resist AI tools, fearing job displacement or having to support "AI slop." His approach is to frame AI as a new tool to learn—just like Google and Stack Overflow were in years past—rather than a threat. He openly shares his experiments, including failures, demonstrating that it's acceptable to laugh at garbage code while learning from how it was generated. The Guardrails That Make AI Safe "If we have all of that—the guardrails, the ability to test, automation—then AI just helps us to create the code in the right way, following our coding standards."   The same engineering practices that protect against human errors protect against AI mistakes: automated testing, deployment guardrails, coding standards, and code review. Brian sees an opportunity for AI to help teams finally accomplish what they've always wanted but never had time for—comprehensive documentation and thorough automated test suites. Looking Ahead: More Architects, More Experiments, More Failures "I'm going to see more engineers acting like architects, more engineers thinking in ways of how do I construct this system, how do I move data around, how do I scale."   Brian's 2-3 year prediction: engineers will increasingly think architecturally because AI removes the need to deeply understand framework nuances. We'll have more time for safeguards, automated testing, and documentation. But expect both sides of the spectrum to intensify—more engineers embracing AI tools, and more resistance and high-profile failures from CEOs vibe-coding production apps into security incidents. Resources for Learning Brian recommends staying current through YouTube channels focused on AI and developer tools. His top recommendations for developer-focused AI content:   IndyDevDan NetworkChuck AI Jason   His broader advice: experiment with everything, document what you learn as you go, and be willing to fail publicly. The engineers who thrive will be those actively experimenting and learning.   About Brian Childress   Brian Childress is a CTO and software architect with over 15 years of experience working across highly regulated industries including healthcare, finance, and consumer SaaS products. He brings a non-traditional background to technology leadership, having built his expertise through dedication and continuous learning rather than formal computer science education. Brian is passionate about helping engineers think architecturally and leverage AI tools effectively while maintaining simplicity in system design.   You can link with Brian Childress on LinkedIn.

We're excited to continue our AI Tools series with Yaron Lavie, a veteran product leader with over 25 years of experience in FinTech, InsurTech, and now retail tech at Nexite, where he helps fashion retailers unlock unique in-store data. In this episode, Yaron joins Matt and Moshe to share how he used Base44, an AI-powered, full‑stack vibe coding platform, to take a completely new product idea from concept to a deployed prototype without touching his R&D team.Yaron walks through why traditional approaches like Figma mockups and static visuals weren't enough for the kind of validation he needed, and how he experimented with tools like Gemini, Claude, and ChatGPT before landing on Base44 for an end‑to‑end, fully hosted solution. He explains how Base44's conversational, chat-based builder let him model user personas, flows, and entities, then iteratively refine an interactive analytics dashboard with real (anonymized) data, all inside a time‑boxed, low‑risk experiment that still respected security constraints.Join Matt, Moshe, and Yaron as they explore:Why Yaron needed to validate a new product idea without pulling scarce R&D resources off other prioritiesHow he moved from static mockups to interactive prototypes with real data, and where Gemini helped and fell shortWhat made Base44 stand out versus other vibe coding tools like Lovable: full-stack, hosted, and truly end-to-endThe importance of “context engineering” over simple prompt engineering when building with LLM-based buildersUsing Base44's discussion mode, live preview, and QA test generation to shape the product before committing to codeReal-world limits: hitting a ceiling on UX depth, inflated code, and friction with design systems and engineering standardsHow he transitioned from a Base44 prototype to a ground-up rebuild with the core dev team, using the prototype to generate user storiesPractical pros and cons: integrations, multi-currency support, database control, and when full-stack vibe coding is “good enough”Where Yaron sees vibe coding going next, and how PMs can use it responsibly for experimentation and usability testingAnd much more!Want to connect with Yaron or learn more?LinkedIn: https://il.linkedin.com/in/yaronlavieYou can also connect with us and find more episodes:Product for Product Podcast: http://linkedin.com/company/product-for-product-podcastMatt Green: https://www.linkedin.com/in/mattgreenproduct/Moshe Mikanovsky: http://www.linkedin.com/in/mikanovskyNote: Any views mentioned in the podcast are the sole views of our hosts and guests, and do not represent the products mentioned in any way.Please leave us a review and feedback ⭐️⭐️⭐️⭐️⭐️

Get PJ's free AI Video Production Stack + Workflow: https://clickhubspot.com/whs Ep. 393 233 million views in just three days — can AI-generated ads really replace million-dollar productions? Kipp, Kieran, and guest, PJ Accetturo, of Genre.ai, dive into the wild world of AI-powered commercial workflows and the viral David Beckham ad that's turning heads across the industry. Learn more about AI-driven creative teams, the tools behind photorealistic video production, and the emerging future—where hyper-niche stories thrive and challenger brands outsmart the incumbents. Mentions PJ Accetturo https://www.linkedin.com/in/pj-accetturo-b3b693129/ Genre.ai https://www.genre.ai/ Figma https://www.figma.com/ Nano Banana Pro https://gemini.google/overview/image-generation/ Freepik https://www.freepik.com/ai/image-generator Veo 3.1 https://gemini.google/overview/video-generation/ Kling https://klingai.com/global/ ElevenLabs https://elevenlabs.io/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt We're creating our next round of content and want to ensure it tackles the challenges you're facing at work or in your business. To understand your biggest challenges we've put together a survey and we'd love to hear from you! https://bit.ly/matg-research Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Join our community https://landing.connect.com/matg Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

Switching into product can feel like a one-way door, especially if you're already successful in another function. But for Michael, the path from product marketing to product management wasn't a leap of faith, it was a series of low-risk experiments, relationship-driven conversations, and intentional “spikes” he could bring to the PM role.In this episode of Supra Insider, Marc Baselga and Ben Erez sit down with Michael Chen (former PMM at LinkedIn, Slack, and Asana; now a PM at DoorDash) to break down exactly how he made the transition from marketing into product, and what made it work. They unpack the fears people don't say out loud (title cuts, failing publicly, losing social capital), why internal moves are often more about timing + business need than a single ask, and how to frame the whole process as an exploration rather than a high-stakes bet.Michael also shares how his go-to-market and storytelling background has become a real product advantage, especially in areas like pricing & packaging, subscription tiers, and helping customers “see and believe” the value before they ever click buy. If you're a PMM, marketer, or operator who wants to become a builder, or a PM who wants stronger GTM instincts - this episode is a practical blueprint.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

We've curated a special 10-minute version of the podcast for those in a hurry. Here you can listen to the full episode: https://podcasts.apple.com/no/podcast/figma-ceo-from-idea-to-ipo-design-at-scale-and-ais/id1614211565?i=1000745081487&l=nbIn this episode of In Good Company, Nicolai Tangen speaks with Dylan Field, founder and CEO of Figma, about the ideas behind one of the most influential design platforms in the world. Field shares lessons from founding Figma at 19, navigating years of iteration before launch, and scaling with a strong product culture. They discuss taste, craft, and community, how AI is changing the creative process, and what it means to lead with optimism in a rapidly evolving tech landscapeIn Good Company is hosted by Nicolai Tangen, CEO of Norges Bank Investment Management. New full episodes every Wednesday, and don't miss our Highlight episodes every Friday. The production team for this episode includes Isabelle Karlsson and PLAN-B's Niklas Figenschau Johansen, Sebastian Langvik-Hansen and Pål Huuse. Background research was conducted by Oscar Hjelde. Watch the episode on YouTube: Norges Bank Investment Management - YouTubeWant to learn more about the fund? The fund | Norges Bank Investment Management (nbim.no)Follow Nicolai Tangen on LinkedIn: Nicolai Tangen | LinkedInFollow NBIM on LinkedIn: Norges Bank Investment Management: Administrator for bedriftsside | LinkedInFollow NBIM on Instagram: Explore Norges Bank Investment Management on Instagram Hosted on Acast. See acast.com/privacy for more information.

Amir (Co-Founder at Humblytics) shares how he builds an “AI-native” company by focusing less on shiny tools and more on change management: assessing AI fluency across roles, setting the right success metrics, and creating shared context so AI can reliably ship work. The big theme is convergence—engineering, product, and design are collapsing into tighter loops thanks to tools like Cursor, MCP connectors, and Figma Make. Amir demos workflows like: AI-generated context files + auto-updated documentation, scraping customer domains to infer ICPs, turning screenshots into layered Figma designs, then converting Figma to working React code in minutes, and even running an “AI co-founder” Slack bot that files Linear tickets and can hand work to agents.Timestamps0:00 Introduction0:06 Amir's stance: “no AI experts” — it's constant learning in a fast-changing field.1:59 Cursor as the unlock: not just coding, but PM/strategy/design work via MCPs.4:17 The real problem: AI adoption is mostly change management + fluency assessment.5:18 The AI fluency rubric (helper → automator → augmentor → agentic) and why it matters.8:13 Cursor analytics: measuring AI-generated code and usage across the team.9:24 “New code is ~99% AI-generated” + how they keep quality via tight review + incremental changes.10:58 Docs workflow: GitBook connected to repo → AI edits docs and pushes live fast.14:02 ICP building: export Stripe customers → scrape domains with Firecrawl → cluster personas.17:45 Hallucination in the wild: AI misclassifies a company; human correction loop matters.34:43 Wild move: they often design in code and use an AI-generated style guide to stay consistent.38:10 Best demo: screenshot → Figma Make → layered design → Figma MCP → React code in minutes.45:29 “AI co-founder” Slack bot (Pixel): turns a bug report into a Linear ticket and can hand off to agents.48:46 Amir's wish list: we “solved dev”; now we need Cursor for marketing/sales → path to $1M ARR.Tools & technologies mentionedCursor — AI-first IDE used for coding and product/design/strategy workflows; includes team analytics.MCP (Model Context Protocol) — “connector” layer (Anthropic-origin) that lets LLMs interface with external tools/services.ChatGPT — used as a common baseline tool; discussed in the context of prompting practices and workflows.Microsoft Copilot — referenced via the law firm incentive story; used as an example of “usage metrics” gone wrong.Anthropic (AI fluency framework) — inspiration source for the helper/automator/augmentor/agentic rubric.GitBook — documentation platform connected to the repo so docs can be updated and published quickly.Firecrawl (MCP) — agentic web scraper used to analyze customer domains and infer ICP/personas.Stripe — source of customer export data (domains) to build ICP clustering.Figma — design collaboration tool; used here with Make + MCP to move from design → code.Figma Make — feature to recreate UI from an image/screenshot into editable, layered designs.Figma MCP — connector that allows Cursor/LLMs to pull Figma components/designs and generate code.React — front-end framework used in the demo for generating functional UI components.Supabase — mentioned as part of a sample stack when generating a PRD.React Router — mentioned as part of the sample stack in PRD generation.Slack — where Amir runs internal agents (including the “AI co-founder” bot).Linear — project management tool used for creating tickets from Slack/agent workflows.CI/CD — their deployment/review pipeline; emphasized as the human accountability layer.Subscribe at⁠ thisnewway.com⁠ to get the step-by-step playbooks, tools, and workflows.

FREE: AI Project ChallengeSign up for the free AI Project Challenge Keeping up with AI updates has basically become a full-time job.New models, new tools, new workflows — every week something changes, and most of us don't have the time to read every release note, test every feature, or scroll through endless feeds.So I did that for you.In this episode, I break down five major AI updates from the last quarter that designers should actually know about right now.Not as headlines, but as real insights: what changed, why it matters, and how this shows up in design and product work.We'll talk about:how Claude is evolving into a real AI co-worker with agent-style workflows and Claude Codewhy OpenAI Health is an important signal for high-stakes AI product designwhat's happening at Google with Gemini, generative UI, multimodal AI, and vibe codinghow ChatGPT Apps turn AI into a workflow layer across tools like Figma and Slackand what CES tells us about the future of AI beyond screens, from devices to ambient experiencesThis episode is a curated deep dive for designers who want to stay informed without drowning in updates — with concrete examples, UX implications, and clear takeaways.At the beginning of the episode, I also share details about my free AI Challenge, starting next week, where you'll build your first AI project brief step by step and get hands-on experience with AI.If you work in design, UX, or product and want to understand where AI is actually heading — this episode is for you.AI for Designers: 5-week Bootcamp

In this episode of In Good Company, Nicolai Tangen speaks with Dylan Field, founder and CEO of Figma, about the ideas behind one of the most influential design platforms in the world. Field shares lessons from founding Figma at 19, navigating years of iteration before launch, and scaling with a strong product culture. They discuss taste, craft, and community, how AI is changing the creative process, and what it means to lead with optimism in a rapidly evolving tech landscapeIn Good Company is hosted by Nicolai Tangen, CEO of Norges Bank Investment Management. New full episodes every Wednesday, and don't miss our Highlight episodes every Friday. The production team for this episode includes Isabelle Karlsson and PLAN-B's Niklas Figenschau Johansen, Sebastian Langvik-Hansen and Pål Huuse. Background research was conducted by Oscar Hjelde. Watch the episode on YouTube: Norges Bank Investment Management - YouTubeWant to learn more about the fund? The fund | Norges Bank Investment Management (nbim.no)Follow Nicolai Tangen on LinkedIn: Nicolai Tangen | LinkedInFollow NBIM on LinkedIn: Norges Bank Investment Management: Administrator for bedriftsside | LinkedInFollow NBIM on Instagram: Explore Norges Bank Investment Management on Instagram Hosted on Acast. See acast.com/privacy for more information.

Get Kieran's AI Video Ad Stack guide + prompts: https://clickhubspot.com/rhv Ep. 390 How long does it really take to make a realistic AI video ad? Kipp and Kieran dive into a step-by-step tutorial for creating high-quality, believable AI-powered videos, even if you're not a video expert. Learn more on how to develop a creative concept that AI tools can't replace, the essential workflow for using Veo 3.1 and Nano Banana Pro, and why reference images are the secret to seamless video scenes. This episode breaks down the process and tips to help you master AI video creation faster and smarter. Mentions Veo 3.1 https://deepmind.google/models/veo/ Nano Banana Pro https://gemini.google/overview/image-generation/ ElevenLabs https://elevenlabs.io/ Figma https://www.figma.com/ CapCut https://www.capcut.com/ Get our guide to build your own Custom GPT: https://clickhubspot.com/customgpt We're creating our next round of content and want to ensure it tackles the challenges you're facing at work or in your business. To understand your biggest challenges we've put together a survey and we'd love to hear from you! https://bit.ly/matg-research Resource [Free] Steal our favorite AI Prompts featured on the show! Grab them here: https://clickhubspot.com/aip We're on Social Media! Follow us for everyday marketing wisdom straight to your feed YouTube: ​​https://www.youtube.com/channel/UCGtXqPiNV8YC0GMUzY-EUFg  Twitter: https://twitter.com/matgpod  TikTok: https://www.tiktok.com/@matgpod  Join our community https://landing.connect.com/matg Thank you for tuning into Marketing Against The Grain! Don't forget to hit subscribe and follow us on Apple Podcasts (so you never miss an episode)! https://podcasts.apple.com/us/podcast/marketing-against-the-grain/id1616700934   If you love this show, please leave us a 5-Star Review https://link.chtbl.com/h9_sjBKH and share your favorite episodes with friends. We really appreciate your support. Host Links: Kipp Bodnar, https://twitter.com/kippbodnar   Kieran Flanagan, https://twitter.com/searchbrat  ‘Marketing Against The Grain' is a HubSpot Original Podcast // Brought to you by Hubspot Media // Produced by Darren Clarke.

THANK YOU FOR 50 EPISODES!  This is the 50th episode of Writers of Silicon Valley. Thank you for listening all this time - through my bad editing skills, a three year break, and me saying "absolutely" a lot.  It means so much that you'd tune in once, let alone 50 times. So thank you :)  As an extra 'thank you' I'm offering 35% off Advanced UX Content for Product at UX Content Collective. Use PODCAST35 at checkout :) Here's to 50 more.  Content design for AI agents Christopher Greer has been creating cool content design resources for years, but his latest is a real accomplishment: a Claude Skill that hooks into Figma and critiques UX writing.  It turns out Chris is quite optimistic about the state of the content design market.  We talk about his work at Stripe, what it actually means to design content for AI agents and internal systems - not chatbots for end users, but the infrastructure, context, and governance that sit behind them. Chris shares how content design skills translate directly into agent design, why context management is now a core capability, and how content designers can scale their influence by working closer to engineering and systems. What we talked about: ✅ Why content design skills map closely to designing AI agents and systems ✅ Context management, "context rot," and why structure matters more than prompts ✅ How content designers can scale influence through internal tools and governance ✅ Working as a content designer inside an engineering-led company like Stripe ✅ What Chris learned building and open-sourcing a Claude skill for UX writing critique ✅ Why GitHub and version control are becoming practical skills for content designers ✅ The risks AI poses to junior roles, and the strategic work that won't disappear ✅ Why qualitative judgment, taste, and human evaluation still matter Where to find Chris: LinkedIn Chris's blog Chris's Claude Skill

Dylan Field is the co-founder and CEO of Figma, a design software company that went public in July 2025. Founded in 2012, Figma transformed how people design, prototype, and build products together. After a $20 billion acquisition attempt by Adobe collapsed in 2022 because of regulators, Dylan helped Figma rebound stronger than ever. Just three years later, Figma listed its shares at nearly $20 billion and its stock price more than tripled on its first trading day.A few highlights:Expanding a sleepy marketMerging of designers and product rolesCounter-narrative to polarizing CEOsIf models get better, we have toRemembering Brat Summer Resources:More on Dylan:https://www.figma.com/https://X.com/zoinkMore on Jack:https://www.altcap.com/https://x.com/jaltmahttps://linktr.ee/uncappedpodEmail: friends@uncappedpod.com Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.  Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

It's that time of year again—the School of Motion End of Year Podcast is here, and this one is our longest yet... by a lot. Buckle up for an in-depth look at everything that shaped motion design in 2025, and a look ahead to 2026!