Podcasts about API

In this episode of the Ardan Labs Podcast, Ale Kennedy talks with Jens Neuse, CEO and co-founder of WunderGraph, about his unconventional path into technology and entrepreneurship. After a life-altering accident ended his carpentry career, Jens taught himself to code during recovery and eventually built WunderGraph to solve modern API challenges.Jens shares the evolution of WunderGraph from an early-stage startup to a successful open-source platform, including pivotal moments like securing eBay as a customer. The conversation highlights the importance of resilience, community-driven development, and balancing startup life with family, offering insight into what it takes to build meaningful technology through adversity and persistence.00:00 Introduction and Current Life07:19 Dropping Out and Carpentry Career10:52 Life-Altering Accident and Recovery18:01 Learning to Walk and Finding Direction27:46 Discovering Coding and Technology31:17 Starting the Startup Journey33:07 Discovering the Power of APIs40:50 Building a Team and Leadership Growth48:17 Founding WunderGraph59:07 Pivoting to Open Source01:05:32 eBay Breakthrough and Validation01:10:08 Balancing Family and Startup LifeConnect with Jens: LinkedIn: https://www.linkedin.com/in/jens-neuseMentioned in this Episode:Wundergraph: https://wundergraph.comWant more from Ardan Labs? You can learn Go, Kubernetes, Docker & more through our video training, live events, or through our blog!Online Courses : https://ardanlabs.com/education/ Live Events : https://www.ardanlabs.com/live-training-events/ Blog : https://www.ardanlabs.com/blog Github : https://github.com/ardanlabs

This Week In Startups is made possible by:Caldera Lab - [calderalab.com/twist](https://calderalab.com/twist)Iru - [iru.com](http://Iru.com)LinkedIn Jobs - http://linkedin.com/twist*OpenClaw is incredible at automating tasks. But what if it could also fix your startup's internal communication problems? Give agents shared memory, and you may be able to break down information silos while ensuring that teammates have the same context.@oliverhenry and @jeffweisbein demo what they've actually built with OpenClaw, including marketing automations, agentic loops, and bug fixing tools. Then we dig into what agentic infrastructure means for how startups operate, and why traditional SaaS products need to quickly adapt for the agentic era.Oliver Henry: The creator of the ‘[Larry](https://clawhub.ai/OllieWazza/larry)' OpenClaw skill, and founder of [Larrybrain](https://www.larrybrain.com/)Jeff Weisbein: The Claw-pilled founder of [WizardRFP](https://www.wizardrfp.com/) and [WhoCoversIt](https://www.whocoversit.com/), who shared his OpenClaw framework [publicly](https://weisbe.in/openclaw) and built a [getting-started guide for the tool](https://github.com/jeffweisbein/openclaw-starter-kit)**Timestamps:** 00:00 Intro(00:01:43) Here's why you never ski alone in a blizzard!(00:04:22) Why everyone at LAUNCH is going to get their own Mac Mini and AI agent(00:08:06) “OpenClaw has changed my entire solo-preneur lifestyle.” — Jeff Weinstein of Hype Lab(00:09:06) Jason's urgent API message to Steve Huffman of Reddit(00:10:20) LinkedIn Jobs - Hire right, the first time. Post your first job and get $100 off towards your job post at https://LinkedIn.com/twist(00:15:12) Oliver shows us his Larry Skill to make viral TikTok content with zero human intervention(00:20:10) Iru - Iru unifies identity, endpoint security, and compliance into one platform. Book a demo at https://iru.com.(00:21:22) Why are platforms like TikTok still so hostile toward bots?(00:24:45) The shift from asking a chatbot how to do things, to just telling an agent to do things(00:26:05) How Oliver is training Larry to get better at its job(00:30:09) Caldera Lab - Whether you're starting fresh or upgrading your routine, Caldera Lab makes skincare simple and effective. Head to https://CalderaLab.com/TWIST and use TWIST at checkout for 20% off your first order.(00:32:47) Why making your agent more PROACTIVE is more important than automating everything(00:37:14) Why pull requests… just aren't really a thing any more.(00:39:40) How Jason is using his new AI assistant, “Roy,” to keep track of everything going on at his company(00:53:00) Is the SaaS crash actually rational after all?(00:51:48) Using AI to create “pools of excellence”(00:54:03) The more you integrate software into AI, the less valuable the software becomes(00:56:56) Why “Agentify Your SaaS” may become the rallying cry(00:58:31) How has the age verification scandal impacted Discord's IPO plans?(01:03:10) When you want to build your own skill vs. downloading someone else's(01:03:53) How Larrybrain finds helpful skills and helps creators monetize(01:08:32) When we will get true experts making verifiably top skills?(01:11:40) Jason's SCARY but also AWESOME new OpenClaw CEO tools(01:18:10) What does this mean for the future of venture capital?(01:18:35) Why a lot of MBAs should probably have PhD'sThank you to our partners:(30:09) Caldera Lab - Whether you're starting fresh or upgrading your routine, Caldera Lab makes skincare simple and effective. Head to [CalderaLab.com/TWIST](http://calderalab.com/TWIST) and use TWIST at checkout for 20% off your first order.(20:10) Iru - Iru unifies identity, endpoint security, and compliance into one platform. Book a demo at [iru.com](http://iru.com/).(10:20) LinkedIn Jobs - *Hire right, the first time. Post your first job and get $100 off towards your job post at* [LinkedIn.com/twist](http://linkedin.com/HiringProOffer)

In this episode of Making Risk Flow, host Juan de Castro speaks with David McMillan, former CEO of esureGroup, to unpack how a mid-sized insurer reinvented itself under private equity ownership. Facing COVID-19, reserve pressures, a soft market, and geopolitical disruption, the company leaned into culture, clarity, and modern technology to outpace larger rivals. David shares why building a high-performing team starts with shared values, how blending insurance expertise with external digital talent accelerates innovation, and why cloud-native, API-driven architecture is essential for real-time decision-making. He also explains how to shift boards from traditional ROI forecasts to agile, outcome-based governance. It's a candid conversation about resilience, leadership under pressure, and why staying smaller, more agile, and hence, faster can be a lasting competitive advantage.Fan Mail: Got a challenge digitizing your intake? Share it with us, and we'll unpack solutions from our experience at Cytora.To receive a custom demo from Cytora, click here and use the code 'Making Risk Flow'.Our previous guests include: Bronek Masojada of PPL, Craig Knightly of Inigo, Andrew Horton of QBE Insurance, Simon McGinn of Allianz, Stephane Flaquet of Hiscox, Matthew Grant of InsTech, Paul Brand of Convex, Paolo Cuomo of Gallagher Re, and Thierry Daucourt of AXA.Check out the three most downloaded episodes: The Five Pillars of Data Analytics Strategy in Insurance | Craig Knightly, Inigo 20 Years as CEO of Hiscox: Personal Reflections and the Evolution of PPL | Bronek Masojada Implementing ESG in the Insurance and Underwriting Space | Simon Tighe, Chaucer, and Paul McCarney, Moody's

In this Retail Technology Spotlight Series episode from Omni Talk Retail, Chris Walton and Anne Mezzenga welcome back Grocery Dealz co-CEOs Matt Goynes and Micheal Waldroup to unpack the rapid national expansion of their real-time grocery price comparison app. Now live in 40 states, Grocery Dealz enables consumers to compare grocery prices across retailers in real time before they shop and even push their lists directly into Instacart for delivery. The conversation dives into how the platform works, how pricing data is sourced, why retailers are paying attention, and what price transparency means for the future of grocery. From over-the-counter medicine and alcohol price swings to retail media monetization and API partnerships, this episode explores how comparison shopping could reshape consumer behavior... just as it did in travel and gas. With grocery bills rivaling plane tickets in weekly spend, is price transparency the next major retail disruption? Key Topics Covered: •What Grocery Dealz is and why it exists •The 3-step user experience: search, substitute, compare •Categories covered beyond center store (including OTC meds & alcohol) •How real-time grocery pricing data is sourced •Retailers' response to price transparency •The Instacart integration and delivery convenience factor •How Grocery Dealz makes money (affiliate + retail media model) •Consumer adoption metrics and time spent in app •What's coming next: live coupons and national growth •Can price comparison truly change grocery shopping behavior? Connect with the Guests: Matt Goynes: https://www.linkedin.com/in/matt-goynes-65921368/ Micheal Waldroup: https://www.linkedin.com/in/micheal-waldroup-3a74b82b7/ #retailtech #grocerytech #pricetransparency #retailmedia #ecommerce #omnichannel #retailinnovation #retailpodcast #OmniTalkRetail

Norgespris gjør oss til verdens mest energi-ineffektive land. Statnett holder strømprisen høyere enn nødvendig. Det sier ikke en opposisjonspolitiker, det sier Edgeir Aksnes, gründer og CEO i Tibber, Europas største virtuelle kraftverk.Vi dro til Førde for å konfrontere ham med tallene og fikk en masterclass i hva som er galt med norsk energipolitikk, hvorfor svenskene har slått oss knockout, og hva du faktisk kan gjøre med strømregningen din.Vi snakker om:Norgespris har økt forbruket 10%, noe som kan doble strømprisenStatnett-konflikten: Hvorfor Grid Rewards fungerer i Sverige men er blokkert i Norge"Svenskene har slått teknisk knockout. Fullstendig knockout."76 års nedbetalingstid på solceller, samtlige solselskaper er mer eller mindre konkursTibber Pulse til 995 kr: Plugg inn billigste Kina-batteriet og det fungererKonkrete tips: 15-minuttersregelen, kan det være av i 15 min uten at noen merker det?"Vi trenger en Tibber for næringsbygg", hvem bygger det?Timestamps:(00:00) Tre kontroversielle Edgeir-sitater(00:30) Velkommen fra vinduskarmen i Førde(01:37) "Vi er et strømspareselskap, ikke et strømselskap"(04:29) Oppskriften på fiasko: ikke prøv å nå alle(05:52) Hvis du skulle bygd Tibber for næringsbygg?(10:59) Fleksibilitetsmarkedet forklart: fra spotpris til Grid Rewards(16:14) Statnett-konflikten: hundre år gammel modell møter ny teknologi(25:19) Mic drop: "Strømprisen er høyere enn nødvendig"(28:37) Praktiske tips: Kan det være av i 15 min uten at noen merker det?(34:26) 250.000 unike kombinasjoner: standardiseringsproblemet(36:08) Tibber Pulse: fra HAN-port til Home Energy Management System(41:55) Lastutkobler fra 400.000 kr til Pulse for 995 kr(44:57) Effektledd: "Hvordan i helsike er det mulig å forstå dette?"(47:51) HomeVolt: Popkorn-lanseringen vi ventet på(50:47) 995 kr Pulse + billigste kina-batteri = HomeVolt-funksjonalitet(51:45) Norgespris: "Verdens mest energi-ineffektive land"(55:28) Eksklusiv data: 10% høyere forbruk med Norgespris(62:56) Økosystem: Philips Hue-historien og åpen API-filosofi(66:09) Edgeir skroter Fibaro, går til Homey(71:32) Edge vs cloud: "Normally dumb" som designprinsipp(74:51) AI, paradigmeskifte og karriereråd til tenåringeneGjest:Edgeir Vårdal Aksnes, Co-founder & CEO, Tibbertibber.comFølg Praktisk PropTech:LinkedIn: linkedin.com/company/praktisk-proptechInstagram: @praktiskproptechYouTube: youtube.com/@praktiskproptechWeb: praktiskproptech.noProdusent: SylvioMentioned in this episode:

1320. Vídeopodcast en Apple Podcasts. Así, tal cual, fue como lo vendieron en un correo que recibí el lunes 16 de febrero por estar dado de alta como creador en la plataforma. Y claro, a simple vista, aquello parecía un bombazo: “el vídeo llega a Apple Podcasts”, “esta primavera”, “todo desde un solo lugar”… vamos, que faltaba el lacito y el “solo hoy” para rematarlo. Pero cuanto más lo leía, más me daba la sensación de que aquí había que hilar fino, leer entre líneas y no dejarse llevar por el titular jugoso. En el mail te plantan una imagen muy Apple: una persona frente al micro, cortinas moradas, estética cuidada, y la típica promesa de “puedes encender o apagar el vídeo”. Hasta ahí, bien. El problema es que, cuando bajas al barro, el mensaje real no es “ahora Apple Podcasts será YouTube”, sino “ahora Apple Podcasts admite distribución de vídeo mediante HLS”. Y ojo con esa frase, porque ahí está la clave: no hablan de un cambio de modelo para el creador promedio, hablan de un estándar técnico y de una integración que, según ellos, no afecta a lo que ya existe._____________Toñi Martínez patrocina esta entrega de 'Al otro lado del micrófono' con sus 2 proyectos:'Perretes | Las razas de perros' un podcast que analiza cada raza canina desde su historia, comportamiento y necesidades reales, más allá de lo estético. https://pod.link/1584806497 En 'Heroínas o Malvadas. Grandes mujeres' descubrirás a mujeres que desafiaron lo establecido y marcaron su tiempo. https://pod.link/1771654857_____________De hecho, insisten varias veces en lo mismo: seguidores, descargas y comportamiento de la audiencia “seguirán funcionando de la misma manera”. Y cuando una empresa como Apple repite tanto una idea, yo ya sospecho: esto no viene a revolucionar tu día a día como podcaster, viene a encajar una pieza nueva sin romper lo anterior… y, de paso, abrir la puerta a la monetización de vídeo con publicidad dinámica. Porque sí, te sueltan el “tú mantienes el control para monetizar”, pero todos sabemos por dónde van los tiros cuando aparece la palabra monetización en un correo corporativo. Luego llega la yincana: más información, más clics, documentación técnica, claves API, proveedor de hosting compatible, configuraciones… y aquí ya me paro un segundo. Porque esto se aleja bastante de “hago un podcast” y se acerca mucho más a “monta un flujo técnico nuevo para distribuir vídeo”. ¿Se puede? Sí. ¿Lo va a hacer todo el mundo? Ni de broma. Y encima te rematan con un dato que para mí es revelador: por ahora, las suscripciones de Apple Podcasts solo admiten audio. O sea, que si alguien pensaba que todo este pifostio era para empujar el modelo de suscripción con vídeo… pues no. Así que, por ahora, yo lo veo como un “cambio no cambio”: una mejora para quien ya viene con vídeo y un reclamo de marketing para que parezca que está pasando algo gigantesco, cuando en realidad es una integración técnica más, con mucha letra pequeña y con Apple queriendo estar en la conversación del videopodcast sin cambiar la esencia de su plataforma.Puedes leer todos los detalles de esta novedad entrando en 'Esta primavera: podcasts con vídeo': https://podcasters.apple.com/es-es/video-apple-podcastsEl episodio de 'Sujétame el micro' del que hablo en este capítulo es el siguiente 'Vídeo en Apple Podcasts' https://emilcar.fm/2026/02/18/video-en-apple-podcasts/_____________Consigue tu entrada para el directo de 'Contando Kilómetros Podcast' el 28 de marzo en las Podnights Madrid a través de Eventbritehttps://www.eventbrite.es/e/1980175107050?aff=oddtdtcreator_____________ ¡Gracias por pasarte 'Al otro lado del micrófono' un día más para seguir aprendiendo sobre podcasting! Si quieres descubrir cómo puedes unirte a la comunidad o a los diferentes canales donde está presente este podcast, te invito a visitar https://alotroladodelmicrofono.com/unete Además, puedes apoyar el proyecto mediante un pequeño impulso mensual, desde un granito de café mensual hasta un brunch digital. Descubre las diferentes opciones entrando en: https://alotroladodelmicrofono.com/cafe. También puedes apoyar el proyecto a través de tus compras en Amazon mediante mi enlace de afiliados https://alotroladodelmicrofono.com/amazon La voz que puedes escuchar en la intro del podcast es de Juan Navarro Torelló (PoniendoVoces) y el diseño visual es de Antonio Poveda. La dirección, grabación y locución corre a cargo de Jorge Marín. La sintonía que puedes escuchar en cada capítulo ha sido creada por Jason Show y se titula: 2 Above Zero.  'Al otro lado del micrófono' es una creación de EOVE Productora.

In this episode of FinTech Impact, host Jason Pereira interviews Mike Wilson, CEO and co-founder of Hamachi, an AI governance layer for financial advisors that unifies data from systems like CRMs and portfolio platforms to power workflow “bots” such as a daily dossier and household brief. Wilson explains Hamachi's origin, its compliance-first guardrails, and its strategy to embed via integrations and API rather than compete for the advisor desktop. They also discuss roadmap integrations and partners, including TaxStatus, FP Pathfinder, Financial's client portal distribution, and potential research data sources like Morningstar and Zacks.Episode Highlights:00:00 Welcome to FinTech Impact + Meet Mike Wilson (Hamachi)00:30 What Hamachi Does: An AI ‘Governing Intelligence Layer' for Advisors01:09 Origin Story: From Orion Alumni to Compliant Advisor AI02:52 From Email Add‑On to Guardrailed Chat: Early Product Evolution04:38 Real Advisor Workflows: Daily Dossier & Household Brief in Action08:58 Integration Roadmap: CRM, Portfolio Platforms, and Tax Data (TaxStatus)10:52 Where Hamachi Fits: Embedded AI, API Strategy, and Bot Marketplace14:25 What's Next: Pathfinder Workflows, Client Portals, and Research Feeds23:28 Why Governance Matters: PII Redaction, Audit Trails, SEC/FINRA Compliance27:21 Rapid‑Fire Wrap‑Up: Industry Wish, Biggest Challenge, and What Excites Mike33:01 Closing Remarks, Call to Action, and Sponsor MessageResources:Facebook – Jason Pereira's FacebookLinkedIn – Jason Pereira's LinkedInWoodgate.com – SponsorHamachi.aiLinkedIn - Mike Wilson's LinkedIn Hosted on Acast. See acast.com/privacy for more information.

In this episode, hosts Lois Houston and Nikita Abraham are joined by special guests Samvit Mishra and Rashmi Panda for an in-depth discussion on security and migration with Oracle Database@AWS. Samvit shares essential security best practices, compliance guidance, and data protection mechanisms to safeguard Oracle databases in AWS, while Rashmi walks through Oracle's powerful Zero-Downtime Migration (ZDM) tool, explaining how to achieve seamless, reliable migrations with minimal disruption.   Oracle Database@AWS Architect Professional: https://mylearn.oracle.com/ou/course/oracle-databaseaws-architect-professional/155574 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   -------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26 Nikita: Welcome to the Oracle University Podcast! I'm Nikita Abraham, Team Lead: Editorial Services with Oracle University, and with me is Lois Houston, Director of Communications and Adoption with Customer Success Services. Lois: Hello again! We're continuing our discussion on Oracle Database@AWS and in today's episode, we're going to talk about the aspects of security and migration with two special guests: Samvit Mishra and Rashmi Panda. Samvit is a Senior Manager and Rashmi is a Senior Principal Database Instructor.  00:59 Nikita: Hi Samvit and Rashmi! Samvit, let's begin with you. What are the recommended security best practices and data protection mechanisms for Oracle Database@AWS? Samvit: Instead of everyone using the root account, which has full access, we create individual users with AWS, IAM, Identity Center, or IAM service. And in addition, you must use multi-factor authentication. So basically, as an example, you need a password and a temporary code from virtual MFA app to log in to the console.  Always use SSL or TLS to communicate with AWS services. This ensures data in transit is encrypted. Without TLS, the sensitive information like credentials or database queries can be intercepted. AWS CloudTrail records every action taken in your AWS account-- who did what, when, and from where. This helps with audit, troubleshooting, and detecting suspicious activity. So you must set up API and user activity logging with AWS CloudTrail.  Use AWS encryption solutions along with all default security controls within AWS services. To store and manage keys by using transparent data encryption, which is enabled by default, Oracle Database@AWS uses OCI vaults. Currently, Oracle Database@AWS doesn't support the AWS Key Management Service. You should also use advanced managed security services such as Amazon Macie, which assists in discovering and securing sensitive data that is stored in Amazon S3.  03:08 Lois: And how does Oracle Database@AWS deliver strong security and compliance? Samvit: Oracle Database@AWS enforces transparent data encryption for all data at REST, ensuring stored information is always protected. Data in transit is secured using SSL and Native Network Encryption, providing end-to-end confidentiality. Oracle Database@AWS also uses OCI Vault for centralized and secure key management. This allows organizations to manage encryption keys with fine-grained control, rotation policies, and audit capabilities to ensure compliance with regulatory standards. At the database level, Oracle Database@AWS supports unified auditing and fine-grained auditing to track user activity and sensitive operations. At the resource level, AWS CloudTrail and OCI audit service provide comprehensive visibility into API calls and configuration changes. At the database level, security is enforced using database access control lists and Database Firewall to restrict unauthorized connections. At the VPC level, network ACLs and security groups provide layered network isolation and access control. Again, at the database level, Oracle Database@AWS enforces access controls to Database Vault, Virtual Private Database, and row-level security to prevent unauthorized access to sensitive data. And at a resource level, AWS IAM policies, groups, and roles manage user permissions with the fine-grained control. 05:27 Lois Samvit, what steps should users be taking to keep their databases secure? Samvit: Security is not a single feature but a layered approach covering user access, permissions, encryption, patching, and monitoring. The first step is controlling who can access your database and how they connect. At the user level, strong password policies ensure only authorized users can login. And at the network level, private subnets and network security group allow you to isolate database traffic and restrict access to trusted applications only. One of the most critical risks is accidental or unauthorized deletion of database resources. To mitigate this, grant delete permissions only to a minimal set of administrators. This reduces the risk of downtime caused by human error or malicious activity. Encryption ensures that even if the data is exposed, it cannot be read. By default, all databases in OCI are encrypted using transparent data encryption. For migrated databases, you must verify encryption is enabled and active. Best practice is to rotate the transparent data encryption master key every 90 days or less to maintain compliance and limit exposure in case of key compromise. Unpatched databases are one of the most common entry points for attackers. Always apply Oracle critical patch updates on schedule. This mitigates known vulnerabilities and ensures your environment remains protected against emerging threats. 07:33 Nikita: Beyond what users can do, are there any built-in features or tools from Oracle that really help with database security? Samvit: Beyond the basics, Oracle provides powerful database security tools. Features like data masking allow you to protect sensitive information in non-production environments. Auditing helps you monitor database activity and detect anomalies or unauthorized access. Oracle Data Safe is a managed service that takes database security to the next level. It can access your database configuration for weaknesses. It can also detect risky user accounts and privileges, identify and classify sensitive data. It can also implement controls such as masking to protect that data. And it can also continuously audit user activity to ensure compliance and accountability. Now, transparent data encryption enables you to encrypt sensitive data that you store in tables and tablespaces. It also enables you to encrypt database backups. After the data is encrypted, this data is transparently decrypted for authorized users or applications when they access that data. You can configure OCI Vault as a part of the transparent data encryption implementation. This enables you to centrally manage keystore in your enterprise. So OCI Vault gives centralized control over encryption keys, including key rotation and customer managed keys. 09:23 Lois: So obviously, lots of companies have to follow strict regulations. How does Oracle Database@AWS help customers with compliance?  Samvit: Oracle Database@AWS has achieved a broad and rigorous set of compliance certifications. The service supports SOC 1, SOC 2, and SOC 3, as well as HIPAA for health care data protection. If we talk about SOC 1, that basically covers internal controls for financial statements and reporting. SOC 2 covers internal controls for security, confidentiality, processing integrity, privacy, and availability. SOC 3 covers SOC 2 results tailored for a general audience. And HIPAA is a federal law that protects patients' health information and ensures its confidentiality, integrity, and availability. It also holds certifications and attestations such as CSA STAR, C5. Now C5 is a German government standard that verifies cloud providers meet strict security and compliance requirements. CSA STAR attestation is an independent third-party audit of cloud security controls. CSA STAR certification also validates a cloud provider's security posture against CSA's cloud controls matrix. And HDS is a French certification that ensures cloud providers meet stringent requirements for hosting and protecting health care data. Oracle Database@AWS also holds ISO and IEC standards. You can also see PCI DSS, which is basically for payment card security and HITRUST, which is for high assurance health care framework. So, these certifications ensure that Oracle Database@AWS not only adheres to best practices in security and privacy, but also provides customers with assurance that their workloads align with globally recognized compliance regimes. 11:47 Nikita: Thank you, Samvit. Now Rashmi, can you walk us through Oracle's migration solution that helps teams move to OCI Database Services? Rashmi: Oracle Zero-Downtime Migration is a robust and flexible end-to-end database migration solution that can completely automate and streamline the migration of Oracle databases. With bare minimum inputs from you, it can orchestrate and execute the entire migration task, virtually needing no manual effort from you. And the best part is you can use this tool for free to migrate your source Oracle databases to OCI Oracle Database Services faster and reliably, eliminating the chances of human errors. You can migrate individual databases or migrate an entire fleet of databases in parallel. 12:34 Nikita: Ok. For someone planning a migration with ZDM, are there any key points they should keep in mind?  Rashmi: When migrating using ZDM, your source databases may require minimal downtime up to 15 minutes or no downtime at all, depending upon the scenario. It is built with the principles of Oracle maximum availability architecture and leverages technologies like Oracle GoldenGate and Oracle Data Guard to achieve high availability and online migration workflow using Oracle migration methods like RMAN, Data Pump, and Database Links. Depending on the migration requirement, ZDM provides different migration method options. It can be logical or physical migration in an online or offline mode. Under the hood, it utilizes the different database migration technologies to perform the migration. 13:23 Lois: Can you give us an example of this? Rashmi: When you are migrating a mission critical production database, you can use the logical online migration method. And when you are migrating a development database, you can simply choose the physical offline migration method. As part of the migration job, you can perform database upgrades or convert your database to multitenant architecture. ZDM offers greater flexibility and automation in performing the database migration. You can customize workflow by adding pre or postrun scripts as part of the workflow. Run prechecks to check for possible failures that may arise during migration and fix them. Audit migration jobs activity and user actions. Control the execution like schedule a job pause, resume, if needed, suspend and resume the job, schedule the job or terminate a running job. You can even rerun a job from failure point and other such capabilities. 14:13 Lois: And what kind of migration scenarios does ZDM support? Rashmi: The minimum version of your source Oracle Database must be 11.2.0.4 and above. For lower versions, you will have to first upgrade to at least 11.2.0.4. You can migrate Oracle databases that may be of the Standard or Enterprise edition. ZDM supports migration of Oracle databases, which may be a single-instance, or RAC One Node, or RAC databases. It can migrate on Unix platforms like Linux, Oracle Solaris, and AIX. For Oracle databases on AIX and Oracle Solaris platform, ZDM uses logical migration method. But if the source platform is Linux, it can use both physical and logical migration method. You can use ZDM to migrate databases that may be on premises, or in third-party cloud, or even within Oracle Cloud Infrastructure. ZDM leverages Oracle technologies like RMAN datacom, Database Links, Data Guard, Oracle GoldenGate when choosing a specific migration workflow. 15:15 Are you ready to revolutionize the way you work? Discover a wide range of Oracle AI Database courses that help you master the latest AI-powered tools and boost your career prospects. Start learning today at mylearn.oracle.com. 15:35 Nikita: Welcome back! Rashmi, before someone starts using ZDM, is there any prep work they should do or things they need to set up first? Rashmi: Working with ZDM needs few simple configuration. Zero-downtime migration provides a command line interface to run your migration job. First, you have to download the ZDM binary, preferably download from my Oracle Support, where you can get the binary with the latest updates. Set up and configure the binary by following the instructions available at the same invoice node. The host in which ZDM is installed and configured is called the zero-downtime migration service host. The host has to be Oracle Linux version 7 or 8, or it can be RCL 8. Next is the orchestration step where connection to the source and target is configured and tested like SSH configuration with source and target, opening the ports in respective destinations, creation of dump destination, granting required database privileges. Prepare the response file with parameter values that define the workflow that ZDM should use during Oracle Database migration. You can also customize the migration workflow using the response file. You can plug in run scripts to be executed before or after a specific phase of the migration job. These customizations are called custom plugins with user actions. Your sources may be hosted on-premises or OCI-managed database services, or even third-party cloud. They may be Oracle Database Standard or Enterprise edition and on accelerator infrastructure or a standard compute. The target can be of the same type as the source. But additionally, ZDM supports migration to multicloud deployments on Oracle Database@Azure, Oracle Database@Google Cloud, and Oracle Database@AWS. You begin with a migration strategy where you list the different databases that can be migrated, classification of the databases, grouping them, performing three migration checks like dependencies, downtime requirement versions, and preparing the order migration, the target migration environment, et cetera. 17:27 Lois: What migration methods and technologies does ZDM rely on to complete the move? Rashmi: There are primarily two types of migration: physical or logical. Physical migration pertains to copy of the database OS blocks to the target database, whereas in logical migration, it involves copying of the logical elements of the database like metadata and data. Each of these migration methods can be executed when the database is online or offline. In online mode, migration is performed simultaneously while the changes are in progress in the source database. While in offline mode, all changes to the source database is frozen. For physical offline migration, it uses backup and restore technique, while with the physical online, it creates a physical standby using backup and restore, and then performing a switchover once the standby is in sync with the source database. For logical offline migration, it exports and imports database metadata and data into the target database, while in logical online migration, it is a combination of export and import operation, followed by apply of incremental updates from the source to the target database. The physical or logical offline migration method is used when the source database of the application can allow some downtime for the migration. The physical or logical online migration approach is ideal for scenarios where any downtime for the source database can badly affect critical applications. The only downtime that can be tolerated by the application is only during the application connection switchover to the migrated database. One other advantage is ZDM can migrate one or a fleet of Oracle databases by executing multiple jobs in parallel, where each job workflow can be customized to a specific database need. It can perform physical or logical migration of your Oracle databases.  And whether it should be performed online or offline depends on the downtime that can be approved by business. 19:13 Nikita: Samvit and Rashmi, thanks for joining us today. Lois: Yeah, it's been great to have you both. If you want to dive deeper into the topics we covered today, go to mylearn.oracle.com and search for the Oracle Database@AWS Architect Professional course. Until next time, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 19:35 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Access the podcast to hear AIOps experts discuss how Hitachi VSP One delivers high-performance file services that accelerate IT modernization by improving fleet management, operational visibility and API-driven automation. Learn how Hitachi Vantara Federal can help your agency streamline firmware updates, licensing and system setup to enhance application performance and automate critical data workloads.

Team USA shocks Canada in the men's hockey gold medal game and we break down everything — the biggest moments, the coaching decisions, the betting market movement before puck drop, and how the result impacted bettors on both sides. We react to the game itself, the pregame narratives that didn't hold up, and what this outcome says about the USA vs Canada rivalry going forward. Plus, we dive into the stat making the rounds that 57% of bettors are female — what it actually means, how it's being interpreted, and whether gambling media is telling the story correctly. And of course, Gambling Twitter drama is back. We unpack the latest beef, subtweets, and industry shots that have reignited timelines this week. Circle Back is hosted by Jacob Gramegna and features professional sports bettor and CEO of The Hammer, Rob Pizzola, basketball originator Kirk Evans, and sophisticated square Geoff Fienberg. The crew reacts to the biggest stories in sports betting, gambling media, and the broader betting industry every week on Circles Off, part of The Hammer Betting Network.

Planet Nix and SCaLE are just days away, and we're getting a head start with two guests, the tech, and the trends shaping open source. Our trip starts here!Sponsored By:Jupiter Party Annual Membership: Put your support on automatic with our annual plan, and get one month of membership for free! Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love. Support LINUX UnpluggedLinks:

Today, I'm joined by Sam O'Keefe, co-founder & CEO of Flex.   Enabling use of HSA and FSA funds at checkout, Flex unlocks tax-advantaged spending on preventative health products — from fitness and recovery to supplements and wearables.   In this episode, we discuss making "pay with HSA/FSA" as common as PayPal.    We also cover:   Confusion around HSAs & FSAs Value props for merchants and consumers Integrating with Equinox's membership flow    Subscribe to the podcast → insider.fitt.co/podcast  Subscribe to our newsletter → insider.fitt.co/subscribe  Follow us on LinkedIn → linkedin.com/company/fittinsider    Flex's Website: www.withflex.com  Shop with Flex: www.withflex.com/shop  Instagram: www.instagram.com/paywithflex/  LinkedIn: www.linkedin.com/company/withflex/  X (Twitter): https://x.com/paywithflex  Sam's Email: sam@withflex.com    -   The Fitt Insider Podcast is brought to you by EGYM. Visit EGYM.com to learn more about its smart fitness ecosystem for fitness and health facilities.   Fitt Talent: https://talent.fitt.co/  Consulting: https://consulting.fitt.co/  Investments: https://capital.fitt.co/    Chapters: (00:00) Introduction (01:39) Sam's background (02:26) Why HSA/FSA funds are hard to use (04:00) Making HSA/FSA work (05:35) IRS regulations (07:05) The education problem (09:00) Merchant value proposition (10:30) Increased accessibility (11:10) API-first infrastructure (12:05) Custom integrations (14:40) Patterns across fitness, sleep, supplements, wearables (16:15) Women directing family healthcare spending (18:00) Subscription retention (19:15) Psychology of separate health spending pool (21:25) Equinox partnership (22:40) Seamless checkout integration vs reimbursement friction (24:15) Fitness language shift (25:05) Making HSA/FSA ubiquitous in every checkout (27:15) $150 billion underutilized in HSA/FSA accounts (28:40) Consumer marketplace (29:30) Growing B2C alongside B2B focus (30:30) 2026 priorities (32:33) Conclusion  

Welcome to Season 6, Episode 8! What do you get when you mix Jamaican Reggae with Hawaiian music. This isn't the set-up for a joke, it's an episode on Jawaiian music, sometimes called Island Reggae. Today we talk about the origin of Jawaiian music, who some of the key musicians were, why it resonated with so many Hawaiians, and some of the small controversies around it.  We go more in-depth on three key musicians who have made a HUGE impact in Jawaiian music: Brother Noland, Fiji, and J-Boog. To get an idea of the spirit of Jawaiian, then look up their work!  In our recurring segment, we do some celebrations of the API in the 2026 Winter Olympics. Although we recorded before they were over, we wanted to celebrate what's happened so far for Asian Pacific athletes. We also take time to rant a bit about the really two-faced coverage of Eileen Gu, Chloe Kim, and Alysa Liu.  If you like what we do, please share, follow, and like us in your podcast directory of choice or on Instagram @AAHistory101. For previous episodes and resources, please visit our site at https://asianamericanhistory101.libsyn.com or our links at http://castpie.com/AAHistory101. If you have any questions, comments or suggestions, email us at info@aahistory101.com. Segments 00:25 Intro 01:56 The History of Jawaiian Music 15:50 Celebrations and a Little Rant About API in the Olympics Top Picture is Brother Noland Bottom Picture is J Boog

In this episode of The Cybersecurity Defenders Podcast, we discuss some intel being shared in the LimaCharlie community.A financially motivated threat actor known as GS7 is conducting a large-scale phishing campaign called Operation DoppelBrand, targeting Fortune 500 companies by impersonating their corporate login portals.Kaspersky researchers have analyzed a newly identified Android malware strain named Keenadu that provides attackers with remote control over infected devices.Application Programming Interfaces continue to be a primary attack surface, and new research from Wallarm shows the problem is accelerating as AI adoption expands.Hacker News outlines cybersecurity technology priorities for 2026, framing the environment as one of continuous instability rather than periodic disruption.Support our show by sharing your favorite episodes with a friend, subscribe, give us a rating or leave a comment on your podcast platform.This podcast is brought to you by LimaCharlie, maker of the SecOps Cloud Platform, infrastructure for SecOps where everything is built API first. Scale with confidence as your business grows. Start today for free at limacharlie.io.

This episode presents Project Amber lead Brian Goetz's recent email "Data Oriented Programming, Beyond Records", wherein he describes plans to improve Java's data handling capabilities by introducing carrier classes, a generalization of records. Like them, carrier classes describe their state through a component list that defines the type's external API: accessors, a constructor, and matching deconstructor - this allows carrier classes to participate in pattern matching and reconstruction. Unlike records, the implementation of this API remains the developer's task although component fields offer a shortcut for the common case where the API does map to a field. Carrier classes don't have to be final (and can hence participate in inheritance) and neither do their fields (so they can be mutable data carriers). The email also mentions carrier interfaces, allowing records to be abstract as well as a relaxation of deconstruction patterns that make them more amenable to evolution of the matched type. This episode also briefly touches on Gavin Bierman's mail to the Project Amber mailing list that announces pattern assignments and constant patterns.

Today we are talking about Mautic, marketing automation, and its history with Drupal with guest Ruth Cheesley. We'll also cover Mautic ECA as our module of the week. For show notes visit: https://www.talkingDrupal.com/541 Topics What Is Mautic? Self-Hosting and Data Ownership Who Uses Mautic + Personalization Mautic's History with Drupal How Drupal Integrate Mautic Orchestration in Mautic Privacy & Compliance: GDPR Tools, Consent, and Do-Not-Contact Controls Hosting Options Advanced Segmentation Points-Based Lead Scoring Validating Segments Using Points to Boost Common Mautic Adoption Pitfalls Getting Support The Future with AI AI and Open Source Maintenance Mautic Sustainability & Fundraising How to Contribute Resources Mautic Mautic Integration Advanced Mautic Integration Talking Drupal #343 - Marketing Automation with Mautic Managed hosting, 40% goes to the community Mautic/Drupal case study and presentation on that from our conference https://www.youtube.com/watch?v=r0SkfeHTLK8 https://mautic.org/case-study/inagro/ GDPR cleanup jobs to remove old data Anonymization tasks to comply with specific laws (eg CCPA) Anonymize IP setting Proposal to overhaul all things privacy and streamline experience for marketers - currently seeking funding, planning to ship in Mautic 9 Mautic contribution docs Testing PRs: inlcuding local setup guide Low/no-code tasks board Thanks Dev Ecosystems Guests Ruth Cheesley - ruthcheesley.co.uk RCheesley Hosts Nic Laflin - nLighteneddevelopment.com nicxvan John Picozzi - epam.com johnpicozzi Catherine Tsiboukas - mindcraftgroup.com bletch MOTW Correspondent Martin Anderson-Clutz - mandclu.com mandclu Brief description: Have you ever wanted to integrate Mautic marketing automation into your Drupal website, using ECA? There's a module for that. Module name/project name: Mautic ECA Brief history How old: created in Jun 2025 by Abhisek Mazumdar (abhisekmazumdar) of Dropsolid Versions available: 1.0.6 which works with Drupal 10 and 11 Maintainership Actively maintained Documentation - detailed README Number of open issues: 1 open issues, which is not a bug Usage stats: 3 sites Module features and usage With the module installed, your ECA models can respond to Mautic webhooks, and can also make use of new actions to give you CRUD capabilities (Create, Read, Update, or Delete) for contacts and segments within ECA Mautic ECA declares the Mautic API module as a dependency, and you need to use it to set up an API connection, and to define any webhooks you want to use in your models It's worth noting that the maintainers of Mautic ECA also seem to be involved with a number of other modules in the Mautic API ecosystem, including Mautic Personalization, as well as Mautic Content Provider, which can expose Drupal content for use in Mautic, for example to include in emails

If large language models are so powerful, why can they still get basic things wrong? In this episode, we take a practical look at how AI systems actually work, why hallucinations happen by design, and what's being done to reduce them. We break down core concepts like probabilistic prediction, chain-of-thought reasoning, RAG systems, context windows, API orchestration, and cost structures. Not from a tech hype lens, but from a business one. Most importantly, we explore what this means for seafood companies integrating AI into real workflows: how to think about reliability, data access, governance, and long-term cost before plugging models into sensitive systems. This isn't about whether AI will matter but about how to use it responsibly at scale. For more aquaculture insights head to our Fish n' Bits blog.

The Alabama Policy's Stephanie Smith joins Greg for their weekly visit to discuss upcoming legislation that API is tracking this week in Montgomery.

Trudno nie zgodzić się z tym, że tworzenie dokumentacji do oprogramowania wymaga wiedzy technicznej, szczególnie jeśli pracujesz w modelu docs as code. Natomiast trudno jest określić jaki poziom tej wiedzy powinien być. Czy jeśli pracuję z deweloperami to muszę znać ich narzędzia prawie tak samo dobrze jak oni? Czy wręcz odwrotnie - mogę się w ogóle nimi nie przejmować?Jak zwykle prawda leży po środku. Powinno się znać te narzędzia na tyle na ile jest to potrzebne, żeby być lepszym technoskrybą - nie bardziej i nie mniej. W naszej rozmowie poszukujemy tego środka i dzielimy się naszymi praktycznymi wskazówkami, które sprawią, że zdobywanie wiedzy technicznej stanie się tylko środkiem do osiągnięcia Twojego celu a nie celem samym w sobie.Dźwięki wykorzystane w audycji pochodzą z kolekcji "107 Free Retro Game Sounds" dostępnej na stronie https://dominik-braun.net, udostępnianej na podstawie licencji Creative Commons license CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).Linki:Arbortext: https://www.ptc.com/en/products/arbortext"Docs as Code", Write the Docs: https://www.writethedocs.org/guide/docs-as-code/"Jak pracować z narzędziami deweloperskimi - wskazówki dla tech writerów", techwriter.pl: https://techwriter.pl/jak-pracowac-z-narzedziami-nie-bedac-programistaIntelliJ IDEA: https://www.jetbrains.com/idea/"Git (oprogramowanie)", Wikipedia: https://pl.wikipedia.org/wiki/Git_(oprogramowanie)Markdown: https://daringfireball.net/projects/markdown/syntax "Kurde! Reuse popsuł mi searcha", techwriter.pl: https://techwriter.pl/reuse-zly-dla-searcha/"Terminal", Filmweb: https://www.filmweb.pl/film/Terminal-2004-106408Visual Studio (VS) Code: https://code.visualstudio.com"CI/CD", Wikipedia: https://en.wikipedia.org/wiki/CI/CD"Wiersz poleceń", Wikipedia: https://pl.wikipedia.org/wiki/Wiersz_polece%C5%84Oxygen XML: https://www.oxygenxml.com/ "Darwin Information Typing Architecture" (DITA), Wikipedia: https://en.wikipedia.org/wiki/Darwin_Information_Typing_ArchitectureAsciiDoc: https://asciidoc.org/reStructuredText: http://docutils.sourceforge.net/rst.htmlStatic site generator: https://www.gatsbyjs.com/docs/glossary/static-site-generator/DITA Open Toolkit (DITA OT): https://www.dita-ot.org/"Component content management system (CCMS)", Wikipedia: https://en.wikipedia.org/wiki/Component_content_management_systemJamstack: https://jamstack.org/Yarn: https://yarnpkg.com/"What is Amazon S3", Amazon docs: https://docs.aws.amazon.com/AmazonS3/latest/userguide/Welcome.htmlWtyczka Prettier - Code formatter: https://marketplace.visualstudio.com/items?itemName=esbenp.prettier-vscodeWtyczka GitLens — Git supercharged: https://marketplace.visualstudio.com/items?itemName=eamodio.gitlensWtyczka Code Spell Checker: https://marketplace.visualstudio.com/items?itemName=streetsidesoftware.code-spell-checker"API", Wikipedia: https://en.wikipedia.org/wiki/APIWtyczka REST Client: https://marketplace.visualstudio.com/items?itemName=humao.rest-client Postman API client: https://www.postman.com/product/api-client/cURL: https://curl.se/„Ciemność, widzę ciemność, ciemność widzę - czyli jak poskromić linię komend", techwriter.pl: https://techwriter.pl/linia-komend

Sun, 22 Feb 2026 16:00:00 GMT http://relay.fm/mpu/837 http://relay.fm/mpu/837 Menu Bar Mayhem 837 David Sparks and Stephen Robles David and Stephen go deep on the Mac menu bar, comparing their contrasting philosophies and walk through their favorites. They also explore how macOS 26's multiple Control Centers are changing the game. David and Stephen go deep on the Mac menu bar, comparing their contrasting philosophies and walk through their favorites. They also explore how macOS 26's multiple Control Centers are changing the game. clean 5733 David and Stephen go deep on the Mac menu bar, comparing their contrasting philosophies and walk through their favorites. They also explore how macOS 26's multiple Control Centers are changing the game. This episode of Mac Power Users is sponsored by: Insta360: Introducing the Insta360 Wave and the Link 2 Pro. HTTPBot: A powerful API client and debugger for Apple platforms. Get a 7-day trial and 25% off your subscription. Ecamm: Powerful live streaming platform for Mac. 1Password: Never forget a password again. Links and Show Notes: Credits The Mac Power Users Stephen Robles David Sparks The Editor Jim Metzendorf The Fixer Kerry Provanzano More Power Users: Ad-free episodes with regular bonus segments Submit Feedback David's Menu Bar, Condensed David's Full Menu Bar Stephen's Menu Bar Ice Menu Bar Manager ‎Hidden Bar App - App Store ‎Barbee - App Store BuhoBarX MacMenuBar.com iStat Menus Loom CleanShot X for Mac Screen Studio Dropzone 4 DEVONtechnologies Supercharge — Sindre Sorhus ‎DiskView App - App Store Audio Hijack Setapp Hazel for Mac PopClip for Mac BetterTouchTool CleanMyMac Moom · Many Tricks Karabiner-Elements Carbon Copy Cloner WhisperType Cotypist Wispr Flow Tailscale ‎Pastebot App - App Store ‎Shortery App - App Store ‎Itsyhome App - App Store ‎HomeControl Menu for HomeKit App - App Store Shawn Blanc Backblaze MacWhisper Grammarly Timing Flexibits | Fantastical ‎Screens 5: VNC Remote Desktop App - App Store Drafts | Where Text Starts Day One Journal App Keyboard Maestro TextExpander Alfred Menuwhere Bitfocus - Companion Parcel - Delivery Tracking ‎Creator's Best Friend App - App Store

OpenClaw is a self-hosted AI agent daemon that executes autonomous tasks through messaging apps like WhatsApp and Telegram using persistent memory. It integrates with Claude Code to enable software development and administrative automation directly from mobile devices. Links Notes and resources at ocdevel.com/mlg/mla-29 Try a walking desk - stay healthy & sharp while you learn & code Generate a podcast - use my voice to listen to any AI generated content you want OpenClaw is a self-hosted AI agent daemon (Node.js, port 18789) that executes autonomous tasks via messaging apps like WhatsApp or Telegram. Developed by Peter Steinberger in November 2025, the project reached 196,000 GitHub stars in three months. Architecture and Persistent Memory Operational Loop: Gateway receives message, loads SOUL.md (personality), USER.md (user context), and MEMORY.md (persistent history), calls LLM for tool execution, streams response, and logs data. Memory System: Compounds context over months. Users should prompt the agent to remember specific preferences to update MEMORY.md. Heartbeats: Proactive cron-style triggers for automated actions, such as 6:30 AM briefings or inbox triage. Skills: 5,705+ community plugins via ClawHub. The agent can author its own skills by reading API documentation and writing TypeScript scripts. Claude Code Integration Mobile to Deploy Workflow: The claude-code-skill bridge provides OpenClaw access to Bash, Read, Edit, and Git tools via Telegram. Agent Teams: claude-team manages multiple workers in isolated git worktrees to perform parallel refactors or issue resolution. Interoperability: Use mcporter to share MCP servers between Claude Code and OpenClaw. Industry Comparisons vs n8n: Use n8n for deterministic, zero-variance pipelines. Use OpenClaw for reasoning and ambiguous natural language tasks. vs Claude Cowork: Cowork is a sandboxed, desktop-only proprietary app. OpenClaw is an open-source, mobile-first, 24/7 daemon with full system access. Professional Applications Therapy: Voice to SOAP note transcription. PHI requires local Ollama models due to a lack of encryption at rest in OpenClaw. Marketing: claw-ads for multi-platform ad management, Mixpost for scheduling, and SearXNG for search. Finance: Receipt OCR and Google Drive filing. Requires human review to mitigate non-deterministic LLM errors. Real Estate: Proactive transaction deadline monitoring and memory-driven buyer matching. Security and Operations Hardening: Bind to localhost, set auth tokens, and use Tailscale for remote access. Default settings are unsafe, exposing over 135,000 instances. Injection Defense: Add instructions to SOUL.md to treat external emails and web pages as hostile. Costs: Software is MIT-licensed. API costs are paid per-token or bundled via a Claude subscription key. Onboarding: Run the BOOTSTRAP.md flow immediately after installation to define agent personality before requesting tasks.

Sun, 22 Feb 2026 16:00:00 GMT http://relay.fm/mpu/837 http://relay.fm/mpu/837 David Sparks and Stephen Robles David and Stephen go deep on the Mac menu bar, comparing their contrasting philosophies and walk through their favorites. They also explore how macOS 26's multiple Control Centers are changing the game. David and Stephen go deep on the Mac menu bar, comparing their contrasting philosophies and walk through their favorites. They also explore how macOS 26's multiple Control Centers are changing the game. clean 5733 David and Stephen go deep on the Mac menu bar, comparing their contrasting philosophies and walk through their favorites. They also explore how macOS 26's multiple Control Centers are changing the game. This episode of Mac Power Users is sponsored by: Insta360: Introducing the Insta360 Wave and the Link 2 Pro. HTTPBot: A powerful API client and debugger for Apple platforms. Get a 7-day trial and 25% off your subscription. Ecamm: Powerful live streaming platform for Mac. 1Password: Never forget a password again. Links and Show Notes: Credits The Mac Power Users Stephen Robles David Sparks The Editor Jim Metzendorf The Fixer Kerry Provanzano More Power Users: Ad-free episodes with regular bonus segments Submit Feedback David's Menu Bar, Condensed David's Full Menu Bar Stephen's Menu Bar Ice Menu Bar Manager ‎Hidden Bar App - App Store ‎Barbee - App Store BuhoBarX MacMenuBar.com iStat Menus Loom CleanShot X for Mac Screen Studio Dropzone 4 DEVONtechnologies Supercharge — Sindre Sorhus ‎DiskView App - App Store Audio Hijack Setapp Hazel for Mac PopClip for Mac BetterTouchTool CleanMyMac Moom · Many Tricks Karabiner-Elements Carbon Copy Cloner WhisperType Cotypist Wispr Flow Tailscale ‎Pastebot App - App Store ‎Shortery App - App Store ‎Itsyhome App - App Store ‎HomeControl Menu for HomeKit App - App Store Shawn Blanc Backblaze MacWhisper Grammarly Timing Flexibits | Fantastical ‎Screens 5: VNC Remote Desktop App - App Store Drafts | Where Text Starts Day One Journal App Keyboard Maestro TextExpander Alfred Menuwhere Bitfocus - Companion Parcel - Delivery Tracking ‎Creator's Best Friend App - App Store

In this episode of the CPQ Podcast, we sit down with Dustin Anglen, Strategic Partnerships Manager at PandaDoc, to discuss how PandaDoc CPQ supports faster quoting for SMB teams (roughly 5–500 employees). PandaDoc is widely known for proposals and eSignature, and Dustin explains why CPQ is a natural extension—especially for organizations that want a practical, easy-to-administer approach without heavy configuration overhead. We cover where PandaDoc CPQ fits best (including SaaS, software & technology, professional services, and education) and how customers typically use it alongside their CRM. Dustin outlines PandaDoc's API-forward SaaSapproach and its key integrations with HubSpot, Pipedrive, and Salesforce. We also discuss what's available today—and what's still evolving—such as ERP connectivity (currently not a standard integration, with MVP work underway) and common customer expectations around implementation, which is often 8–12 weeks. On the capability side, Dustin shares the top requests he sees from the market: product configuration, contract-based pricing, and CRM integration. We talk about product structure support (including bundles), pricing flexibility across segments and regions, usage-based pricing, and how PandaDoc positions its CPQ as a rules engine that is largely no-code (with options for more advanced logic when needed). We also dig into PandaDoc's AI direction—template generation, OCR and document intelligence, metadata-driven automation, and an admin-focused AI feature for helping set up product and pricing rules (currently in testing, with broader availability expected later this year). You'll also hear a few personal moments from Dustin—from his early career in the Salesforce ecosystem (including starting at Apttus in 2014), to an unexpected chapter running a beekeeping business in Santa Barbara, to his passion for freediving near San Diego. A PandaDoc CPQ free trial is available on PandaDoc's website.

An airhacks.fm conversation with Kabir Khan (@kabirkhan) about: Discussion about the A2A (Agent-to-Agent) protocol initiated by Google and donated to the Linux Foundation, the A2A Java SDK reference implementation using quarkus, the Java SDK development accepted by Google, comparison of python and Java expressiveness and coding practices, the concept of an agent as a stateful process versus a tool as a stateless function call, the agent card as a JSON document advertising capabilities including supported protocols and descriptions and input/output modes and examples, the three wire protocols supported: JSON RPC and HTTP+JSON (REST) and grpc, the proto file becoming the single source of truth for the upcoming 1.0 spec, the facade/adapter pattern for the unified client across protocols, the agent executor interface with request context and event queue parameters, the distinction between simple message interactions and long-running multi-turn tasks, tasks as Java Records containing conversation history with messages and artifacts, message parts including text parts and data parts and file parts, task lifecycle with task IDs and context IDs for stateful conversations, the event queue as internal plumbing for propagating task updates, the separation between spec package (wire protocol entities) and server package (implementation details), the task store as a CRUD interface with in-memory default and database-backed implementations in extras, replicated queue manager using microprofile reactive messaging with Kafka for kubernetes environments, building A2A agents without any LLM involvement for simple use cases like backup systems, the role of LLMs in creating prompts from task messages and context, the agentic loop and the challenge of deciding when an agent's work is complete, the relationship between MCP (Model Context Protocol) for tool access and A2A for agent-to-agent communication, the possibility of wrapping agent calls as MCP tools, memory management considerations with short-term and long-term memory and prompt size affecting LLM quality, the distinction between the bare reference implementation and Quarkus-specific enhancements like annotations and dependency injection, upcoming 1.0 release with standardized Java records for all API classes and improved JavaDoc, protocol extensions including the agent payment protocol and GUI snippet extensions using template engines, authentication support with OAuth2 tokens and API keys and bearer tokens, the authenticated agent card containing more information than the public agent card, authorization hooks being discussed for task-level access control, the API and SPI segregation suggestion for better clarity between spec and implementation Kabir Khan on twitter: @kabirkhan

Jim Love discusses how rapid adoption of agentic AI is repeating the industry pattern of shipping technology without security, citing issues like vulnerabilities in Anthropic's MCP and insecure open-source agent tools. He interviews Ido Shlomo, co-founder and CTO of Token Security, who argues AI agents are fundamentally hard to secure because they are non-deterministic, have infinite input/output space, and often require broad permissions to be useful.  Cybersecurity Today  would like to thank Meter for their support in bringing you this podcast. Meter delivers a complete networking stack, wired, wireless and cellular in one integrated solution that's built for performance and scale.  You can find them at Meter.com/cst Shlomo proposes focusing security on access, identity, attribution, least privilege, and auditability rather than trying to filter prompts and outputs, and describes Token's "intent-based permission management" approach that maps agents and sub-agents as non-human identities tied to their purpose and allowed actions. The conversation covers real-world risks such as developer tools like Claude Code running with extensive access, widespread over-provisioning of admin permissions and API keys, exposure of unencrypted local token files, and misconfigurations that leak data publicly. Shlomo recommends organizations build governance processes for agents—discovery/inventory, boundary setting, continuous monitoring, and secure decommissioning—and says AI is needed to help police AI. He also highlights emerging trends like agent teams and multi-day autonomous tasks, and notes Token Security is a top-10 finalist in the RSA Innovation Sandbox 2026, planning to present an intent-and-access-focused security model for AI agents. 00:00 Sponsor: Meter's integrated networking stack 00:19 Why agentic AI security is breaking (MCP & open-source chaos) 02:53 Meet Token Security: practical guardrails for AI agents 04:57 Why you can't just ban agents at work (shadow AI reality) 06:24 Tel Aviv's cybersecurity pipeline: gaming, military, and startups 08:57 Why AI/agents are fundamentally hard to secure (new OS + 'human spirit') 13:44 Trust, autonomy, and permissions: managing the blast radius 18:17 Real-world exposure: Claude Code and the developer identity attack surface 20:16 A workable approach: treat agents as untrusted processes with identity + least privilege 22:33 Zero Trust for Agents: Access ≠ Permission to Act 23:27 Token's "Intent-Based Permission Management" Explained 25:29 Building the Identity Map: Tracing What Agents Touch 26:52 The Secret Sauce: Using AI to Secure AI in Real Time 28:10 Real-World Case: 1,500 Agents and Wildly Over-Provisioned Access 30:57 CUA 'Computer-Use' Agents: Exciting, Personal… and Terrifying 34:44 Secure-by-Default & Sandboxing: Fixing 'Always Allow' Dark Patterns 35:36 What Security Teams Should Do Now: Inventory, Boundaries, Governance 37:59 What's Next: Agent Teams and Multi-Day Autonomous Work 40:10 Tony Stark Vision: Agents That Improve the Human Experience 41:02 RSA Innovation Sandbox: Token's Big Bet on Intent + Access 43:01 Wrap-Up, Audience Q&A, and Sponsor Message

In this frequently requested episode, I'll go over each of the dividend stocks in my multi-million dollar portfolio and share whether I believe them to be undervalued, fairly priced, or overvalued, which may help inform your own research as you decide whether now is the time to buy, hold, or sell. Join the world's largest free Dividend Discord ➜ https://discord.gg/kkSr5FY Join my channel membership as a GenEx Partner to access new perks: https://www.youtube.com/channel/UCuOS-UH_s4KGhArN6HdRB0Q/join Seeking Alpha Affiliate Referral Link ➜ https://link.seekingalpha.com/2352ZCK/4G6SHH/ Click my FAST Graphs Link (Use coupon code AFFILIATE25 to get 25% off your 1st payment) ➜ https://fastgraphs.com/?ref=GenExDividendInvestor Please use my Amazon Affiliates Link ➜ https://amzn.to/2YLxsiW Thanks! As an Amazon Associate I earn from qualifying purchases. Support me & get Patreon perks ➜ https://www.patreon.com/join/genexdividendinvestor Use my Financial Modeling Prep affiliate link for awesome stock API data (up to a 25% discount) ➡️ https://site.financialmodelingprep.com/pricing-plans?couponCode=genex25

Michael Truell, CEO of Cursor, sits down with Patrick Collison, CEO of Stripe and an investor in Anysphere, to talk about Collison's history with Smalltalk and Lisp, the MongoDB and Ruby decisions Stripe still lives with 15 years later, why he'd spend even more time on API design if he could do it over, and whether AI is actually showing up in economic productivity data. This episode originally aired on Cursor's podcast.   Resources:  Follow Patrick Collison on X:   https://twitter.com/patrickc Follow Michael Truell on X: https://twitter.com/mntruell Follow Cursor: https://www.youtube.com/@cursor_ai Stay Updated:Find a16z on YouTube: YouTubeFind 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.

Box CEO Aaron Levie joins for our weekly discussion of the latest tech news. We cover: 1) OpenAI's anticipated $100 billion fundraise 2) Does OpenAI's big forthcoming raise settle questions about its competitiveness 3) What's going on with OpenAI and NVIDIA? 4) Hype or True: Big Proclamations from the India AI Impact Summit 5) Why can't Sam And Dario hold hands? 6) Anthropic's powerful new model 7) OpenAI acquires OpenClaw 8) What the acquisition portends 9) If software is an API, what is software? 10) Wait, is AI not increasing productivity? --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b EXCLUSIVE NordVPN Deal ➼ https://nordvpn.com/bigtech  Try it risk-free now with a 30-day money-back guarantee! Take back your personal data with Incogni! Go to incogni.com/bigtechpod and Use code bigtechpod at checkout, our code will get you 60% off on annual plans. Go check it out! Learn more about your ad choices. Visit megaphone.fm/adchoices

This week on Defender Fridays, Farshad Abasi, Founder and CEO of Forward Security and Eureka DevSecOps, discusses how AI can help us set a new standard in app and cloud security. Farshad brings over 27 years of industry experience to the forefront of cybersecurity innovation. His professional journey includes key technical roles at Intel and Motorola, evolving into senior security positions as the Principal Security Architect for HSBC Global, and Head of IT Security for the Canadian division. Farshad's commitment to the field extends to his role as an instructor at BCIT, where he imparts his wealth of knowledge to the next generation of cybersecurity experts. His diverse experience, which spans startups to large enterprises, informs his approach to delivering adaptive and reliable solutions.Engaged actively in the cybersecurity community through roles in BSides Vancouver/MARS, OWASP Vancouver/AppSec PNW, and as a CISSP designate, Farshad's vision and leadership continue to drive the industry forward. Under his guidance, Forward Security is setting new standards in application and cloud security. Learn more at https://www.eurekadevsecops.com/ and https://forwardsecurity.com/Register for Live SessionsJoin us every Friday at 10:30am PT for live, interactive discussions with industry experts. Whether you're a seasoned professional or just curious about the field, these sessions offer an engaging dialogue between our guests, hosts, and you – our audience.Register here: https://limacharlie.io/defender-fridaysSubscribe to our YouTube channel and hit the notification bell to never miss a live session or catch up on past episodes!Sponsored by LimaCharlieThis episode is brought to you by LimaCharlie, a cloud-native SecOps platform where AI agents operate security infrastructure directly. Founded in 2018, LimaCharlie provides complete API coverage across detection, response, automation, and telemetry, with multi-tenant architecture designed for MSSPs and MDR providers managing thousands of unique client environments.Why LimaCharlie?Transparency: Complete visibility into every action and decision. No black boxes, no vendor lock-in.Scalability: Security operations that scale like infrastructure, not like procurement cycles. Move at cloud speed.Unopinionated Design: Integrate the tools you need, not just those contracts allow. Build security on your terms.Agentic SecOps Workspace (ASW): AI agents that operate alongside your team with observable, auditable actions through the same APIs human analysts use.Security Primitives: Composable building blocks that endure as tools come and go. Build once, evolve continuously.Try the Agentic SecOps Workspace free: https://limacharlie.ioLearn more: https://docs.limacharlie.ioFollow LimaCharlieSign up for free: https://limacharlie.ioLinkedIn: / limacharlieio X: https://x.com/limacharlieioCommunity Discourse: https://community.limacharlie.com/Host: Maxime Lamothe-Brassard - CEO / Co-founder at LimaCharlie

Future-Proofing Leadership: Masterminds and the AI Revolution with Brad HartIn this episode of The Thoughtful Entrepreneur Podcast, host Josh Elledge sits down with Brad Hart, the Founder of Optimus, to discuss the critical intersection of high-level peer communities and the rapid advancement of artificial intelligence. Brad, a seasoned entrepreneur who has launched over 25 mastermind groups globally, shares how curated human connection serves as the ultimate safeguard against the isolation and disruption of the digital age. This conversation provides a strategic roadmap for small and medium-sized enterprise (SME) leaders looking to integrate AI into their operations while maintaining the deep relationships and creative judgment that technology simply cannot replicate.The Strategic Value of Curated Communities in a Tech-Driven WorldThe modern business landscape often leaves leaders isolated, navigating complex technological shifts like AI and automation without a trusted sounding board. Brad identifies "The Three R's"—Results, Relationships, and Recreation—as the essential pillars of a high-impact mastermind group. For a community to be truly transformative, it must drive tangible business outcomes through accountability, foster deep vulnerability among peers, and incorporate shared experiences that combat the pervasive loneliness of leadership. When these elements align, a mastermind becomes more than just a networking group; it evolves into an engine for innovation that helps members ask better questions and see blind spots they would otherwise miss.As AI becomes a prediction machine capable of processing vast amounts of data, the role of the human leader is shifting toward wisdom, taste, and discretion. Brad emphasizes that while AI can accelerate the work of a skilled individual, it cannot replace the nuanced judgment or emotional intelligence found in a curated peer group. SME leaders who fail to implement AI by 2030 will likely struggle to remain competitive, but those who succeed will be the ones who treat AI as an accelerator rather than a replacement. By automating routine tasks, leaders can free up their capacity for the high-level strategic thinking and relationship-building that provide a permanent edge in any market.To bridge the gap between current operations and an AI-driven future, Brad developed Optimus—a new model of mastermind that combines high-level peer support with cutting-edge technical integration. Unlike traditional coaching programs, this model leverages an integrated platform that connects to a business's tech stack via API, allowing leaders to interact with their data using natural language. This "done-with-you" approach ensures that entrepreneurs aren't just learning about AI in theory, but are actively implementing workflows that increase efficiency and resilience. Ultimately, the goal is to build a business that is technologically advanced yet remains deeply rooted in authentic human connection.About Brad HartBrad Hart is the Founder of Optimus and a recognized expert in building and scaling mastermind groups. With a background that includes launching a hedge fund and early ventures in cryptocurrency, Brad has dedicated his career to helping entrepreneurs unlock their potential through the power of curated communities and strategic automation.About OptimusOptimus is a specialized mastermind group and technology platform designed to help small and medium-sized enterprises prepare for the future of AI. By providing both a high-level peer network and an API-driven automation platform, Optimus helps business leaders streamline their operations and future-proof their companies.Links Mentioned in This Episode:Optimus Official Website

Your email gateway isn't enough anymore, attackers are already inside the workspace through OAuth apps, browser extensions, and account takeover.  In this episode, Ron sits down with Rajan Kapoor, VP of Security at Material Security, to break down the real risks hiding inside Google Workspace and Microsoft 365. They cover how phishing has evolved into full-blown business email compromise, why malicious OAuth apps are the new favorite attack vector, and what security teams, especially lean ones, can do right now to lock down their cloud workspace. Rajan also drops practical advice on passkeys, document sharing hygiene, and why data lifecycle management is a problem no one is solving well enough. Impactful Moments 00:00 – Introduction 03:30 – The current state of phishing 05:30 – Outbound email compromise risk 09:30 – OAuth apps as attack vectors 15:00 – AI agents accessing your workspace 16:00 – Prompt injection is the new SQL injection 18:00 – Allow listing apps immediately 24:30 – Google Workspace vs Microsoft 365 security 27:30 – Custom detections require API expertise 28:00 – Why passkeys matter right now 32:00 – Data lifecycle management for shared docs Links Connect with our guest, Rajan Kapoor, on LinkedIn: https://www.linkedin.com/in/rajankkapoor/ Learn more about Material Security: https://material.security  ___ Become a sponsor of the show to amplify your brand: https://hackervalley.com/work-with-us/ Check out our upcoming events: https://www.hackervalley.com/livestreams  Love Hacker Valley Studio? Pick up some swag: https://store.hackervalley.com   

Open banking in the United States has been on a long and winding road, and the journey is far from over. In this episode, I sit down with Steve Boms, Executive Director of FDATA North America, the trade association representing the fintech companies at the heart of the open banking ecosystem. Steve has been one of the most active voices in shaping U.S. open banking policy for over a decade, and he brings a uniquely informed perspective to where things stand today.We dig into the current state of the 1033 rule and what amendments are likely coming, FDATA's firm stance that banks should not be permitted to charge fees for consumer-directed data access, and the growing complexity created by a patchwork of state-level regulations on data privacy, AI, and fintech products. We close with a fascinating discussion on how agentic AI, with its need for clear consent frameworks, robust APIs, and defined liability rules, could become the next major catalyst that finally forces meaningful open banking progress in this country.In this podcast you will learn:The origin story of FDATA in the UK and how it came to the US.How Steve has been involved with CFPB and Section 1033 since 2015.Over the next 10+ years, how FDATA has been engaged in open banking policy.How open banking and open finance has evolved in the UK.Who their members are and what FDATA does for them.Where we are at today when it comes to the 1033 rule.The FDATA view on banks charging fees for access to their data.Why this is not really a bank versus fintech fight.Why it may be many years before we have a final rule for open banking.Why data access negotiations have been put on pause for now.What else Steve is working on beyond open banking.Why he is increasing concerned about the Balkanization of financial services regulation (see his recent Open Banker column).How they coordinate with the other fintech trade associations.How they think about the standardization of API and other data standards.Why Steve is optimistic about the future of open banking in the U.S.Why AI agents could be a catalyzing force for clear open banking rules.Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes

In 2026, Java keeps evolving: Project Valhalla is gunning for merging its value types preview in the second half of this year; Babylon wants to incubate code reflection; Loom will probably finalize the structured concurrency API; Leyden plans to ship AOT code compilation; and Amber hopes to present JEPs on constant patterns and pattern assignments. And those are just the most progressed features - more are in the pipeline and discussed in this episode of the Inside Java Newscast.

Tickets for AIEi Miami and AIE Europe are live, with first wave speakers announced!From pioneering software-defined networking to backing many of the most aggressive AI model companies of this cycle, Martin Casado and Sarah Wang sit at the center of the capital, compute, and talent arms race reshaping the tech industry. As partners at a16z investing across infrastructure and growth, they've watched venture and growth blur, model labs turn dollars into capability at unprecedented speed, and startups raise nine-figure rounds before monetization.Martin and Sarah join us to unpack the new financing playbook for AI: why today's rounds are really compute contracts in disguise, how the “raise → train → ship → raise bigger” flywheel works, and whether foundation model companies can outspend the entire app ecosystem built on top of them. They also share what's underhyped (boring enterprise software), what's overheated (talent wars and compensation spirals), and the two radically different futures they see for AI's market structure.We discuss:* Martin's “two futures” fork: infinite fragmentation and new software categories vs. a small oligopoly of general models that consume everything above them* The capital flywheel: how model labs translate funding directly into capability gains, then into revenue growth measured in weeks, not years* Why venture and growth have merged: $100M–$1B hybrid rounds, strategic investors, compute negotiations, and complex deal structures* The AGI vs. product tension: allocating scarce GPUs between long-term research and near-term revenue flywheels* Whether frontier labs can out-raise and outspend the entire app ecosystem built on top of their APIs* Why today's talent wars ($10M+ comp packages, $B acqui-hires) are breaking early-stage founder math* Cursor as a case study: building up from the app layer while training down into your own models* Why “boring” enterprise software may be the most underinvested opportunity in the AI mania* Hardware and robotics: why the ChatGPT moment hasn't yet arrived for robots and what would need to change* World Labs and generative 3D: bringing the marginal cost of 3D scene creation down by orders of magnitude* Why public AI discourse is often wildly disconnected from boardroom reality and how founders should navigate the noiseShow Notes:* “Where Value Will Accrue in AI: Martin Casado & Sarah Wang” - a16z show* “Jack Altman & Martin Casado on the Future of Venture Capital”* World Labs—Martin Casado• LinkedIn: https://www.linkedin.com/in/martincasado/• X: https://x.com/martin_casadoSarah Wang• LinkedIn: https://www.linkedin.com/in/sarah-wang-59b96a7• X: https://x.com/sarahdingwanga16z• https://a16z.com/Timestamps00:00:00 – Intro: Live from a16z00:01:20 – The New AI Funding Model: Venture + Growth Collide00:03:19 – Circular Funding, Demand & “No Dark GPUs”00:05:24 – Infrastructure vs Apps: The Lines Blur00:06:24 – The Capital Flywheel: Raise → Train → Ship → Raise Bigger00:09:39 – Can Frontier Labs Outspend the Entire App Ecosystem?00:11:24 – Character AI & The AGI vs Product Dilemma00:14:39 – Talent Wars, $10M Engineers & Founder Anxiety00:17:33 – What's Underinvested? The Case for “Boring” Software00:19:29 – Robotics, Hardware & Why It's Hard to Win00:22:42 – Custom ASICs & The $1B Training Run Economics00:24:23 – American Dynamism, Geography & AI Power Centers00:26:48 – How AI Is Changing the Investor Workflow (Claude Cowork)00:29:12 – Two Futures of AI: Infinite Expansion or Oligopoly?00:32:48 – If You Can Raise More Than Your Ecosystem, You Win00:34:27 – Are All Tasks AGI-Complete? Coding as the Test Case00:38:55 – Cursor & The Power of the App Layer00:44:05 – World Labs, Spatial Intelligence & 3D Foundation Models00:47:20 – Thinking Machines, Founder Drama & Media Narratives00:52:30 – Where Long-Term Power Accrues in the AI StackTranscriptLatent.Space - Inside AI's $10B+ Capital Flywheel — Martin Casado & Sarah Wang of a16z[00:00:00] Welcome to Latent Space (Live from a16z) + Meet the Guests[00:00:00] Alessio: Hey everyone. Welcome to the Latent Space podcast, live from a 16 z. Uh, this is Alessio founder Kernel Lance, and I'm joined by Twix, editor of Latent Space.[00:00:08] swyx: Hey, hey, hey. Uh, and we're so glad to be on with you guys. Also a top AI podcast, uh, Martin Cado and Sarah Wang. Welcome, very[00:00:16] Martin Casado: happy to be here and welcome.[00:00:17] swyx: Yes, uh, we love this office. We love what you've done with the place. Uh, the new logo is everywhere now. It's, it's still getting, takes a while to get used to, but it reminds me of like sort of a callback to a more ambitious age, which I think is kind of[00:00:31] Martin Casado: definitely makes a statement.[00:00:33] swyx: Yeah.[00:00:34] Martin Casado: Not quite sure what that statement is, but it makes a statement.[00:00:37] swyx: Uh, Martin, I go back with you to Netlify.[00:00:40] Martin Casado: Yep.[00:00:40] swyx: Uh, and, uh, you know, you create a software defined networking and all, all that stuff people can read up on your background. Yep. Sarah, I'm newer to you. Uh, you, you sort of started working together on AI infrastructure stuff.[00:00:51] Sarah Wang: That's right. Yeah. Seven, seven years ago now.[00:00:53] Martin Casado: Best growth investor in the entire industry.[00:00:55] swyx: Oh, say[00:00:56] Martin Casado: more hands down there is, there is. [00:01:00] I mean, when it comes to AI companies, Sarah, I think has done the most kind of aggressive, um, investment thesis around AI models, right? So, worked for Nom Ja, Mira Ia, FEI Fey, and so just these frontier, kind of like large AI models.[00:01:15] I think, you know, Sarah's been the, the broadest investor. Is that fair?[00:01:20] Venture vs. Growth in the Frontier Model Era[00:01:20] Sarah Wang: No, I, well, I was gonna say, I think it's been a really interesting tag, tag team actually just ‘cause the, a lot of these big C deals, not only are they raising a lot of money, um, it's still a tech founder bet, which obviously is inherently early stage.[00:01:33] But the resources,[00:01:36] Martin Casado: so many, I[00:01:36] Sarah Wang: was gonna say the resources one, they just grow really quickly. But then two, the resources that they need day one are kind of growth scale. So I, the hybrid tag team that we have is. Quite effective, I think,[00:01:46] Martin Casado: what is growth these days? You know, you don't wake up if it's less than a billion or like, it's, it's actually, it's actually very like, like no, it's a very interesting time in investing because like, you know, take like the character around, right?[00:01:59] These tend to [00:02:00] be like pre monetization, but the dollars are large enough that you need to have a larger fund and the analysis. You know, because you've got lots of users. ‘cause this stuff has such high demand requires, you know, more of a number sophistication. And so most of these deals, whether it's US or other firms on these large model companies, are like this hybrid between venture growth.[00:02:18] Sarah Wang: Yeah. Total. And I think, you know, stuff like BD for example, you wouldn't usually need BD when you were seed stage trying to get market biz Devrel. Biz Devrel, exactly. Okay. But like now, sorry, I'm,[00:02:27] swyx: I'm not familiar. What, what, what does biz Devrel mean for a venture fund? Because I know what biz Devrel means for a company.[00:02:31] Sarah Wang: Yeah.[00:02:32] Compute Deals, Strategics, and the ‘Circular Funding' Question[00:02:32] Sarah Wang: You know, so a, a good example is, I mean, we talk about buying compute, but there's a huge negotiation involved there in terms of, okay, do you get equity for the compute? What, what sort of partner are you looking at? Is there a go-to market arm to that? Um, and these are just things on this scale, hundreds of millions, you know, maybe.[00:02:50] Six months into the inception of a company, you just wouldn't have to negotiate these deals before.[00:02:54] Martin Casado: Yeah. These large rounds are very complex now. Like in the past, if you did a series A [00:03:00] or a series B, like whatever, you're writing a 20 to a $60 million check and you call it a day. Now you normally have financial investors and strategic investors, and then the strategic portion always still goes with like these kind of large compute contracts, which can take months to do.[00:03:13] And so it's, it's very different ties. I've been doing this for 10 years. It's the, I've never seen anything like this.[00:03:19] swyx: Yeah. Do you have worries about the circular funding from so disease strategics?[00:03:24] Martin Casado: I mean, listen, as long as the demand is there, like the demand is there. Like the problem with the internet is the demand wasn't there.[00:03:29] swyx: Exactly. All right. This, this is like the, the whole pyramid scheme bubble thing, where like, as long as you mark to market on like the notional value of like, these deals, fine, but like once it starts to chip away, it really Well[00:03:41] Martin Casado: no, like as, as, as, as long as there's demand. I mean, you know, this, this is like a lot of these sound bites have already become kind of cliches, but they're worth saying it.[00:03:47] Right? Like during the internet days, like we were. Um, raising money to put fiber in the ground that wasn't used. And that's a problem, right? Because now you actually have a supply overhang.[00:03:58] swyx: Mm-hmm.[00:03:59] Martin Casado: And even in the, [00:04:00] the time of the, the internet, like the supply and, and bandwidth overhang, even as massive as it was in, as massive as the crash was only lasted about four years.[00:04:09] But we don't have a supply overhang. Like there's no dark GPUs, right? I mean, and so, you know, circular or not, I mean, you know, if, if someone invests in a company that, um. You know, they'll actually use the GPUs. And on the other side of it is the, is the ask for customer. So I I, I think it's a different time.[00:04:25] Sarah Wang: I think the other piece, maybe just to add onto this, and I'm gonna quote Martine in front of him, but this is probably also a unique time in that. For the first time, you can actually trace dollars to outcomes. Yeah, right. Provided that scaling laws are, are holding, um, and capabilities are actually moving forward.[00:04:40] Because if you can put translate dollars into capabilities, uh, a capability improvement, there's demand there to martine's point. But if that somehow breaks, you know, obviously that's an important assumption in this whole thing to make it work. But you know, instead of investing dollars into sales and marketing, you're, you're investing into r and d to get to the capability, um, you know, increase.[00:04:59] And [00:05:00] that's sort of been the demand driver because. Once there's an unlock there, people are willing to pay for it.[00:05:05] Alessio: Yeah.[00:05:06] Blurring Lines: Models as Infra + Apps, and the New Fundraising Flywheel[00:05:06] Alessio: Is there any difference in how you built the portfolio now that some of your growth companies are, like the infrastructure of the early stage companies, like, you know, OpenAI is now the same size as some of the cloud providers were early on.[00:05:16] Like what does that look like? Like how much information can you feed off each other between the, the two?[00:05:24] Martin Casado: There's so many lines that are being crossed right now, or blurred. Right. So we already talked about venture and growth. Another one that's being blurred is between infrastructure and apps, right? So like what is a model company?[00:05:35] Mm-hmm. Like, it's clearly infrastructure, right? Because it's like, you know, it's doing kind of core r and d. It's a horizontal platform, but it's also an app because it's um, uh, touches the users directly. And then of course. You know, the, the, the growth of these is just so high. And so I actually think you're just starting to see a, a, a new financing strategy emerge and, you know, we've had to adapt as a result of that.[00:05:59] And [00:06:00] so there's been a lot of changes. Um, you're right that these companies become platform companies very quickly. You've got ecosystem build out. So none of this is necessarily new, but the timescales of which it's happened is pretty phenomenal. And the way we'd normally cut lines before is blurred a little bit, but.[00:06:16] But that, that, that said, I mean, a lot of it also just does feel like things that we've seen in the past, like cloud build out the internet build out as well.[00:06:24] Sarah Wang: Yeah. Um, yeah, I think it's interesting, uh, I don't know if you guys would agree with this, but it feels like the emerging strategy is, and this builds off of your other question, um.[00:06:33] You raise money for compute, you pour that or you, you pour the money into compute, you get some sort of breakthrough. You funnel the breakthrough into your vertically integrated application. That could be chat GBT, that could be cloud code, you know, whatever it is. You massively gain share and get users.[00:06:49] Maybe you're even subsidizing at that point. Um, depending on your strategy. You raise money at the peak momentum and then you repeat, rinse and repeat. Um, and so. And that wasn't [00:07:00] true even two years ago, I think. Mm-hmm. And so it's sort of to your, just tying it to fundraising strategy, right? There's a, and hiring strategy.[00:07:07] All of these are tied, I think the lines are blurring even more today where everyone is, and they, but of course these companies all have API businesses and so they're these, these frenemy lines that are getting blurred in that a lot of, I mean, they have billions of dollars of API revenue, right? And so there are customers there.[00:07:23] But they're competing on the app layer.[00:07:24] Martin Casado: Yeah. So this is a really, really important point. So I, I would say for sure, venture and growth, that line is blurry app and infrastructure. That line is blurry. Um, but I don't think that that changes our practice so much. But like where the very open questions are like, does this layer in the same way.[00:07:43] Compute traditionally has like during the cloud is like, you know, like whatever, somebody wins one layer, but then another whole set of companies wins another layer. But that might not, might not be the case here. It may be the case that you actually can't verticalize on the token string. Like you can't build an app like it, it necessarily goes down just because there are no [00:08:00] abstractions.[00:08:00] So those are kinda the bigger existential questions we ask. Another thing that is very different this time than in the history of computer sciences is. In the past, if you raised money, then you basically had to wait for engineering to catch up. Which famously doesn't scale like the mythical mammoth. It take a very long time.[00:08:18] But like that's not the case here. Like a model company can raise money and drop a model in a, in a year, and it's better, right? And, and it does it with a team of 20 people or 10 people. So this type of like money entering a company and then producing something that has demand and growth right away and using that to raise more money is a very different capital flywheel than we've ever seen before.[00:08:39] And I think everybody's trying to understand what the consequences are. So I think it's less about like. Big companies and growth and this, and more about these more systemic questions that we actually don't have answers to.[00:08:49] Alessio: Yeah, like at Kernel Labs, one of our ideas is like if you had unlimited money to spend productively to turn tokens into products, like the whole early stage [00:09:00] market is very different because today you're investing X amount of capital to win a deal because of price structure and whatnot, and you're kind of pot committing.[00:09:07] Yeah. To a certain strategy for a certain amount of time. Yeah. But if you could like iteratively spin out companies and products and just throw, I, I wanna spend a million dollar of inference today and get a product out tomorrow.[00:09:18] swyx: Yeah.[00:09:19] Alessio: Like, we should get to the point where like the friction of like token to product is so low that you can do this and then you can change the Right, the early stage venture model to be much more iterative.[00:09:30] And then every round is like either 100 k of inference or like a hundred million from a 16 Z. There's no, there's no like $8 million C round anymore. Right.[00:09:38] When Frontier Labs Outspend the Entire App Ecosystem[00:09:38] Martin Casado: But, but, but, but there's a, there's a, the, an industry structural question that we don't know the answer to, which involves the frontier models, which is, let's take.[00:09:48] Anthropic it. Let's say Anthropic has a state-of-the-art model that has some large percentage of market share. And let's say that, uh, uh, uh, you know, uh, a company's building smaller models [00:10:00] that, you know, use the bigger model in the background, open 4.5, but they add value on top of that. Now, if Anthropic can raise three times more.[00:10:10] Every subsequent round, they probably can raise more money than the entire app ecosystem that's built on top of it. And if that's the case, they can expand beyond everything built on top of it. It's like imagine like a star that's just kind of expanding, so there could be a systemic. There could be a, a systemic situation where the soda models can raise so much money that they can out pay anybody that bills on top of ‘em, which would be something I don't think we've ever seen before just because we were so bottlenecked in engineering, and this is a very open question.[00:10:41] swyx: Yeah. It's, it is almost like bitter lesson applied to the startup industry.[00:10:45] Martin Casado: Yeah, a hundred percent. It literally becomes an issue of like raise capital, turn that directly into growth. Use that to raise three times more. Exactly. And if you can keep doing that, you literally can outspend any company that's built the, not any company.[00:10:57] You can outspend the aggregate of companies on top of [00:11:00] you and therefore you'll necessarily take their share, which is crazy.[00:11:02] swyx: Would you say that kind of happens in character? Is that the, the sort of postmortem on. What happened?[00:11:10] Sarah Wang: Um,[00:11:10] Martin Casado: no.[00:11:12] Sarah Wang: Yeah, because I think so,[00:11:13] swyx: I mean the actual postmortem is, he wanted to go back to Google.[00:11:15] Exactly. But like[00:11:18] Martin Casado: that's another difference that[00:11:19] Sarah Wang: you said[00:11:21] Martin Casado: it. We should talk, we should actually talk about that.[00:11:22] swyx: Yeah,[00:11:22] Sarah Wang: that's[00:11:23] swyx: Go for it. Take it. Take,[00:11:23] Sarah Wang: yeah.[00:11:24] Character.AI, Founder Goals (AGI vs Product), and GPU Allocation Tradeoffs[00:11:24] Sarah Wang: I was gonna say, I think, um. The, the, the character thing raises actually a different issue, which actually the Frontier Labs will face as well. So we'll see how they handle it.[00:11:34] But, um, so we invest in character in January, 2023, which feels like eons ago, I mean, three years ago. Feels like lifetimes ago. But, um, and then they, uh, did the IP licensing deal with Google in August, 2020. Uh, four. And so, um, you know, at the time, no, you know, he's talked publicly about this, right? He wanted to Google wouldn't let him put out products in the world.[00:11:56] That's obviously changed drastically. But, um, he went to go do [00:12:00] that. Um, but he had a product attached. The goal was, I mean, it's Nome Shair, he wanted to get to a GI. That was always his personal goal. But, you know, I think through collecting data, right, and this sort of very human use case, that the character product.[00:12:13] Originally was and still is, um, was one of the vehicles to do that. Um, I think the real reason that, you know. I if you think about the, the stress that any company feels before, um, you ultimately going one way or the other is sort of this a GI versus product. Um, and I think a lot of the big, I think, you know, opening eyes, feeling that, um, anthropic if they haven't started, you know, felt it, certainly given the success of their products, they may start to feel that soon.[00:12:39] And the real. I think there's real trade-offs, right? It's like how many, when you think about GPUs, that's a limited resource. Where do you allocate the GPUs? Is it toward the product? Is it toward new re research? Right? Is it, or long-term research, is it toward, um, n you know, near to midterm research? And so, um, in a case where you're resource constrained, um, [00:13:00] of course there's this fundraising game you can play, right?[00:13:01] But the fund, the market was very different back in 2023 too. Um. I think the best researchers in the world have this dilemma of, okay, I wanna go all in on a GI, but it's the product usage revenue flywheel that keeps the revenue in the house to power all the GPUs to get to a GI. And so it does make, um, you know, I think it sets up an interesting dilemma for any startup that has trouble raising up until that level, right?[00:13:27] And certainly if you don't have that progress, you can't continue this fly, you know, fundraising flywheel.[00:13:32] Martin Casado: I would say that because, ‘cause we're keeping track of all of the things that are different, right? Like, you know, venture growth and uh, app infra and one of the ones is definitely the personalities of the founders.[00:13:45] It's just very different this time I've been. Been doing this for a decade and I've been doing startups for 20 years. And so, um, I mean a lot of people start this to do a GI and we've never had like a unified North star that I recall in the same [00:14:00] way. Like people built companies to start companies in the past.[00:14:02] Like that was what it was. Like I would create an internet company, I would create infrastructure company, like it's kind of more engineering builders and this is kind of a different. You know, mentality. And some companies have harnessed that incredibly well because their direction is so obviously on the path to what somebody would consider a GI, but others have not.[00:14:20] And so like there is always this tension with personnel. And so I think we're seeing more kind of founder movement.[00:14:27] Sarah Wang: Yeah.[00:14:27] Martin Casado: You know, as a fraction of founders than we've ever seen. I mean, maybe since like, I don't know the time of like Shockly and the trade DUR aid or something like that. Way back in the beginning of the industry, I, it's a very, very.[00:14:38] Unusual time of personnel.[00:14:39] Sarah Wang: Totally.[00:14:40] Talent Wars, Mega-Comp, and the Rise of Acquihire M&A[00:14:40] Sarah Wang: And it, I think it's exacerbated by the fact that talent wars, I mean, every industry has talent wars, but not at this magnitude, right? No. Yeah. Very rarely can you see someone get poached for $5 billion. That's hard to compete with. And then secondly, if you're a founder in ai, you could fart and it would be on the front page of, you know, the information these days.[00:14:59] And so there's [00:15:00] sort of this fishbowl effect that I think adds to the deep anxiety that, that these AI founders are feeling.[00:15:06] Martin Casado: Hmm.[00:15:06] swyx: Uh, yes. I mean, just on, uh, briefly comment on the founder, uh, the sort of. Talent wars thing. I feel like 2025 was just like a blip. Like I, I don't know if we'll see that again.[00:15:17] ‘cause meta built the team. Like, I don't know if, I think, I think they're kind of done and like, who's gonna pay more than meta? I, I don't know.[00:15:23] Martin Casado: I, I agree. So it feels so, it feel, it feels this way to me too. It's like, it is like, basically Zuckerberg kind of came out swinging and then now he's kind of back to building.[00:15:30] Yeah,[00:15:31] swyx: yeah. You know, you gotta like pay up to like assemble team to rush the job, whatever. But then now, now you like you, you made your choices and now they got a ship.[00:15:38] Martin Casado: I mean, the, the o other side of that is like, you know, like we're, we're actually in the job hiring market. We've got 600 people here. I hire all the time.[00:15:44] I've got three open recs if anybody's interested, that's listening to this for investor. Yeah, on, on the team, like on the investing side of the team, like, and, um, a lot of the people we talk to have acting, you know, active, um, offers for 10 million a year or something like that. And like, you know, and we pay really, [00:16:00] really well.[00:16:00] And just to see what's out on the market is really, is really remarkable. And so I would just say it's actually, so you're right, like the really flashy one, like I will get someone for, you know, a billion dollars, but like the inflated, um, uh, trickles down. Yeah, it is still very active today. I mean,[00:16:18] Sarah Wang: yeah, you could be an L five and get an offer in the tens of millions.[00:16:22] Okay. Yeah. Easily. Yeah. It's so I think you're right that it felt like a blip. I hope you're right. Um, but I think it's been, the steady state is now, I think got pulled up. Yeah. Yeah. I'll pull up for[00:16:31] Martin Casado: sure. Yeah.[00:16:32] Alessio: Yeah. And I think that's breaking the early stage founder math too. I think before a lot of people would be like, well, maybe I should just go be a founder instead of like getting paid.[00:16:39] Yeah. 800 KA million at Google. But if I'm getting paid. Five, 6 million. That's different but[00:16:45] Martin Casado: on. But on the other hand, there's more strategic money than we've ever seen historically, right? Mm-hmm. And so, yep. The economics, the, the, the, the calculus on the economics is very different in a number of ways. And, uh, it's crazy.[00:16:58] It's cra it's causing like a, [00:17:00] a, a, a ton of change in confusion in the market. Some very positive, sub negative, like, so for example, the other side of the, um. The co-founder, like, um, acquisition, you know, mark Zuckerberg poaching someone for a lot of money is like, we were actually seeing historic amount of m and a for basically acquihires, right?[00:17:20] That you like, you know, really good outcomes from a venture perspective that are effective acquihires, right? So I would say it's probably net positive from the investment standpoint, even though it seems from the headlines to be very disruptive in a negative way.[00:17:33] Alessio: Yeah.[00:17:33] What's Underfunded: Boring Software, Robotics Skepticism, and Custom Silicon Economics[00:17:33] Alessio: Um, let's talk maybe about what's not being invested in, like maybe some interesting ideas that you would see more people build or it, it seems in a way, you know, as ycs getting more popular, it's like access getting more popular.[00:17:47] There's a startup school path that a lot of founders take and they know what's hot in the VC circles and they know what gets funded. Uh, and there's maybe not as much risk appetite for. Things outside of that. Um, I'm curious if you feel [00:18:00] like that's true and what are maybe, uh, some of the areas, uh, that you think are under discussed?[00:18:06] Martin Casado: I mean, I actually think that we've taken our eye off the ball in a lot of like, just traditional, you know, software companies. Um, so like, I mean. You know, I think right now there's almost a barbell, like you're like the hot thing on X, you're deep tech.[00:18:21] swyx: Mm-hmm.[00:18:22] Martin Casado: Right. But I, you know, I feel like there's just kind of a long, you know, list of like good.[00:18:28] Good companies that will be around for a long time in very large markets. Say you're building a database, you know, say you're building, um, you know, kind of monitoring or logging or tooling or whatever. There's some good companies out there right now, but like, they have a really hard time getting, um, the attention of investors.[00:18:43] And it's almost become a meme, right? Which is like, if you're not basically growing from zero to a hundred in a year, you're not interesting, which is just, is the silliest thing to say. I mean, think of yourself as like an introvert person, like, like your personal money, right? Mm-hmm. So. Your personal money, will you put it in the stock market at 7% or you put it in this company growing five x in a very large [00:19:00] market?[00:19:00] Of course you can put it in the company five x. So it's just like we say these stupid things, like if you're not going from zero to a hundred, but like those, like who knows what the margins of those are mean. Clearly these are good investments. True for anybody, right? True. Like our LPs want whatever.[00:19:12] Three x net over, you know, the life cycle of a fund, right? So a, a company in a big market growing five X is a great investment. We'd, everybody would be happy with these returns, but we've got this kind of mania on these, these strong growths. And so I would say that that's probably the most underinvested sector.[00:19:28] Right now.[00:19:29] swyx: Boring software, boring enterprise software.[00:19:31] Martin Casado: Traditional. Really good company.[00:19:33] swyx: No, no AI here.[00:19:34] Martin Casado: No. Like boring. Well, well, the AI of course is pulling them into use cases. Yeah, but that's not what they're, they're not on the token path, right? Yeah. Let's just say that like they're software, but they're not on the token path.[00:19:41] Like these are like they're great investments from any definition except for like random VC on Twitter saying VC on x, saying like, it's not growing fast enough. What do you[00:19:52] Sarah Wang: think? Yeah, maybe I'll answer a slightly different. Question, but adjacent to what you asked, um, which is maybe an area that we're not, uh, investing [00:20:00] right now that I think is a question and we're spending a lot of time in regardless of whether we pull the trigger or not.[00:20:05] Um, and it would probably be on the hardware side, actually. Robotics, right? And the robotics side. Robotics. Right. Which is, it's, I don't wanna say that it's not getting funding ‘cause it's clearly, uh, it's, it's sort of non-consensus to almost not invest in robotics at this point. But, um, we spent a lot of time in that space and I think for us, we just haven't seen the chat GPT moment.[00:20:22] Happen on the hardware side. Um, and the funding going into it feels like it's already. Taking that for granted.[00:20:30] Martin Casado: Yeah. Yeah. But we also went through the drone, you know, um, there's a zip line right, right out there. What's that? Oh yeah, there's a zip line. Yeah. What the drone, what the av And like one of the takeaways is when it comes to hardware, um, most companies will end up verticalizing.[00:20:46] Like if you're. If you're investing in a robot company for an A for agriculture, you're investing in an ag company. ‘cause that's the competition and that's surprising. And that's supply chain. And if you're doing it for mining, that's mining. And so the ad team does a lot of that type of stuff ‘cause they actually set up to [00:21:00] diligence that type of work.[00:21:01] But for like horizontal technology investing, there's very little when it comes to robots just because it's so fit for, for purpose. And so we kinda like to look at software. Solutions or horizontal solutions like applied intuition. Clearly from the AV wave deep map, clearly from the AV wave, I would say scale AI was actually a horizontal one for That's fair, you know, for robotics early on.[00:21:23] And so that sort of thing we're very, very interested. But the actual like robot interacting with the world is probably better for different team. Agree.[00:21:30] Alessio: Yeah, I'm curious who these teams are supposed to be that invest in them. I feel like everybody's like, yeah, robotics, it's important and like people should invest in it.[00:21:38] But then when you look at like the numbers, like the capital requirements early on versus like the moment of, okay, this is actually gonna work. Let's keep investing. That seems really hard to predict in a way that is not,[00:21:49] Martin Casado: I think co, CO two, kla, gc, I mean these are all invested in in Harvard companies. He just, you know, and [00:22:00] listen, I mean, it could work this time for sure.[00:22:01] Right? I mean if Elon's doing it, he's like, right. Just, just the fact that Elon's doing it means that there's gonna be a lot of capital and a lot of attempts for a long period of time. So that alone maybe suggests that we should just be investing in robotics just ‘cause you have this North star who's Elon with a humanoid and that's gonna like basically willing into being an industry.[00:22:17] Um, but we've just historically found like. We're a huge believer that this is gonna happen. We just don't feel like we're in a good position to diligence these things. ‘cause again, robotics companies tend to be vertical. You really have to understand the market they're being sold into. Like that's like that competitive equilibrium with a human being is what's important.[00:22:34] It's not like the core tech and like we're kind of more horizontal core tech type investors. And this is Sarah and I. Yeah, the ad team is different. They can actually do these types of things.[00:22:42] swyx: Uh, just to clarify, AD stands for[00:22:44] Martin Casado: American Dynamism.[00:22:45] swyx: Alright. Okay. Yeah, yeah, yeah. Uh, I actually, I do have a related question that, first of all, I wanna acknowledge also just on the, on the chip side.[00:22:51] Yeah. I, I recall a podcast that where you were on, i, I, I think it was the a CC podcast, uh, about two or three years ago where you, where you suddenly said [00:23:00] something, which really stuck in my head about how at some point, at some point kind of scale it makes sense to. Build a custom aic Yes. For per run.[00:23:07] Martin Casado: Yes.[00:23:07] It's crazy. Yeah.[00:23:09] swyx: We're here and I think you, you estimated 500 billion, uh, something.[00:23:12] Martin Casado: No, no, no. A billion, a billion dollar training run of $1 billion training run. It makes sense to actually do a custom meic if you can do it in time. The question now is timelines. Yeah, but not money because just, just, just rough math.[00:23:22] If it's a billion dollar training. Then the inference for that model has to be over a billion, otherwise it won't be solvent. So let's assume it's, if you could save 20%, which you could save much more than that with an ASIC 20%, that's $200 million. You can tape out a chip for $200 million. Right? So now you can literally like justify economically, not timeline wise.[00:23:41] That's a different issue. An ASIC per model, which[00:23:44] swyx: is because that, that's how much we leave on the table every single time. We, we, we do like generic Nvidia.[00:23:48] Martin Casado: Exactly. Exactly. No, it, it is actually much more than that. You could probably get, you know, a factor of two, which would be 500 million.[00:23:54] swyx: Typical MFU would be like 50.[00:23:55] Yeah, yeah. And that's good.[00:23:57] Martin Casado: Exactly. Yeah. Hundred[00:23:57] swyx: percent. Um, so, so, yeah, and I mean, and I [00:24:00] just wanna acknowledge like, here we are in, in, in 2025 and opening eyes confirming like Broadcom and all the other like custom silicon deals, which is incredible. I, I think that, uh, you know, speaking about ad there's, there's a really like interesting tie in that obviously you guys are hit on, which is like these sort, this sort of like America first movement or like sort of re industrialized here.[00:24:17] Yeah. Uh, move TSMC here, if that's possible. Um, how much overlap is there from ad[00:24:23] Martin Casado: Yeah.[00:24:23] swyx: To, I guess, growth and, uh, investing in particularly like, you know, US AI companies that are strongly bounded by their compute.[00:24:32] Martin Casado: Yeah. Yeah. So I mean, I, I would view, I would view AD as more as a market segmentation than like a mission, right?[00:24:37] So the market segmentation is, it has kind of regulatory compliance issues or government, you know, sale or it deals with like hardware. I mean, they're just set up to, to, to, to, to. To diligence those types of companies. So it's a more of a market segmentation thing. I would say the entire firm. You know, which has been since it is been intercepted, you know, has geographical biases, right?[00:24:58] I mean, for the longest time we're like, you [00:25:00] know, bay Area is gonna be like, great, where the majority of the dollars go. Yeah. And, and listen, there, there's actually a lot of compounding effects for having a geographic bias. Right. You know, everybody's in the same place. You've got an ecosystem, you're there, you've got presence, you've got a network.[00:25:12] Um, and, uh, I mean, I would say the Bay area's very much back. You know, like I, I remember during pre COVID, like it was like almost Crypto had kind of. Pulled startups away. Miami from the Bay Area. Miami, yeah. Yeah. New York was, you know, because it's so close to finance, came up like Los Angeles had a moment ‘cause it was so close to consumer, but now it's kind of come back here.[00:25:29] And so I would say, you know, we tend to be very Bay area focused historically, even though of course we've asked all over the world. And then I would say like, if you take the ring out, you know, one more, it's gonna be the US of course, because we know it very well. And then one more is gonna be getting us and its allies and Yeah.[00:25:44] And it goes from there.[00:25:45] Sarah Wang: Yeah,[00:25:45] Martin Casado: sorry.[00:25:46] Sarah Wang: No, no. I agree. I think from a, but I think from the intern that that's sort of like where the companies are headquartered. Maybe your questions on supply chain and customer base. Uh, I, I would say our customers are, are, our companies are fairly international from that perspective.[00:25:59] Like they're selling [00:26:00] globally, right? They have global supply chains in some cases.[00:26:03] Martin Casado: I would say also the stickiness is very different.[00:26:05] Sarah Wang: Yeah.[00:26:05] Martin Casado: Historically between venture and growth, like there's so much company building in venture, so much so like hiring the next PM. Introducing the customer, like all of that stuff.[00:26:15] Like of course we're just gonna be stronger where we have our network and we've been doing business for 20 years. I've been in the Bay Area for 25 years, so clearly I'm just more effective here than I would be somewhere else. Um, where I think, I think for some of the later stage rounds, the companies don't need that much help.[00:26:30] They're already kind of pretty mature historically, so like they can kind of be everywhere. So there's kind of less of that stickiness. This is different in the AI time. I mean, Sarah is now the, uh, chief of staff of like half the AI companies in, uh, in the Bay Area right now. She's like, ops Ninja Biz, Devrel, BizOps.[00:26:48] swyx: Are, are you, are you finding much AI automation in your work? Like what, what is your stack.[00:26:53] Sarah Wang: Oh my, in my personal stack.[00:26:54] swyx: I mean, because like, uh, by the way, it's the, the, the reason for this is it is triggering, uh, yeah. We, like, I'm hiring [00:27:00] ops, ops people. Um, a lot of ponders I know are also hiring ops people and I'm just, you know, it's opportunity Since you're, you're also like basically helping out with ops with a lot of companies.[00:27:09] What are people doing these days? Because it's still very manual as far as I can tell.[00:27:13] Sarah Wang: Hmm. Yeah. I think the things that we help with are pretty network based, um, in that. It's sort of like, Hey, how do do I shortcut this process? Well, let's connect you to the right person. So there's not quite an AI workflow for that.[00:27:26] I will say as a growth investor, Claude Cowork is pretty interesting. Yeah. Like for the first time, you can actually get one shot data analysis. Right. Which, you know, if you're gonna do a customer database, analyze a cohort retention, right? That's just stuff that you had to do by hand before. And our team, the other, it was like midnight and the three of us were playing with Claude Cowork.[00:27:47] We gave it a raw file. Boom. Perfectly accurate. We checked the numbers. It was amazing. That was my like, aha moment. That sounds so boring. But you know, that's, that's the kind of thing that a growth investor is like, [00:28:00] you know, slaving away on late at night. Um, done in a few seconds.[00:28:03] swyx: Yeah. You gotta wonder what the whole, like, philanthropic labs, which is like their new sort of products studio.[00:28:10] Yeah. What would that be worth as an independent, uh, startup? You know, like a[00:28:14] Martin Casado: lot.[00:28:14] Sarah Wang: Yeah, true.[00:28:16] swyx: Yeah. You[00:28:16] Martin Casado: gotta hand it to them. They've been executing incredibly well.[00:28:19] swyx: Yeah. I, I mean, to me, like, you know, philanthropic, like building on cloud code, I think, uh, it makes sense to me the, the real. Um, pedal to the metal, whatever the, the, the phrase is, is when they start coming after consumer with, uh, against OpenAI and like that is like red alert at Open ai.[00:28:35] Oh, I[00:28:35] Martin Casado: think they've been pretty clear. They're enterprise focused.[00:28:37] swyx: They have been, but like they've been free. Here's[00:28:40] Martin Casado: care publicly,[00:28:40] swyx: it's enterprise focused. It's coding. Right. Yeah.[00:28:43] AI Labs vs Startups: Disruption, Undercutting & the Innovator's Dilemma[00:28:43] swyx: And then, and, but here's cloud, cloud, cowork, and, and here's like, well, we, uh, they, apparently they're running Instagram ads for Claudia.[00:28:50] I, on, you know, for, for people on, I get them all the time. Right. And so, like,[00:28:54] Martin Casado: uh,[00:28:54] swyx: it, it's kind of like this, the disruption thing of, uh, you know. Mo Open has been doing, [00:29:00] consumer been doing the, just pursuing general intelligence in every mo modality, and here's a topic that only focus on this thing, but now they're sort of undercutting and doing the whole innovator's dilemma thing on like everything else.[00:29:11] Martin Casado: It's very[00:29:11] swyx: interesting.[00:29:12] Martin Casado: Yeah, I mean there's, there's a very open que so for me there's like, do you know that meme where there's like the guy in the path and there's like a path this way? There's a path this way. Like one which way Western man. Yeah. Yeah.[00:29:23] Two Futures for AI: Infinite Market vs AGI Oligopoly[00:29:23] Martin Casado: And for me, like, like all the entire industry kind of like hinges on like two potential futures.[00:29:29] So in, in one potential future, um, the market is infinitely large. There's perverse economies of scale. ‘cause as soon as you put a model out there, like it kind of sublimates and all the other models catch up and like, it's just like software's being rewritten and fractured all over the place and there's tons of upside and it just grows.[00:29:48] And then there's another path which is like, well. Maybe these models actually generalize really well, and all you have to do is train them with three times more money. That's all you have to [00:30:00] do, and it'll just consume everything beyond it. And if that's the case, like you end up with basically an oligopoly for everything, like, you know mm-hmm.[00:30:06] Because they're perfectly general and like, so this would be like the, the a GI path would be like, these are perfectly general. They can do everything. And this one is like, this is actually normal software. The universe is complicated. You've got, and nobody knows the answer.[00:30:18] The Economics Reality Check: Gross Margins, Training Costs & Borrowing Against the Future[00:30:18] Martin Casado: My belief is if you actually look at the numbers of these companies, so generally if you look at the numbers of these companies, if you look at like the amount they're making and how much they, they spent training the last model, they're gross margin positive.[00:30:30] You're like, oh, that's really working. But if you look at like. The current training that they're doing for the next model, their gross margin negative. So part of me thinks that a lot of ‘em are kind of borrowing against the future and that's gonna have to slow down. It's gonna catch up to them at some point in time, but we don't really know.[00:30:47] Sarah Wang: Yeah.[00:30:47] Martin Casado: Does that make sense? Like, I mean, it could be, it could be the case that the only reason this is working is ‘cause they can raise that next round and they can train that next model. ‘cause these models have such a short. Life. And so at some point in time, like, you know, they won't be able to [00:31:00] raise that next round for the next model and then things will kind of converge and fragment again.[00:31:03] But right now it's not.[00:31:04] Sarah Wang: Totally. I think the other, by the way, just, um, a meta point. I think the other lesson from the last three years is, and we talk about this all the time ‘cause we're on this. Twitter X bubble. Um, cool. But, you know, if you go back to, let's say March, 2024, that period, it felt like a, I think an open source model with an, like a, you know, benchmark leading capability was sort of launching on a daily basis at that point.[00:31:27] And, um, and so that, you know, that's one period. Suddenly it's sort of like open source takes over the world. There's gonna be a plethora. It's not an oligopoly, you know, if you fast, you know, if you, if you rewind time even before that GPT-4 was number one for. Nine months, 10 months. It's a long time. Right.[00:31:44] Um, and of course now we're in this era where it feels like an oligopoly, um, maybe some very steady state shifts and, and you know, it could look like this in the future too, but it just, it's so hard to call. And I think the thing that keeps, you know, us up at [00:32:00] night in, in a good way and bad way, is that the capability progress is actually not slowing down.[00:32:06] And so until that happens, right, like you don't know what's gonna look like.[00:32:09] Martin Casado: But I, I would, I would say for sure it's not converged, like for sure, like the systemic capital flows have not converged, meaning right now it's still borrowing against the future to subsidize growth currently, which you can do that for a period of time.[00:32:23] But, but you know, at the end, at some point the market will rationalize that and just nobody knows what that will look like.[00:32:29] Alessio: Yeah.[00:32:29] Martin Casado: Or, or like the drop in price of compute will, will, will save them. Who knows?[00:32:34] Alessio: Yeah. Yeah. I think the models need to ask them to, to specific tasks. You know? It's like, okay, now Opus 4.5 might be a GI at some specific task, and now you can like depreciate the model over a longer time.[00:32:45] I think now, now, right now there's like no old model.[00:32:47] Martin Casado: No, but let, but lemme just change that mental, that's, that used to be my mental model. Lemme just change it a little bit.[00:32:53] Capital as a Weapon vs Task Saturation: Where Real Enterprise Value Gets Built[00:32:53] Martin Casado: If you can raise three times, if you can raise more than the aggregate of anybody that uses your models, that doesn't even matter.[00:32:59] It doesn't [00:33:00] even matter. See what I'm saying? Like, yeah. Yeah. So, so I have an API Business. My API business is 60% margin, or 70% margin, or 80% margin is a high margin business. So I know what everybody is using. If I can raise more money than the aggregate of everybody that's using it, I will consume them whether I'm a GI or not.[00:33:14] And I will know if they're using it ‘cause they're using it. And like, unlike in the past where engineering stops me from doing that.[00:33:21] Alessio: Mm-hmm.[00:33:21] Martin Casado: It is very straightforward. You just train. So I also thought it was kind of like, you must ask the code a GI, general, general, general. But I think there's also just a possibility that the, that the capital markets will just give them the, the, the ammunition to just go after everybody on top of ‘em.[00:33:36] Sarah Wang: I, I do wonder though, to your point, um, if there's a certain task that. Getting marginally better isn't actually that much better. Like we've asked them to it, to, you know, we can call it a GI or whatever, you know, actually, Ali Goi talks about this, like we're already at a GI for a lot of functions in the enterprise.[00:33:50] Um. That's probably those for those tasks, you probably could build very specific companies that focus on just getting as much value out of that task that isn't [00:34:00] coming from the model itself. There's probably a rich enterprise business to be built there. I mean, could be wrong on that, but there's a lot of interesting examples.[00:34:08] So, right, if you're looking the legal profession or, or whatnot, and maybe that's not a great one ‘cause the models are getting better on that front too, but just something where it's a bit saturated, then the value comes from. Services. It comes from implementation, right? It comes from all these things that actually make it useful to the end customer.[00:34:24] Martin Casado: Sorry, what am I, one more thing I think is, is underused in all of this is like, to what extent every task is a GI complete.[00:34:31] Sarah Wang: Mm-hmm.[00:34:32] Martin Casado: Yeah. I code every day. It's so fun.[00:34:35] Sarah Wang: That's a core question. Yeah.[00:34:36] Martin Casado: And like. When I'm talking to these models, it's not just code. I mean, it's everything, right? Like I, you know, like it's,[00:34:43] swyx: it's healthcare.[00:34:44] It's,[00:34:44] Martin Casado: I mean, it's[00:34:44] swyx: Mele,[00:34:45] Martin Casado: but it's every, it is exactly that. Like, yeah, that's[00:34:47] Sarah Wang: great support. Yeah.[00:34:48] Martin Casado: It's everything. Like I'm asking these models to, yeah, to understand compliance. I'm asking these models to go search the web. I'm asking these models to talk about things I know in the history, like it's having a full conversation with me while I, I engineer, and so it could be [00:35:00] the case that like, mm-hmm.[00:35:01] The most a, you know, a GI complete, like I'm not an a GI guy. Like I think that's, you know, but like the most a GI complete model will is win independent of the task. And we don't know the answer to that one either.[00:35:11] swyx: Yeah.[00:35:12] Martin Casado: But it seems to me that like, listen, codex in my experience is for sure better than Opus 4.5 for coding.[00:35:18] Like it finds the hardest bugs that I work in with. Like, it is, you know. The smartest developers. I don't work on it. It's great. Um, but I think Opus 4.5 is actually very, it's got a great bedside manner and it really, and it, it really matters if you're building something very complex because like, it really, you know, like you're, you're, you're a partner and a brainstorming partner for somebody.[00:35:38] And I think we don't discuss enough how every task kind of has that quality.[00:35:42] swyx: Mm-hmm.[00:35:43] Martin Casado: And what does that mean to like capital investment and like frontier models and Submodels? Yeah.[00:35:47] Why “Coding Models” Keep Collapsing into Generalists (Reasoning vs Taste)[00:35:47] Martin Casado: Like what happened to all the special coding models? Like, none of ‘em worked right. So[00:35:51] Alessio: some of them, they didn't even get released.[00:35:53] Magical[00:35:54] Martin Casado: Devrel. There's a whole, there's a whole host. We saw a bunch of them and like there's this whole theory that like, there could be, and [00:36:00] I think one of the conclusions is, is like there's no such thing as a coding model,[00:36:04] Alessio: you know?[00:36:04] Martin Casado: Like, that's not a thing. Like you're talking to another human being and it's, it's good at coding, but like it's gotta be good at everything.[00:36:10] swyx: Uh, minor disagree only because I, I'm pretty like, have pretty high confidence that basically open eye will always release a GPT five and a GT five codex. Like that's the code's. Yeah. The way I call it is one for raisin, one for Tiz. Um, and, and then like someone internal open, it was like, yeah, that's a good way to frame it.[00:36:32] Martin Casado: That's so funny.[00:36:33] swyx: Uh, but maybe it, maybe it collapses down to reason and that's it. It's not like a hundred dimensions doesn't life. Yeah. It's two dimensions. Yeah, yeah, yeah, yeah. Like and exactly. Beside manner versus coding. Yeah.[00:36:43] Martin Casado: Yeah.[00:36:44] swyx: It's, yeah.[00:36:46] Martin Casado: I, I think for, for any, it's hilarious. For any, for anybody listening to this for, for, for, I mean, for you, like when, when you're like coding or using these models for something like that.[00:36:52] Like actually just like be aware of how much of the interaction has nothing to do with coding and it just turns out to be a large portion of it. And so like, you're, I [00:37:00] think like, like the best Soto ish model. You know, it is going to remain very important no matter what the task is.[00:37:06] swyx: Yeah.[00:37:07] What He's Actually Coding: Gaussian Splats, Spark.js & 3D Scene Rendering Demos[00:37:07] swyx: Uh, speaking of coding, uh, I, I'm gonna be cheeky and ask like, what actually are you coding?[00:37:11] Because obviously you, you could code anything and you are obviously a busy investor and a manager of the good. Giant team. Um, what are you calling?[00:37:18] Martin Casado: I help, um, uh, FEFA at World Labs. Uh, it's one of the investments and um, and they're building a foundation model that creates 3D scenes.[00:37:27] swyx: Yeah, we had it on the pod.[00:37:28] Yeah. Yeah,[00:37:28] Martin Casado: yeah. And so these 3D scenes are Gaussian splats, just by the way that kind of AI works. And so like, you can reconstruct a scene better with, with, with radiance feels than with meshes. ‘cause like they don't really have topology. So, so they, they, they produce each. Beautiful, you know, 3D rendered scenes that are Gaussian splats, but the actual industry support for Gaussian splats isn't great.[00:37:50] It's just never, you know, it's always been meshes and like, things like unreal use meshes. And so I work on a open source library called Spark js, which is a. Uh, [00:38:00] a JavaScript rendering layer ready for Gaussian splats. And it's just because, you know, um, you, you, you need that support and, and right now there's kind of a three js moment that's all meshes and so like, it's become kind of the default in three Js ecosystem.[00:38:13] As part of that to kind of exercise the library, I just build a whole bunch of cool demos. So if you see me on X, you see like all my demos and all the world building, but all of that is just to exercise this, this library that I work on. ‘cause it's actually a very tough algorithmics problem to actually scale a library that much.[00:38:29] And just so you know, this is ancient history now, but 30 years ago I paid for undergrad, you know, working on game engines in college in the late nineties. So I've got actually a back and it's very old background, but I actually have a background in this and so a lot of it's fun. You know, but, but the, the, the, the whole goal is just for this rendering library to, to,[00:38:47] Sarah Wang: are you one of the most active contributors?[00:38:49] The, their GitHub[00:38:50] Martin Casado: spark? Yes.[00:38:51] Sarah Wang: Yeah, yeah.[00:38:51] Martin Casado: There's only two of us there, so, yes. No, so by the way, so the, the pri The pri, yeah. Yeah. So the primary developer is a [00:39:00] guy named Andres Quist, who's an absolute genius. He and I did our, our PhDs together. And so like, um, we studied for constant Quas together. It was almost like hanging out with an old friend, you know?[00:39:09] And so like. So he, he's the core, core guy. I did mostly kind of, you know, the side I run venture fund.[00:39:14] swyx: It's amazing. Like five years ago you would not have done any of this. And it brought you back[00:39:19] Martin Casado: the act, the Activ energy, you're still back. Energy was so high because you had to learn all the framework b******t.[00:39:23] Man, I f*****g used to hate that. And so like, now I don't have to deal with that. I can like focus on the algorithmics so I can focus on the scaling and I,[00:39:29] swyx: yeah. Yeah.[00:39:29] LLMs vs Spatial Intelligence + How to Value World Labs' 3D Foundation Model[00:39:29] swyx: And then, uh, I'll observe one irony and then I'll ask a serious investor question, uh, which is like, the irony is FFE actually doesn't believe that LMS can lead us to spatial intelligence.[00:39:37] And here you are using LMS to like help like achieve spatial intelligence. I just see, I see some like disconnect in there.[00:39:45] Martin Casado: Yeah. Yeah. So I think, I think, you know, I think, I think what she would say is LLMs are great to help with coding.[00:39:51] swyx: Yes.[00:39:51] Martin Casado: But like, that's very different than a model that actually like provides, they, they'll never have the[00:39:56] swyx: spatial inte[00:39:56] Martin Casado: issues.[00:39:56] And listen, our brains clearly listen, our brains, brains clearly have [00:40:00] both our, our brains clearly have a language reasoning section and they clearly have a spatial reasoning section. I mean, it's just, you know, these are two pretty independent problems.[00:40:07] swyx: Okay. And you, you, like, I, I would say that the, the one data point I recently had, uh, against it is the DeepMind, uh, IMO Gold, where, so, uh, typically the, the typical answer is that this is where you start going down the neuros symbolic path, right?[00:40:21] Like one, uh, sort of very sort of abstract reasoning thing and one form, formal thing. Um, and that's what. DeepMind had in 2024 with alpha proof, alpha geometry, and now they just use deep think and just extended thinking tokens. And it's one model and it's, and it's in LM.[00:40:36] Martin Casado: Yeah, yeah, yeah, yeah, yeah.[00:40:37] swyx: And so that, that was my indication of like, maybe you don't need a separate system.[00:40:42] Martin Casado: Yeah. So, so let me step back. I mean, at the end of the day, at the end of the day, these things are like nodes in a graph with weights on them. Right. You know, like it can be modeled like if you, if you distill it down. But let me just talk about the two different substrates. Let's, let me put you in a dark room.[00:40:56] Like totally black room. And then let me just [00:41:00] describe how you exit it. Like to your left, there's a table like duck below this thing, right? I mean like the chances that you're gonna like not run into something are very low. Now let me like turn on the light and you actually see, and you can do distance and you know how far something away is and like where it is or whatever.[00:41:17] Then you can do it, right? Like language is not the right primitives to describe. The universe because it's not exact enough. So that's all Faye, Faye is talking about. When it comes to like spatial reasoning, it's like you actually have to know that this is three feet far, like that far away. It is curved.[00:41:37] You have to understand, you know, the, like the actual movement through space.[00:41:40] swyx: Yeah.[00:41:40] Martin Casado: So I do, I listen, I do think at the end of these models are definitely converging as far as models, but there's, there's, there's different representations of problems you're solving. One is language. Which, you know, that would be like describing to somebody like what to do.[00:41:51] And the other one is actually just showing them and the space reasoning is just showing them.[00:41:55] swyx: Yeah, yeah, yeah. Right. Got it, got it. Uh, the, in the investor question was on, on, well labs [00:42:00] is, well, like, how do I value something like this? What, what, what work does the, do you do? I'm just like, Fefe is awesome.[00:42:07] Justin's awesome. And you know, the other two co-founder, co-founders, but like the, the, the tech, everyone's building cool tech. But like, what's the value of the tech? And this is the fundamental question[00:42:16] Martin Casado: of, well, let, let, just like these, let me just maybe give you a rough sketch on the diffusion models. I actually love to hear Sarah because I'm a venture for, you know, so like, ventures always, always like kind of wild west type[00:42:24] swyx: stuff.[00:42:24] You, you, you, you paid a dream and she has to like, actually[00:42:28] Martin Casado: I'm gonna say I'm gonna mar to reality, so I'm gonna say the venture for you. And she can be like, okay, you a little kid. Yeah. So like, so, so these diffusion models literally. Create something for, for almost nothing. And something that the, the world has found to be very valuable in the past, in our real markets, right?[00:42:45] Like, like a 2D image. I mean, that's been an entire market. People value them. It takes a human being a long time to create it, right? I mean, to create a, you know, a, to turn me into a whatever, like an image would cost a hundred bucks in an hour. The inference cost [00:43:00] us a hundredth of a penny, right? So we've seen this with speech in very successful companies.[00:43:03] We've seen this with 2D image. We've seen this with movies. Right? Now, think about 3D scene. I mean, I mean, when's Grand Theft Auto coming out? It's been six, what? It's been 10 years. I mean, how, how like, but hasn't been 10 years.[00:43:14] Alessio: Yeah.[00:43:15] Martin Casado: How much would it cost to like, to reproduce this room in 3D? Right. If you, if you, if you hired somebody on fiber, like in, in any sort of quality, probably 4,000 to $10,000.[00:43:24] And then if you had a professional, probably $30,000. So if you could generate the exact same thing from a 2D image, and we know that these are used and they're using Unreal and they're using Blend, or they're using movies and they're using video games and they're using all. So if you could do that for.[00:43:36] You know, less than a dollar, that's four or five orders of magnitude cheaper. So you're bringing the marginal cost of something that's useful down by three orders of magnitude, which historically have created very large companies. So that would be like the venture kind of strategic dreaming map.[00:43:49] swyx: Yeah.[00:43:50] And, and for listeners, uh, you can do this yourself on your, on your own phone with like. Uh, the marble.[00:43:55] Martin Casado: Yeah. Marble.[00:43:55] swyx: Uh, or but also there's many Nerf apps where you just go on your iPhone and, and do this.[00:43:59] Martin Casado: Yeah. Yeah. [00:44:00] Yeah. And, and in the case of marble though, it would, what you do is you literally give it in.[00:44:03] So most Nerf apps you like kind of run around and take a whole bunch of pictures and then you kind of reconstruct it.[00:44:08] swyx: Yeah.[00:44:08] Martin Casado: Um, things like marble, just that the whole generative 3D space will just take a 2D image and it'll reconstruct all the like, like[00:44:16] swyx: meaning it has to fill in. Uh,[00:44:18] Martin Casado: stuff at the back of the table, under the table, the back, like, like the images, it doesn't see.[00:44:22] So the generator stuff is very different than reconstruction that it fills in the things that you can't see.[00:44:26] swyx: Yeah. Okay.[00:44:26] Sarah Wang: So,[00:44:27] Martin Casado: all right. So now the,[00:44:28] Sarah Wang: no, no. I mean I love that[00:44:29] Martin Casado: the adult[00:44:29] Sarah Wang: perspective. Um, well, no, I was gonna say these are very much a tag team. So we, we started this pod with that, um, premise. And I think this is a perfect question to even build on that further.[00:44:36] ‘cause it truly is, I mean, we're tag teaming all of these together.[00:44:39] Investing in Model Labs, Media Rumors, and the Cursor Playbook (Margins & Going Down-Stack)[00:44:39] Sarah Wang: Um, but I think every investment fundamentally starts with the same. Maybe the same two premises. One is, at this point in time, we actually believe that there are. And of one founders for their particular craft, and they have to be demonstrated in their prior careers, right?[00:44:56] So, uh, we're not investing in every, you know, now the term is NEO [00:45:00] lab, but every foundation model, uh, any, any company, any founder trying to build a foundation model, we're not, um, contrary to popular opinion, we're

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

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

Payments leaders are feeling the squeeze of shrinking margins, price-driven churn, and rising expectations from merchants who want funding that feels as seamless as a card transaction. We sat down with Aarati Soman, Head of Product at Parafin, and Jaron Ruckman, Product Manager at NMI, to map the new playbook: embedded lending that meets merchants where they already work, backed by real-time data, AI-driven underwriting, and modular infrastructure that launches fast and scales cleanly.We unpack how moving capital inside your existing workflows changes the relationship with your merchants. Instead of sending them to third-party portals or closed ecosystems, you present pre-underwritten offers based on sales data, bank transactions, and relevant third-party signals. Machine learning models spot revenue patterns, seasonality, refunds, disputes, and expense profiles; LLMs structure unstructured data to speed decisions. The impact is tangible: faster approvals, fairer pricing, higher eligibility for SMBs that banks often overlook, and the kind of stickiness that turns payment processing from a commodity into a growth engine.Aarati outlines how Parafin carries the heavy lifts - capital, risk, servicing, and compliance so partners can focus on distribution and experience. Jaron shares how NMI's API-first approach and embeddable components get partners live with offers before any deep development, with the option to integrate more tightly over time. We explore strategic positioning against Stripe and Square, why contextual placement at the point of pain drives adoption, and where product innovation is headed: fit-for-purpose capital for inventory spikes, equipment, payroll, and beyond. We close with practical advice on choosing partners - breadth of products, ease of integration, transparency, and program durability so you avoid costly rip-and-replace cycles and deliver fast funding your merchants trust.

I've been delaying this episode for a long time because the topic is genuinely difficult and, for many of us, scary. AI is threatening not just to our livelihood, but to our sense of self-worth as creators.In this episode, I don't offer false guarantees about job security. Instead, I frame the problem through the lens of microeconomics and rational incentives to help you understand how to remain employable. We discuss why you must separate your ego from your current skill set and how to position yourself not as a competitor to AI, but as a force multiplier.• The Hard Truth: I explain why the "abstinence" approach—hoping the industry rejects AI or that it turns out to be a bubble—is a high-risk gamble that is unlikely to succeed.• Ego vs. Employability: We discuss the difficult mental shift required to disconnect your self-worth from the act of writing code manually, allowing you to adopt new tools without feeling like you are losing your identity.• The Microeconomics of Your Job: Understand the cold reality that a rational market only pays you if you generate more value than you cost; if AI can do the same task with less risk or cost, the market will choose AI.• The Non-Zero Sum Game: Learn why the economy isn't a fixed pie. The goal isn't just to survive, but to recognize that the combination of Human + AI can generate more total value than either can alone.• Multiplicative Value: I challenge you to stop thinking about linear skill acquisition and start thinking like a manager: how can you use AI to multiply your output and become indispensable?• Accepting Atrophy: We confront the reality that your core coding skills may degrade over time as you rely on AI, and why accepting this trade-off might be necessary for your career survival.

Yeah, you prolly saw the news: OpenAI acquihired OpenClaw.

AI tools are becoming a core part of modern development workflows—but they come with serious risks most developers aren't thinking about. In this episode, Matt and Mike break down five AI security threats that are already happening in the real world. From prompt injection attacks and rogue AI agents with access to your email, to runaway API bills and poisoned models slipping into your stack - these aren't hypothetical problems. If you're using AI in production, in your codebase, or inside your company workflows, this episode will help you understand what can go wrong - and how to protect yourself before it does. ‍Show Notes: https://www.htmlallthethings.com/podcast/5-ways-ai-can-blow-up-in-your-face

Google has confirmed that state-backed threat actors are operationally using Gemini across the intrusion lifecycle — not experimentally, but strategically. In this episode of Security Squawk, we break down how AI is being integrated into reconnaissance, phishing refinement, vulnerability research, and even dynamic malware generation. According to Google's Threat Intelligence Group, multiple clusters — including DPRK-linked actors — are using Gemini to synthesize OSINT, map organizational structures, refine recruiter impersonation campaigns, and research exploit paths. In one case, malware known as HONESTCUE leveraged Gemini's API to dynamically generate C# code for stage-two payload behavior, compile it in memory using legitimate .NET tooling, and execute filelessly. This isn't a zero-day story. It's a friction story. At the same time, two individuals in Connecticut were charged for allegedly using thousands of stolen identities to exploit FanDuel's onboarding and promotional systems. No exotic exploit. No advanced intrusion chain. Just automated workflow abuse at scale. The pattern is clear: AI is compressing attacker timelines, and identity-driven fraud is industrializing predictable processes. We examine: How AI-enhanced phishing eliminates traditional grammar-based red flags Why trusted SaaS domains (Gemini share links, Discord CDNs, Cloudflare fronting, Supabase backends) are weakening reputation-based defenses What model distillation attempts (100,000+ structured prompts) signal about API abuse and intellectual property risk How fileless malware compiled with legitimate developer tooling challenges signature-based detection Why onboarding workflows and recruiting processes are now primary attack surfaces For CEOs, this is about erosion of trust anchors and shifting insurability expectations. For IT Directors and SOC leaders, this means reevaluating fileless execution visibility, API anomaly detection, and the reliability of reputation filtering models. For MSPs and risk managers, breaches will increasingly originate from workflow exploitation rather than perimeter misconfiguration. AI didn't invent new attack types. It removed friction from existing ones. And when friction disappears, scale compounds. If your recruiting, onboarding, verification, or AI product interfaces can be scripted — they can be weaponized. This episode is about operational clarity in a rapidly compressing threat landscape. Keywords: Google Gemini, HONESTCUE malware, AI phishing, state-backed threat actors, DPRK cyber operations, model distillation attacks, API abuse detection, fileless malware, .NET in-memory compilation, identity fraud, FanDuel fraud case, workflow exploitation, SaaS infrastructure abuse, Cloudflare phishing, Discord CDN payloads, Supabase backend abuse. Support the show https://buymeacoffee.com/securitysquawk

Harvey and Ed speak to Anthemos Georgiades, the co-founder and CEO of Zumper, a leading rental marketplace in the US and Canada. They discuss Zumper's journey, the competitive landscape of the rental market, and the challenges of acquiring inventory. Anthemos shares insights into Zumper's business model, which primarily focuses on B2B revenue, and the impact of AI and technology on the real estate industry. The conversation also touches on Zumper's decision to build an app for ChatGPT.Chapters00:00 Introduction to Zumper and Its Journey06:34 Understanding the Competitive Landscape10:21 Revenue and Inventory Dynamics12:37 Challenges of Diverse Rental Listings14:18 Exploring Ancillary Revenue Streams17:14 Monetizing the Consumer Experience19:07 Competing with Giants: Zillow and CoStar21:37 Embracing AI and Defining Risk22:52 The Future of Online Marketplaces28:17 API Quality and Real-Time Data31:23 Building for ChatGPT: Challenges and Opportunities38:54 Anticipating the Next Big Shift in AI Search43:52 The Dual Future of Marketplaces: Front End and API

In this episode of The Cybersecurity Defenders Podcast, we discuss some intel being shared in the LimaCharlie community.Russian cyber operations have maintained a consistent focus on exploiting both tactical and strategic targets within the defense industrial base, particularly in the context of the war in Ukraine.Sygnia has disclosed a large-scale, AI-driven scam operation involving over 150 cloned websites impersonating law firms.A joint investigation by SentinelLabs and Censys has revealed a growing ecosystem of publicly exposed AI compute infrastructure, driven largely by deployments of Ollama - an open-source framework for running large language models locally.Flare has identified a widespread, ongoing campaign attributed to a threat actor group known as TeamPCP -also operating under aliases such as PCPcat and ShellForce - which has compromised over 60,000 servers worldwide since late December.Support our show by sharing your favorite episodes with a friend, subscribe, give us a rating or leave a comment on your podcast platform.This podcast is brought to you by LimaCharlie, maker of the SecOps Cloud Platform, infrastructure for SecOps where everything is built API first. Scale with confidence as your business grows. Start today for free at limacharlie.io.

We went in depth with Marsha Barnhart on a high strangeness case that was big for us, case 12-058. The events in this case largely took place in Ocean City, Maryland. We play several audio clips from the primary witness and those close to him that had not previously been made public. Contact us about becoming a panelist: https://aerial-phenomenon.org/contact-us/ Become an API investigator: https://aerial-phenomenon.org/join-api/ Our new hotline number: https://aerial-phenomenon.org/weve-updated-our-hotline-number-again/ Report your UFO sighting: https://reportaufo.org

Photo by Viktor Keri on Unsplash Published 16 February 2026 e543 with Andy, Michael and Michael – Stories and discussion on Agentic AI and the changing nature of work, agents renting humans, real time translation, artistic roads, e-bikes for your feet and a whole lot more. Andy, Michael and Michael get things rolling with several AI articles.  First up, is a Mastodon post by Alan Pringle that called attention to a HBR article on the influence of AI on productivity.  This then led to a post on productivity acceleration technologies from years past – from COBOL, which was designed to enable business people to write programs, to 4GLs to case tools.  Then, the team discusses a detailed post from Matt Shumer entitled Something Big Is Happening.  The entire post is well worth reading, not only for how history is unfolding in real time, also for the recommendations that Matt makes for people to take onboard right now.  Among the recommendations are to begin the habit of adapting, and experimenting with multiple tools to build resiliency and experience. Wrapping up this section is a new version of taskrabbit that provides an API for Agents to rent humans for specific work called rentahuman.ai .  The future is certainly coming in fast. In the AR VR section, there is a story from Tom's Guide where the author used her Ray Ban Meta glasses to translate the Super Bowl halftime video in real time.  This feels like the precursor to the next logical step, a dynamic version of the Amazon X-Ray feature where further context can be personalized and served up to the user if they wish. After touching on the assembly of Game Poems and the art of roads in games, the team sprints to the end of the episode with Nike's Project Amplify, which is an ankle exoskeleton to augment humans running abilities.  Looping back to the start of the episode, Andy highlights a BBC show called Chris McCausland. What's been your experience with AI productivity?  What are you experimenting with? Have your bots

Connect to John Gilroy on LinkedIn   https://www.linkedin.com/in/john-gilroy/ Want to listen to other episodes? www.Federaltechpodcast.com Cybersecurity is a rapidly evolving field, where every effective defense technique is quickly noticed and adapted to by malicious actors. The real question is how fast each side of this ongoing cat-and-mouse game can respond. Let us take an example of web applications. In the decade-long slog of the cloud, federal users migrated to web-based applications protected by Web Application Firewalls (WAFs). firewalls. As that method matured, malicious observers noted that the Application Programming Interface (API) allowed these software programs to communicate and exchange data. Voila, another attack vector was born. During today's interview, Joe Henry from Akamai Technologies notes that 80% of their customers report API attacks. Henry details a curious term called "Broken-Object Level Authorization." In this attack, an application fails to check if a user is authorized to access specific data objects. The ID is manipulated, and the malicious actor gets access. Akamai's API Security performs behavioral analysis beyond WAFs, flags PII exposure, and supports a zero-trust posture. Software developers talk about a "shift left"; we apply that to the Akamai approach. They have a worldwide network of Points of Presence (POPs) and data centers where they can observe attacks as they develop. It is so strong that it provides fail-open resilience with a 100% SLA. Akamai provides a State of the Internet Report (quarterly). If you would like to stay connected with the next manifestation of attack, consider subscribing or visiting their website to stay informed about the latest trend

This week Brandon Min, Founder and CEO of Herd Security, joins Defender Fridays to discuss how human risk management needs to rebrand with empathy.Brandon is the co-founder and CEO of Herd Security, where they help security teams drive employee engagement in security, making a more resilient organization. Humans have been the #1 target of organizational cyber attacks; however, security teams, organizations, vendors, and leaders have vilified them. At Herd, they believe security should be led with empathy and care. Building trust amongst users that will drive their engagement in security. Building herd immunity from cyber attacks. Learn more at https://herdsecurity.io/Register for Live SessionsJoin us every Friday at 10:30am PT for live, interactive discussions with industry experts. Whether you're a seasoned professional or just curious about the field, these sessions offer an engaging dialogue between our guests, hosts, and you – our audience.Register here: https://limacharlie.io/defender-fridaysSubscribe to our YouTube channel and hit the notification bell to never miss a live session or catch up on past episodes!Sponsored by LimaCharlieThis episode is brought to you by LimaCharlie, a cloud-native SecOps platform where AI agents operate security infrastructure directly. Founded in 2018, LimaCharlie provides complete API coverage across detection, response, automation, and telemetry, with multi-tenant architecture designed for MSSPs and MDR providers managing thousands of unique client environments.Why LimaCharlie?Transparency: Complete visibility into every action and decision. No black boxes, no vendor lock-in.Scalability: Security operations that scale like infrastructure, not like procurement cycles. Move at cloud speed.Unopinionated Design: Integrate the tools you need, not just those contracts allow. Build security on your terms.Agentic SecOps Workspace (ASW): AI agents that operate alongside your team with observable, auditable actions through the same APIs human analysts use.Security Primitives: Composable building blocks that endure as tools come and go. Build once, evolve continuously.Try the Agentic SecOps Workspace free: https://limacharlie.ioLearn more: https://docs.limacharlie.ioFollow LimaCharlieSign up for free: https://limacharlie.ioLinkedIn: / limacharlieio X: https://x.com/limacharlieioCommunity Discourse: https://community.limacharlie.com/Host: Maxime Lamothe-Brassard - CEO / Co-founder at LimaCharlie

Sherwin Wu leads engineering for OpenAI's API platform, where roughly 95% of engineers use Codex, often working with fleets of 10 to 20 parallel AI agents.We discuss:1. What OpenAI did to cut code review times from 10-15 minutes to 2-3 minutes2. How AI is changing the role of managers3. Why the productivity gap between AI power users and everyone else is widening4. Why “models will eat your scaffolding for breakfast”5. Why the next 12 to 24 months are a rare window where engineers can leap ahead before the role fully transforms—Brought to you by:DX—The developer intelligence platform designed by leading researchersSentry—Code breaks, fix it fasterDatadog—Now home to Eppo, the leading experimentation and feature flagging platform—Episode transcript: https://www.lennysnewsletter.com/p/engineers-are-becoming-sorcerers—Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Sherwin Wu:• X: https://x.com/sherwinwu• LinkedIn: https://www.linkedin.com/in/sherwinwu1—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 Sherwin Wu(03:10) AI's role in coding at OpenAI(06:53) The future of software engineering with AI(12:26) The stress of managing agents(15:07) Codex and code review automation(19:29) The changing role of engineering managers(24:14) The one-person billion-dollar startup(31:40) Management lessons(37:28) Challenges and best practices in AI deployment(43:56) Hot takes on AI and customer feedback(48:57) Building for future AI capabilities(50:16) Where models are headed in the next 18 months(53:35) Business process automation(57:22) OpenAI's ecosystem and platform strategy(01:00:50) OpenAI's mission and global impact(01:05:21) Building on OpenAI's API and tools(01:08:16) Lightning round and final thoughts—Referenced:• Codex: https://openai.com/codex• OpenAI's CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil (CPO at OpenAI, ex-Instagram, Twitter): https://www.lennysnewsletter.com/p/kevin-weil-open-ai• OpenClaw: https://openclaw.ai• The creator of Clawd: “I ship code I don't read”: https://newsletter.pragmaticengineer.com/p/the-creator-of-clawd-i-ship-code• The Sorcerer's Apprentice: https://en.wikipedia.org/wiki/The_Sorcerer%27s_Apprentice_(Dukas)• Quora: https://www.quora.com• Marc Andreessen: The real AI boom hasn't even started yet: https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom• Sarah Friar on LinkedIn: https://www.linkedin.com/in/sarah-friar• Sam Altman on X: https://x.com/sama• Nicolas Bustamante's “LLMs Eat Scaffolding for Breakfast” post on X: https://x.com/nicbstme/status/2015795605524901957• The Bitter Lesson: http://www.incompleteideas.net/IncIdeas/BitterLesson.html• Overton window: https://en.wikipedia.org/wiki/Overton_window• Developers can now submit apps to ChatGPT: https://openai.com/index/developers-can-now-submit-apps-to-chatgpt• Responses: https://platform.openai.com/docs/api-reference/responses• Agents SDK: https://platform.openai.com/docs/guides/agents-sdk• AgentKit: https://openai.com/index/introducing-agentkit• Ubiquiti: https://ui.com• Jujutsu Kaisen on Crunchyroll: https://www.crunchyroll.com/series/GRDV0019R/jujutsu-kaisen?srsltid=AfmBOoqvfzKQ6SZOgzyJwNQ43eceaJTQA2nUxTQfjA1Ko4OxlpUoBNRB• eero: https://eero.com• Opendoor: https://www.opendoor.com—Recommended books:• Structure and Interpretation of Computer Programs: https://www.amazon.com/Structure-Interpretation-Computer-Programs-Engineering/dp/0262510871• The Mythical Man-Month: Essays on Software Engineering: https://www.amazon.com/Mythical-Man-Month-Software-Engineering-Anniversary/dp/0201835959• There Is No Antimemetics Division: A Novel: https://www.amazon.com/There-No-Antimemetics-Division-Novel/dp/0593983750• Breakneck: China's Quest to Engineer the Future: https://www.amazon.com/Breakneck-Chinas-Quest-Engineer-Future/dp/1324106034• Apple in China: The Capture of the World's Greatest Company: https://www.amazon.com/Apple-China-Capture-Greatest-Company/dp/1668053373—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

Prabhleen Kaur: When Team Members Raise Concerns with Clarity, Not Anger Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.   "My idea of success as a Scrum Master is when you look around, you see motivated people, and when something goes wrong, they come to you not in anger, but with concern." - Prabhleen Kaur   Prabhleen offers a refreshing perspective on measuring success as a Scrum Master that goes beyond velocity charts and feature counts. She shares a pivotal moment when her team was in production, delivering relentlessly with barely any time to breathe. A team member approached her—not with frustration or blame—but with thoughtful concern: "This is not going to work out." He sat down with Prabhleen and the Product Owner, explaining that as the middle layer in an API creation team, delays from upstream were creating a cascading problem.  What struck Prabhleen wasn't just the identification of the issue, but how he approached it: with options to discuss, not demands to make. This moment crystallized her definition of success. When team members feel safe enough to voice concerns early, when they come with ideas rather than accusations, when they see themselves as part of the solution rather than victims of circumstances—that's when a Scrum Master has truly succeeded.  Prabhleen reminds us that while stakeholders may focus on features delivered, Scrum Masters should watch how well the team responds to change. That adaptability, rooted in psychological safety and mutual trust, is the true measure of a team's maturity.   Self-reflection Question: When problems emerge in your team, do people approach you with defensive anger or constructive concern? What does that tell you about the psychological safety you've helped create? Featured Retrospective Format for the Week: Keep-Stop-Happy-Gratitude Prabhleen shares her favorite retrospective format, born from necessity when she joined an established team with dismal participation in their standard three-column retrospectives. She transformed it into a four-column approach: (1) What should we keep doing, (2) What should we stop doing, (3) One thing that will make you happy, and (4) Gratitude for the team. The third column—asking what would make team members happy—opened unexpected doors. Suggestions ranged from team outings to skipping Friday stand-ups, giving Prabhleen real-time insights into team needs without waiting for formal working agreement sessions. The gratitude column proved even more powerful. "Appreciation brings a space where trust is automatically built. When every 15 days you're sitting with the team making a point to say thank you to each other for all the work you've done, everybody feels mutually respected," Prabhleen explains. This ties directly to the trust-building discussed in Tuesday's episode—using retrospectives not just to improve processes, but to strengthen the human connections that make teams resilient.   [The Scrum Master Toolbox Podcast Recommends]

Do you remember the early days of your career? You likely spent hours coding late into the night, fueled not by a paycheck, but by the sheer joy of building. But somewhere along the way, that intrinsic fire faded, replaced by the extrinsic motivators of Jira tickets, performance reviews, and ultimately the almighty dollar.In this episode of the Career Growth Accelerator, I explore why this shift happens and how it might be the very thing keeping you stuck. We discuss the "Overjustification Effect"—how getting paid for your passion can actually degrade your performance—and how to reclaim the autotelic personality required to enter a flow state and accelerate your career.• The Overjustification Effect: Learn why introducing extrinsic rewards (like a salary) for a task you inherently enjoy can weaken or completely replace your intrinsic motivation, eventually making the work feel like a chore.• The Loss of Flow: Discover how moving from hobbyist to professional changes your relationship with the work, often stripping away the conditions necessary for "flow state," such as risk-taking and immediate feedback.• Autotelic Personality: Understand the concept of being "autotelic"—doing something for its own sake—and why this trait is critical for high-quality, creative work that pushes your career forward.• The Stagnation Trap: Recognize that if your only motivation is doing what is required to get paid, you are unlikely to take on the voluntary challenges necessary to grow to the next level.• Reclaiming Your Drive: I discuss how finding pockets of intrinsic motivation—even if they are ancillary to your main job—can reignite your ability to enter flow, improve your work quality, and break through career plateaus.