Podcasts about MCP

In this episode, Sam talks with Dev Rishi, GM of AI at Rubrik, about what happens when agents move beyond answering questions and start taking action across tools, systems, and business processes. We explore why the enterprise playbook of static guardrails plus human approval starts to break down in the agent era. Agents are useful because they can plan, call tools, update systems, write code, send messages, and operate across workflows at machine speed, but those same capabilities make them difficult to govern with rules written in advance or approval prompts reviewed one at a time. Dev explains why tool access increases blast radius, why agents can route around controls in surprising ways, and why human-in-the-loop review can become security theater when agents operate at scale. We also discuss what enterprises need instead: better visibility, runtime enforcement, policy-aware governance, agent observability, and recovery mechanisms for when something goes wrong. Along the way, we dig into MCP and tool sprawl, small language models for policy enforcement, defense in depth, agent rewind, and why AI may be needed to help secure AI.

The future of land investing isn't coming; it's already here, and it's creating a bigger gap between investors every day.(Show Notes)The land investors pulling ahead today aren't necessarily smarter or working harder. They're using automation, AI agents, CRM workflows, and property data tools to eliminate busywork, respond faster, and make better decisions.I'll walk through the specific capabilities your CRM and operating system should have, including AI call handling, automated follow-up systems, call summaries, direct mail tracking, e-signatures, API integrations, and agentic AI tools like Claude that can actually perform tasks for you.Whether you use Stride CRM, Land Portal, or something else entirely, the goal is the same: give your time and mental bandwidth back while building a more scalable land investing business.

This week we get asked anything and everything from MCP to personal questions and we don't hold back and answer all questions asked of us. An episode unlike any we have done in the past.If you are in the US, shop at:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://gamechefs.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to help support the guild and use code: GamersGuild to save an additional 15% on your order!  If you would like to further support the channel go here to find out more: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/Thegamersguild⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Please join us on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Discord⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠! ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Or find us on Facebook here.⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

Naseem Al-Naji is the co-founder of MCPcat.io and the creator of Opal — a builder with deep roots in privacy-first developer tooling. In this conversation, he breaks down why MCP servers have become a black box in production, and how MCPcat gives teams X-ray vision into how agents and users actually behave.What we get into:

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Meher Patel is a serial entrepreneur with exits across hospitality, healthcare, and digital media — each in a completely different industry, each built from the ground up. He founded Neon Digital, a performance-first advertising agency, and then built what very few agencies ever achieve: a SaaS platform that outgrew the agency itself. Hector AI now processes over $350 million in ad spend across Amazon and marketplace advertising, with 1,000+ users on the platform — and in under 18 months, has earned 3 global recognitions including the Amazon Ads Innovation Award, the Amazon Partner Award, and a Top 20 Global Amazon Ads Advanced Partner ranking. Today, Meher is building what he believes will become the foundational intelligence layer of the agentic ecommerce era — Hector MCP: the most advanced, context-rich, token-optimized model context protocol purpose-built for Amazon advertising, designed so that every serious AI agent, every autonomous workflow, and every future-ready brand that wants to win on Amazon will have no choice but to be powered by it.Highlight Bullets> Here's a glimpse of what you would learn…. The rapid evolution of Amazon's advertising features driven by AI technology.Limitations of current SaaS platforms for Amazon sellers and the potential of MCP (Model Context Protocol) technology.The significance of context in AI-driven advertising optimization.Challenges associated with using raw data without contextual understanding in advertising.Practical strategies for Amazon sellers to optimize their ad campaigns.The importance of documenting ad optimization processes for effective AI integration.The role of custom AI workflows in enhancing advertising strategies.The necessity of continuous refinement and learning in building effective AI agents.The decision-making process for sellers regarding whether to rent AI tools or develop their own solutions.The use of connectors like Make.com and Knit for creating automated workflows with AI integration.In this episode of the Ecomm Breakthrough Podcast, host Josh Hadley speaks with Meher Patel, founder of Neon Digital and Hector AI, about the future of Amazon advertising. Meher explains how AI and MCP (Model Context Protocol) technology are transforming ad optimization by providing crucial context to raw Amazon data. He emphasizes that sellers should document their ad processes, learn to communicate effectively with AI, and decide whether to build custom AI workflows or use existing tools. The key takeaway: success with AI-driven advertising requires continuous refinement and treating AI as a knowledgeable, context-aware team member.Here are the 3 action items that Josh identified from this episode:Turn your workflow into SOPs Record how you optimize campaigns, explain your decisions, and convert that into SOPs—this becomes the foundation for training AI agents. Never feed AI raw data without context Structure and enrich your Amazon data first (or use MCP-powered tools) so AI can generate accurate, actionable insights. Start small with AI automation, then scale Begin with simple rules (e.g., budget increases for winning campaigns), then gradually build more advanced, custom workflows as you learn.Timestamps:00:00:58 Introduction to the Future of Amazon AdsThe host introduces the topic: autonomous, AI-powered decision-making for Amazon advertising, moving beyond simple optimization.00:01:13 Guest Introduction: Meher PatelThe host introduces Meher Patel, detailing his entrepreneurial background, his agency Neon Digital, and his SaaS platform, Hector AI.00:02:49 The Problem with Early AI Ad ToolsDiscussion on how early AI advertising tools often failed sellers, contrasting with the positive results from newer, more advanced software.00:04:10 Prediction for Amazon AdvertisingMeher predicts Amazon will rapidly release new AI-powered features, but sellers must learn how to properly utilize this infrastructure.00:08:46 The Importance of Context in AIAI is only as good as the context it's given; without it, AI recommendations are generic and potentially harmful.00:10:04 How Smart Sellers Should Prepare for AISellers must learn to ask the right questions and feed AI the right data with the proper context to get valuable results.00:12:07 Why Raw Data Isn't EnoughUploading raw Amazon reports to an AI lacks the necessary context, leading to "garbage out" optimization strategies.00:12:42 The Role of an MCP (Model Context Protocol)An MCP provides the necessary context and data connections, acting as an intelligent layer between raw data and the AI model.00:18:57 Amazon's MCP API LimitationsAmazon's own MCP is just an API, requiring sellers to build their own infrastructure, which is inefficient and token-heavy.00:21:48 Top Strategies: Building Custom AI AgentsThe best strategy is for brands to build their own custom AI agents and workflows based on their unique strategies.00:24:32 Unlocking Custom Workflows with AI AgentsAI agent workflows allow sellers to build bespoke optimization systems, unlike one-size-fits-all SaaS platforms.00:27:10 How to Create an AI Agent WorkflowRecord your optimization process, use an LLM to create an SOP, and then build an AI agent to execute it.00:28:06 The Reality of AI ImplementationBuilding a reliable AI agent is a gradual process of refinement and setting up guardrails, not a weekend project.00:29:21 Automating Agent CreationUsing connectors like Make.com within an LLM allows you to create and schedule automated workflows by simply describing them.00:31:08 The Timeframe for Building an AI SystemBuilding a truly autonomous system is a long-term journey of refinement; the key skill to learn is communicating with AI.00:33:57 Becoming an AI OrchestratorSellers must become orchestrators, designing and managing multiple small, independent AI agents to perform specific, connected tasks.00:35:56 The Future: Loaning vs. Building AI AgentsSellers will choose between "renting" cookie-cutter AI agents or "building" custom ones that act as a competitive moat.00:38:29 Are You a Brand Owner or a SaaS Provider?A warning for sellers: building your own AI tools means you are entering the SaaS business, which requires significant technical resources.00:41:13 The Shift from Prompt to Context EngineeringThe new challenge is context engineering: ensuring the right data and tools are used efficiently to avoid token exhaustion and errors.00:42:55 Three Actionable TakeawaysThe host summarizes three key actions: document processes with video, use an MCP for context, and decide your role (brand/SaaS).00:47:25 Most Influential BookMeher shares that the biography of Steve Jobs has been his most influential book due to its lessons on focus.00:48:25 Favorite AI ToolMeher recommends WhisperFlow for voice-to-text communication with AI, which has eliminated his need to type when using Claude.00:49:23 Most Respected Person in E-commerceMeher names Jeff Cohen as someone he admires for his deep, hands-on knowledge of the Amazon and retail media ecosystem.Resources mentioned in this episode:Josh Hadley on LinkedIneComm Breakthrough ConsultingeComm Breakthrough Podcast

Join us as Dave walks through what it actually takes to build custom AI agents from scratch - not theory, but real projects he has shipped for his family, his work, and his community. Dave shares how he used Kiro and Claude to solve real problems: normalizing flood-damaged library inventory data, automating AWS well-architected review collateral, building a room-cleaning task agent for his 12-year-old, planning family menus with Apple Calendar integration, and post-processing live concert recordings. You will learn how agents reason and take action, when to reach for a Kiro power versus a simpler automation, how MCP servers connect agents to real-world tools, and practical strategies for keeping agents accurate without burning through tokens. Timestamps 0:00 Welcome & Introduction 7:57 Dave's Background and How He Got Started with Agents 13:00 The Library Flood Story - First Real-World Agent Use Case 16:00 AWS Well-Architected Review Automation 17:09 What Are Kiro Powers and MCP Servers? 22:13 Kiro Pricing and Bedrock Integration 28:13 Live Demo - Room Cleaning Agent with AWS Rekognition 41:24 Family Meal Planning and Apple Calendar Integration 44:27 Automating Live Concert Recording Post-Processing 52:31 Getting Started - Dave's Recommendations for Beginners How to find Dave: https://www.linkedin.com/in/dave-stauffacher/ Links from the show: https://kiro.dev/

Dave sits down with Eoin Clancy, VP of Growth at AirOps, to talk about what's working in B2B marketing right now. They get into the rise of the content engineer role, how to use AI to produce high-quality content without creating AI slop, and why webinars have become AirOps' top growth channel in 2026. Eoin breaks down the three signs that content is AI slop, how AirOps runs their webinar funnel end-to-end, and how they follow up with attendees without ever pushing for a demo.Timestamps(00:00) - - Intro and episode overview (04:15) - - What AirOps does and the content engineer role (09:49) - - Why good SEO principles haven't changed in the AI era (14:13) - - AirOps' growth story: 10x revenue in 12 months (17:40) - - The challenge of using AI without creating slop (20:48) - - Three signs your content is AI slop (24:47) - - How to capture and maintain your brand's tone of voice (27:41) - - Why subject matter expertise is the best content ingredient (36:49) - - Why webinars are AirOps' #1 growth channel in 2026 (42:43) - - How AirOps plans topics and sources webinar guests (44:05) - - The webinar tech stack: Luma, HubSpot, Zoom, and Clay (44:34) - - Personalized follow-up strategy and signal scoring (49:07) - - How to build internal buy-in for a long-game content strategy (54:14) - - How to fill a webinar without gating anything Join 50,0000 people who get Dave's Newsletter here: https://www.exitfive.com/newsletterLearn more about Exit Five's private marketing community: https://www.exitfive.com/***Brought to you by:Optimizely - A no-code AI platform where autonomous agents execute marketing work across webpages, email, SEO, and campaigns. Learn how to deploy agents on your marketing team at Agents in the Mix. Learn more at optimizely.com/exitfive. Vector - A contact-level ads platform that lets you build audiences from actual people on your site, clicking your ads, and checking out your competitors. Learn more at vector.co, and get their new MCP server by clicking here. Customer.io - An AI powered customer engagement platform that help marketers turn first-party data into engaging customer experiences across email, SMS, and push. Learn more at customer.io/exitfive.Join us in Stowe, Vermont for Drive 2026 - three days away from your desk to learn what's working in B2B marketing from the people who are actually doing it. Grab your ticket at exitfive.com/drive.***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

Mathias Berny, cofondateur de Prelude, est un spécialiste de la lutte contre la fraude au SMS. Il a appris sur le terrain, chez Zenly, où la vérification des numéros de téléphone par SMS représentait le deuxième poste de dépense de l'entreprise, juste après les salaires. Un week-end, un pic de trafic anormal a englouti l'équivalent d'un trimestre de budget, sans possibilité de remboursement par les partenaires — un épisode fondateur pour lui.De ce constat naît Prelude, qui combine analyse de signaux de trafic et technologies de fingerprinting développées en interne pour distinguer utilisateurs légitimes et bots, même derrière un VPN. Son modèle économique ne prend aucune marge sur le prix du SMS, facturé au coût réel, et fait payer uniquement la couche anti-fraude à prix fixe — un cercle vertueux selon Berny. Avec une cinquantaine de salariés et une forte présence parisienne, Prelude compte parmi ses clients Alan, Alma, Birdy ou Suno. Prochain défi : l'authentification des agents IA, via des protocoles comme le MCP. Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Week 2 highlights follows Anthropic's Fable launch in real workflows, from safety gates and API refusals to autonomous coding, 3D world-building, and a Claude-run Twitter experiment. Geoffrey Irving and Daniel Murfet argue for alignment theory and guarantees before recursive self-improvement, while prinz tests Fable on legal reasoning and monitoring. Rahul Sonwalkar, Shlok Khemani, Tom McGrath, and Andrew Moore add field reports on data agents, hybrid authorship, interpretability, context systems, token economics, and power concentration. Mercury: Run your finances with virtual cards, spending limits, merchant/category locks, and AI-friendly tools like API keys, MCP, and CLI. Check out Mercury at https://mercury.com LINKS: Claude Fable 5 announcement Julius AI platform Rahul Sonwalkar homepage Nate Jones homepage Shlok Khemani homepage FrontierCode benchmark blog Lovelace AI company Andrew Moore Wikipedia profile Geoffrey Irving homepage Daniel Murfet LessWrong profile Sequent Research announcement Timaeus research organization Automated Alignment paper Goodfire AI company Tom McGrath homepage Predictive data debugging tool prinzbench legal benchmark Unit distance conjecture disproof Dario Amodei policy essay Vending-Bench 2 benchmark Andon Labs site Recursive Superintelligence startup Sakana AI company PostTrainBench benchmark Thoughtful Lab company Unit distance conjecture arXiv Glean Work AI Index AI Treaty open letter Karina Nguyen homepage Sponsor: Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr

OpenChoreo is an opinionated, “batteries included”, AI-native Kubernetes platform stack for Platform Engineers that combines GitOps, Observability, AI Agents, and Workflows into a custom K8s distribution “super pack” that is managed via Backstage, CLI, API, or MCP. Now a CNCF project.Check out the video podcast version here: 

The week starts with Dwayne Johnson's "The Red Pill" — the most honest structural diagnosis of Oregon's innovation economy I've read in years, from someone who's been inside the system for two decades, not outside it. Oregon fell from #7 to #41 in CNBC's Top States for Business across the Brown era; Oregon got second in semiconductor productivity and 0.002% of federal CHIPS R&D funds. His phrase: "Oregon runs on cliques, not networks." Then Friday, Engine's Innovation Flywheel report lands — four dimensions for a healthy innovation ecosystem, three of which Portland already has covered, and one — Center of Gravity — that's the exact tripwire Dwayne was pointing at. Plus Expensify ships an MCP server that lets your AI agent talk directly to your expense data, Missing Middle Housing Fund's Nate Wildfire joins the Housing Voices podcast, and Portland moves up five spots to #17 in the Financial Times ranking of best U.S. cities for foreign business — Boston at the top, Seattle slipping.CHAPTERS:00:00 Portland startup news04:15 Dwayne Johnson on Portland's archipelago 08:20 Engine's Innovation Flywheel14:10 Financial Times ranks Oregon #1716:17 SecretsLINKS:Long-time innovation ecosystem builder Dwayne Johnson — https://siliconflorist.com/2026/06/08/long-time-innovation-ecosystem-builder-dwayne-johnson-shares-insights-on-oregon-economic-woes/Dwayne Johnson on LinkedIn — https://www.linkedin.com/in/drfortune/Your AI agent can now talk to your expense data with the new Expensify MCP — https://siliconflorist.com/2026/06/08/your-ai-agent-can-now-talk-to-your-expense-data-with-the-new-expensify-mcp/Expensify MCP — https://expensify.com/mcpPortland's Missing Middle Housing Fund joins Housing Voices — https://siliconflorist.com/2026/06/08/portlands-missing-middle-housing-fund-joins-housing-voices/Missing Middle Housing Fund — https://www.missingmiddlehousing.fund/Portland moves up five spots in Financial Times "best US places for foreign businesses" — https://siliconflorist.com/2026/06/09/portland-moves-up-five-spots-in-financial-times-best-us-places-for-foreign-businesses/FT-Nikkei ranking — https://www.ft.com/content/3fb85af1-d581-4f43-b962-a1d79160cdecUsing Engine's "Innovation Flywheel" to benefit the Portland startup community — https://siliconflorist.com/2026/06/12/using-engines-innovation-flywheel-to-benefit-the-portland-startup-community/The Foundations of an Innovation Flywheel (Engine) — https://www.engine.is/news/category/the-foundations-of-an-innovation-flywheelApply to lead the Portland Metro Region Innovation Hub https://jobs.hrc.pdx.edu/postings/49951FIND RICK TUROCZY ON THE INTERNET AT…- https://patreon.com/turoczy- https://linkedin.com/in/turoczyABOUT SILICON FLORIST ----------For nearly two decades, Rick Turoczy has published Silicon Florist, a blog, newsletter, and podcast that covers entrepreneurs, founders, startups, entrepreneurship, tech, news, and events in the Portland, Oregon, startup community. Whether you're an aspiring entrepreneur, a startup or tech enthusiast, or simply intrigued by Portland's startup culture, Silicon Florist is your go-to source for the latest news, events, jobs, and opportunities in Portland Oregon's flourishing tech and startup scene. Join us in exploring the innovative world of startups in Portland, where creativity and collaboration meet.ABOUT RICK TUROCZY ----------Rick Turoczy has been working in, on, and around the Portland, Oregon, startup community for nearly 30 years. He has been recognized as one of the “OG”s of startup ecosystem building by the Kauffman Foundation. And he has been humbled by any number of opportunities to speak on stages from SXSW to INBOUND and from Kobe, Japan, to Muscat, Oman, including an opportunity to share his views on community building on the TEDxPortland stage (https://www.youtube.com/watch?v=Cj98mr_wUA0). All because of a blog. Weird.https://siliconflorist.com#pdx #portland #oregon #startup #entrepreneur

At Infosecurity Europe 2026 in London, Bill Peterson, Senior Director of Product Marketing at Sumo Logic, joins us to unpack a tension every regulated security team knows well. When an incident hits, the business has to keep running. At the same time, regulators expect sensitive data to stay in region. For a long time, those two demands have pulled in opposite directions. Sumo Logic has spent 15 years as a SaaS platform on AWS, processing roughly four exabytes of data a day for around 2,000 customers. The core promise is speed, driving mean time to resolve as low as possible. Peterson frames it in business terms, because the person signing the check wants to know the return, not the bits and bytes. The news from the show is Sumo Logic availability on the AWS European Sovereign Cloud. EU organizations can keep their data in region, handled by EU staff, while still running the full platform for incident response. That turns a painful either/or into a checklist a regulated buyer can complete. Genesys is the first customer live in the sovereign cloud, with payment processor OpenPay preparing to follow. How does this play out for highly regulated industries? Sumo Logic is focused on finance, healthcare, telco, and government, the verticals feeling the most pressure. The path Peterson describes is simple: let Sumo Logic handle incident management, let AWS move and grow the data in region, and check the sovereignty box without giving up operational readiness. Underneath sits a full-featured SIEM and Dojo AI, the agentic approach Sumo Logic launched earlier this year. The goal is not to replace analysts but to keep a human in the loop while handing proven, repetitive work to an agent. Fix one server, confirm the solution, then let an agent patch the other 599 under oversight. A SOC Analyst Agent reaches general availability at Black Hat later this year, alongside an MCP server. On observability, the differentiator is reading both structured and unstructured data without normalizing it first. A zip code is structured; a cryptic web hook error is not. Sumo Logic reads both, which feeds directly into faster time to identify and faster time to resolve. For any leader weighing sovereignty against uptime, Bill Peterson makes a clear case that they can finally live in the same plan. This is a Brand Spotlight. A Brand Spotlight is a ~15 minute conversation designed to explore the guest, their company, and what makes their approach unique. Learn more: https://www.studioc60.com/creation#spotlight GUEST Bill Peterson, Senior Director of Product Marketing, Sumo Logic LinkedIn: https://www.linkedin.com/in/williampetersonjr/ RESOURCES Learn more about Sumo Logic: https://www.sumologic.com/ Sumo Logic on the AWS European Sovereign Cloud (announced at Infosecurity Europe 2026): https://www.sumologic.com/newsroom Infosecurity Europe 2026 event coverage: https://www.itspmagazine.com/infosecurity-europe-2026-infosec-london-cybersecurity-event-coverage Are you interested in telling your story? ▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full ▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight ▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight ▶︎ Get your own Brand Briefing at an upcoming event: https://www.studioc60.com/buy-brand-briefings KEYWORDS Bill Peterson, Sumo Logic, Sean Martin, brand story, brand marketing, marketing podcast, brand spotlight, AWS European Sovereign Cloud, data sovereignty, incident response, mean time to resolve, SIEM, security operations, Dojo AI, agentic AI, SOC analyst agent, observability, log analytics, Infosecurity Europe 2026 Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Stanislas Polu is Co-Founder & CTO of Dust — the enterprise AI agent platform used by 51,000 workers at 3,000+ companies. Before Dust, he spent three years on OpenAI's research team under Ilya Sutskever, working on mathematical reasoning in language models, and prior to that was an engineer at Stripe. He brings a rare combination of frontier AI research and product-building experience to the enterprise agent space.MCP, Agents & the $40M Bet on Multiplayer AI // MLOps Podcast #384 with Stanislas Polu, Co-Founder & CTO of Dust

An airhacks.fm conversation with Alvaro Hernandez (@ahachete) about: discussion about the quarkus Insights episode "#337 The Database Cloud" stackgres live demo, StackGres as a Quarkus and GraalVM native kubernetes operator for running Postgres, comparing CloudNativePG (CNPG) by EnterpriseDB to StackGres, Patroni for Postgres high availability, the split-brain risk of relying on Kubernetes and etcd alone, distributed consensus and leader lock election via etcd, why distributed systems and cryptography should not be self-implemented, async, synchronous and quorum (semi-synchronous) Postgres replication trade-offs, cascading and cross-region replication topologies, the false-positive problem and heuristic exceptions in two-phase commit, the ondb ("own your database") project for self-hosted Postgres, losing control with managed cloud services and untestable backups, vanilla unmodified Postgres on StackGres, the "Kubernetes without Kubernetes" (Kubeless) pattern, talking directly to ContainerD through the CRI API, runc and the Docker to ContainerD chain, a self-contained native binary that embeds ContainerD over Unix domain sockets, the slony node-local component named after the Postgres slonik elephant mascot, the Matriarch orchestrator component, reverse gRPC tunnels with Slonies phoning home across NAT and firewalls, a multi-tenant cloud control plane provided as a service, curl-pipe-shell node installation with a token, end-to-end encrypted Postgres protocol tunneling for JDBC from anywhere, psql compiled to wasm in the web console, Tailscale-inspired user experience, unifying nodes, Kubernetes clusters and cloud pools as resources, Slony Kubernetes controller, Java 25 source-mode scripting without dependencies, implementing your own MCP server for Postgres JDBC metadata, the Goose agentic UI donated by Block to the Linux Foundation, AI Rails BCE, Java, Web Components skills Alvaro Hernandez on twitter: @ahachete

Hey folks, Alex here, and welcome to a BIG MODEL week! We finally got Mythos (well almost)! Let me catch you up! This week started with WWDC26 from Apple, and Max Weinbach, who was in the room at Apple Park and actually has access to some of the new features including an all new SIRI AI, joined us to break down what could be the most used AI in the world very soon. At first I was skeptical, but he convinced me that the new Siri is actually good! Then, we saw the ultimate model drop: Anthropic finally shipped Mythos (X, my system card thread, benchmarks). Same weights, two names: Mythos 5 is the unrestricted version that only Project Glasswing partners get, Fable 5 is what the rest of us get, wrapped in the heaviest guardrails I've ever seen ship on a frontier model. It's state of the art on nearly every benchmarkThe model that was “too dangerous to release” is now... well, released, but with the heaviest guardrails we've seen. More on this later. Peter Gostev from Arena.ai joined us to break down the new model. Last but definitely not least, Google released a real-time translation model, that our friend Thor Schaeff from DeepMind demoed live, while we all spoke in different languages and it translated us in REAL TIME. It was really cool, definitely check that out. There's quite a few more things, like Loop Engineering Alpha, Swyx came by to talk about FrontierCode, OpenAI confirmed our suspicions that the anti-datacenter social media posts could be a concerted effort by groupds links to the Chinese government and much more. Let's dive in! ThursdAI - Let me catch you up, every week!

New location, Gordon’s Office We went to Wordcamp Europe in Poland, Krakow European Wordcamp is the biggest of the conventions Reasons for going – networking with friends and colleagues Theme this year seemed to be AI Connecting WordPress using MCP to Claude Using AI to build pages, perform WordPress operations, correct grammar, create images, pretty much everything AI is good at Need to have a good understanding of what is happening so you can check it Four floors in the convention centre – live transcription and translations this year. Two huge rooms for lectures Food, drinks and snacks always available Evening parties – drinking and socialising WP Umbrella – great way to manage your websites – https://wp-umbrella.com/ Great country – Perogi was a favourite at meal times Lots of things to see Much cheaper than Switzerland last year Salt Mine visit was great – seeing all the salt sculptures Visit to Auschwitz – which was interesting and harrowing A good rounding off from my Anne Frank experience Rooms of shoes, hair, luggage, pots and pans Next year is in Malaga – a week earlier than this year Details at: https://europe.wordcamp.org/2027/ Visit the website: https://www.thisweekwith.co.uk Drayson Design Website – https://www.draysondesign.com The Creative Tinker Website – https://www.thecreativetinker.com Facebook: https://www.thisweekwith.co.uk/facebook Youtube: https://www.thisweekwith.co.uk/youtube * Full transcript will be available on the website. We may receive a referral fee from any of our links which help towards the costs involved in creating this content for you.

Full show notes and transcript  - https://bit.ly/google-agentic-eraWatch on YouTube - https://youtu.be/eamMBmm6oTU-----Episode Summary:Dara and Matthew open with a breaking-news bulletin on Anthropic's newly released Fable, the consumer sibling to Mythos, covering its safety off-ramp to Opus 4.8, its pricing, and the looming switch from subscription to usage-based access. The main episode is a deep dive on Google Cloud Next '26 and I/O '26, unpacking the Gemini Enterprise Agent Platform, Gemini 3.5 Flash, Omni, Antigravity 2.0, WebMCP, and the shift to generative AI search. The thread running through it all: agents are the headline, but governance and a solid semantic layer are the subplot that makes them actually useful.-----About The Measure Pod:The Measure Pod is your go-to fortnightly podcast hosted by seasoned analytics pros. Join Dara Fitzgerald (Co-Founder at Measurelab) & Matthew Hooson (Head of Engineering at Measurelab) as they dive into the world of data, analytics and measurement, with a side of fun.-----If you liked this episode, don't forget to subscribe to The Measure Pod on your favourite podcast platform and leave us a review. Let's make sense of the analytics industry together!

Brent Baxter, Sam Delestienne, Steve Hoffman, John Strenger, and Matt Melsen Winning a banker-run auction at 5% under the highest bid. Closing a deal when co-sellers have not spoken in months. Getting through 22 countries of employment complexity with a client who refused to work with EOR providers. Acquiring a Netherlands-based public company and discovering the due diligence documents were in Dutch. These are the problems that no playbook prepares you for. Four corp dev professionals share how they handled them, and what it cost when they got it wrong. What You'll Learn  How to win a competitive auction when you're not the highest bidder What seller conflict at the closing table looks like (and how to get a deal back on track) When an employer of record works in a cross-border carve-out and when it creates permanent establishment risk Why management trust in the buyer can outweigh the highest bid number What a first European acquisition actually costs in compliance, legal, and cultural surprises If you're running deals where the numbers are right but the relationship isn't, or you're in a market you haven't operated in before, DealPilot, powered by M&A Science, connects you with advisors who have closed deals in exactly that situation. ____________________ This episode of M&A Science is presented by DealRoom. DealRoom just launched the only MCP server built for Buyer-Led M&A™ — so your AI and your deal data finally work together. Connect Claude, ChatGPT, or Copilot directly to DealRoom and let your AI read your pipeline, analyze due diligence documents, and automatically write findings back.  See for yourself: dealroom.net/mcp ____________________ Episode Chapters [00:00] Intro [03:12] Partners who came to blows over valuation [03:37] The closing table walkout [05:47] Every deal craters on Friday [07:54] Why managing emotions is the hardest job after LOI [13:30] A door blows off an Alaska Airlines jet mid-process [16:00] Winning at $15M under the highest bid [18:23] Trust and reputation as deal currency [23:09] The "baby ugly" lesson [25:06] Preempting banker processes [32:14] What EOR is and when it works [33:52] Permanent establishment risk with C-level hires [34:48] CBA compliance across 22 countries [40:38] First European cross-border acquisition [42:38] Dutch documents and data residency surprises [46:20] Why in-person matters more in Europe [50:38] The $100M tax exposure that was not real [55:57] Outro

#363 | Louis Grenier joins Dave for a conversation about what it takes to stand out in B2B marketing when everything feels the same. They get into how people really decide to buy, why most B2B brands look and sound identical, and the marketing fundamentals that hold up no matter what's changing around them. The kind of conversation that reminds you why you got into marketing in the first place. Timestamps (00:00) - - Intro (07:14) - - Surviving cancer at 36 and what it taught Louis about doing work that matters (14:10) - - Why marketers fail when they try to educate the market instead of meeting it (19:36) - - The case for leading with one thing even when your product does ten (21:39) - - You can't create demand you can only position into demand that already exists (27:08) - - Why the most memorable brands use assets that mean nothing on purpose (36:34) - - What a real point of view is for and why most companies get it wrong (37:33) - - How to get leadership to say yes to bold unconventional marketing ideas (48:18) - - Why pain points don't drive purchases and what actually pulls the trigger (53:22) - - The case for saying the same thing a thousand different ways (01:00:32) - - Why strong marketing fundamentals matter more in an AI world not less Join 50,0000 people who get Dave's Newsletter here: https://www.exitfive.com/newsletterLearn more about Exit Five's private marketing community: https://www.exitfive.com/***Brought to you by:Optimizely - A no-code AI platform where autonomous agents execute marketing work across webpages, email, SEO, and campaigns. Learn how to deploy agents on your marketing team at Agents in the Mix. Learn more at optimizely.com/exitfive. Vector - A contact-level ads platform that lets you build audiences from actual people on your site, clicking your ads, and checking out your competitors. Learn more at vector.co, and get their new MCP server by clicking here. Customer.io - An AI powered customer engagement platform that help marketers turn first-party data into engaging customer experiences across email, SMS, and push. Learn more at customer.io/exitfive.Join us in Stowe, Vermont for Drive 2026 - three days away from your desk to learn what's working in B2B marketing from the people who are actually doing it. Grab your ticket at exitfive.com/drive.***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

One of the biggest problems of vibe coding? Securely keeping the project up to date and sharing it with your team to make it actually useful. And there's a new solution that does just that, Codex Sites. With a few simple prompts, you can turn vibe coded throwaway apps into working pieces of software that your team can share. We put AI to work on Wednesday and show you how to get the most out of Codex Sites. Codex Sites: The Lovable and Replit Killer? A hands-on Guide to Codex Sites -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Codex Sites vs Static File SharingLive Dashboards and Automated WorkflowsBuilding Internal Apps With Codex SitesReal-Time Data Integration in CodexAgent Layer and Role-Based Access ControlCodex Sites vs Replit, Lovable, BoltDynamic Business Insights and CollaborationCodex Sites Secure Team Sharing LimitationsAutomations and Custom Skills in CodexFuture of AI Native Business ToolsTimestamps:00:00 The future of work automation03:43 Free daily newsletter highlights08:29 Managing audience momentum dashboard12:04 Pulling stats and data access14:48 Creating dynamic web tools16:18 Editing video collaboration challenges21:09 Comparing coding platforms like Replit25:47 Future of Business Analytics Tools27:11 Introducing the Start Here series32:35 Updating old content ideas34:53 Streamlining team efficiency with AI37:02 Episode use cases overviewKeywords: Codex sites, OpenAI, AI dashboards, live software, file sharing, business automation, dynamic data, ChatGPT business, agentic system, Chrome integration, MCP servers, skills, plugins, Copilot Scout, internal dashboards, data analysis, role based access control, data governance, enterprise AI tools, site hosting, live app builder, prompt driven apps, automations, Replit alternative, Lovable competitor, full stack app builder, dynamic business context, annotation feature, nontechnical teams, BI dashboards, Kanban tracker, evergreen content, live indicators, audience momentum dashboard, sub agent, responsive design, visual design, parallax feature, actionable insights, version control, dynamic deliverables, artifact, demo over memo, knowledge work, IT security, internal URL sharing, AI native workflow, internal business tools, real time updates, start here seriesSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

Send us Fan MailIn this episode of the WTR Small-Cap Spotlight podcast, Gautam Garg, Vice President of Finance of eGain Corporation (NASDAQ: EGAN), joins host Tim Gerdeman, Vice Chair, Co-Founder, and Chief Marketing Officer of Water Tower Research, and WTR Analyst James Kisner.eGain is a leader in AI-powered knowledge management, helping Global 2000 enterprises unify siloed content into an AI Knowledge Hub that delivers accurate, compliant answers across customer service and adjacent functions.Garg explains why trusted knowledge has emerged as core AI infrastructure and why enterprise AI initiatives frequently underperform when built on stale or inconsistent data. He walks through recent product launches including the AI Knowledge Suite for Retail Banking, the IVA voice agent, Evaluator, Agentic Studio, and the developer-focused Composer platform, which supports integrations with Copilot, Claude, Gemini, and Cursor via MCP connectors.The conversation also covers a surge in RFP activity, a fast-growing partner ecosystem, expansion into HR and field service verticals, and eGain's profitable, debt-free financial profile heading into fiscal year 2027.

Joe Beda and Craig McLuckie co-created Kubernetes, the infrastructure standard that became the default for cloud native computing. Now running Stacklok, they're watching enterprises hit the same identity, permissions, and security problems with AI agents that took the container ecosystem years to resolve, and they're building tools to compress that timeline. In this episode of Founded & Funded, Madrona's Tim Porter sits down with Joe and Craig to talk through what AI adoption actually requires: why MCP is the Docker moment for AI-native applications, how the LLM gateway is becoming a strategic chokepoint for cost, safety, and model flexibility, and why enterprises that don't get the architecture right early will face a familiar trap: vertical integration that looks like productivity and acts like lock-in. They cover: Why the developer workflow is the template for knowledge worker AI adoption, and where the analogy breaks down The mainframe vs. open platform question that will define the AI infrastructure era Why the knowledge worker transition is harder than it looks — and what has to be built differently before developer-grade AI tooling can scale to the rest of your organization The governance gap between human accountability and AI behavior, and what enterprises actually need to build to close it Where to start: MCP controls first, LLM gateway second, and why deploying a platform without staying to close the loop consistently fails Transcript: https://www.madrona.com/the-best-infrastructure-moment-since-cloud Chapters: (0:00) – Introduction (1:04) – Why the Kubernetes Creators Are the Right People to Read This AI Moment (2:18) – Joe's Lesson from Cloud Native: Ignore Conventional Wisdom, Except When You Shouldn't (4:16) – Craig on Enterprises and the Chaos of a New Infrastructure Era (5:32) – Why Joe Rejoined Craig at Stacklok: The Engineer's Case for Getting Your Hands Dirty (7:05) – Developers as Agent Orchestrators: How the Knowledge Worker Transition Will Follow (10:10) – MCP Explained: Craig Sees Docker in 2013 When He Looks at the MCP Spec 1 (7:53) – The Mainframe vs. Open Platform Question That Will Define the AI Era (20:24) – LLM Lock-In Is the Wrong Worry: The Real Risk Is Left of the Model (25:19) – Where Enterprises Actually Start: Developer Posture First, Knowledge Workers Second (29:10) – MCP First, LLM Gateway Second: The Concrete Technical Starting Point (31:19) – How Stacklok Builds Software Now: Agents, Smaller Teams, the Unrecognizable Developer Profile (38:07) – The Recruiter Who Started Building Agents: What AI Tools Do to Role Boundaries

Topics covered in this episode: Vulnerability and malware checks in uv HTTP GET requests with the Python standard library Millions of AI agents imperiled by critical vulnerability in open source package alembic-git-revisions Extras Joke Watch on YouTube About the show Goodbye and Thanks Brian Thanks Calvin for being part of this and future episodes! Also new time for the live show. Thanks Brian for all the hard work over the years. Calvin #1: Vulnerability and malware checks in uv release just yesterday by Astral https://astral.sh/blog/uv-audit uv audit scans dependencies for known vulnerabilities and abandoned packages via the OSV database — runs 4–10x faster than pip-audit Malware check runs on every install/sync, catching actively malicious packages (credential stealers, etc.) before they execute — including ones PyPI quarantined but lockfiles can still reference Enable malware scanning with UV_MALWARE_CHECK=1 — it's opt-in and in preview Future roadmap includes a resolver that steers toward vulnerability-free versions and install-time warnings scoped to newly added deps only Michael #2: HTTP GET requests with the Python standard library If you're doing HTTP in Python, you're probably using one of three popular libraries: requests, httpx, or urllib3. There have been issues with httpx lately. Niquest is another option: Drop-in replacement for Requests. Automatic HTTP/1.1, HTTP/2, and HTTP/3. WebSocket, and SSE included. But maybe less is more, especially in the age of agentic AI A good candidate needs two things to be true at once, not one: the used surface is small, and the behavior behind that surface is shallow. Calvin #3: Millions of AI agents imperiled by critical vulnerability in open source package "BadHost" (CVE-2026-48710) is a critical vulnerability in Starlette — the ASGI framework underlying FastAPI — with 325 million weekly downloads; also affects vLLM, LiteLLM, and most MCP server tooling The exploit is trivial: injecting a single character into an HTTP Host header bypasses path-based authentication, and can lead to credential theft, SSRF, and in some cases remote code execution MCP servers are a prime target since they store credentials for external services (email, databases, cloud accounts) — exposed data in the wild includes biopharma clinical trial DBs, full mailboxes, HR/PII pipelines, and AWS topology Fix is available — patch to Starlette 1.0.1 immediately; use the free scanner at mcp-scan.nemesis.services to check if your servers are still running a vulnerable version Open source sustainability footnote: the maintainer triages near-daily security reports solo, in his free time — most are AI-generated noise, and real ones like this still compete for the same evenings and weekends Michael #4: alembic-git-revisions By Julien Danjou from Mergify Automatic Alembic migration chaining based on git commit history. No more Multiple head revisions are present for given argument 'head'. See the introductory article Caused by two migrations landed with the same down_revision, and Alembic doesn't know which one comes first. The fix is always the same: someone manually edits the migration file to re-chain the revisions. The insight: git already knows the order Extras Calvin: GNU make can do pattern matching in the target. Not new at all, mentioned in the 1994-era docs. just and task don't have this super power on the target name yet. train-%: uv run ./train.py $* --save-hyper-params --overwrite $(TRAIN_ARGS) Michael: Updated my HTTP client using packages from httpx to httpx2: listmonk, umami, and memberful. For motivation, see this reddit thread. Joke: Accurate

Anthropic is preparing for public markets. Europe is pushing to reduce its reliance on American tech. And CNN just filed a copyright lawsuit against Perplexity. This week, Anthropic files confidential paperwork for an IPO in one of the biggest milestones yet for the AI industry, Europe unveils a tech sovereignty plan to reduce its dependence on American technology, CNN sues Perplexity over the alleged redistribution of more than 17,000 copyrighted stories, Morgan Stanley opens its platform to external AI agents using MCP, and Microsoft teams up with Mayo Clinic to build an AI model for healthcare. If you are a founder, operator or executive trying to keep up with AI, this is your weekly five minute briefing. Stories Covered This Week: Anthropic files confidential paperwork for an IPO, with OpenAI expected to follow soon Europe unveils a tech sovereignty plan to build out data centers, revive its chip industry and buy more from European suppliers CNN sues Perplexity over the alleged use of more than 17,000 copyrighted stories, photos and videos Morgan Stanley becomes one of the first major Wall Street banks to open its platform to external AI agents via MCP Microsoft and Mayo Clinic partner to build an AI model designed specifically for healthcare Episode Timestamps: 00:00 Intro 00:17 Anthropic files to go public 01:17 Europe pushes for tech sovereignty 02:28 CNN sues Perplexity 03:23 Morgan Stanley opens up to AI agents 04:22 Microsoft and Mayo Clinic build a healthcare AI 05:11 Outro Partner Links: Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

Apple @ Work is exclusively brought to you by Mosyle, the only Apple Unified Platform. Mosyle is the only solution that integrates in a single professional-grade platform all the solutions necessary to seamlessly and automatically deploy, manage & protect Apple devices at work. Over 45,000 organizations trust Mosyle to make millions of Apple devices work-ready with no effort and at an affordable cost. Request your EXTENDED TRIAL today and understand why Mosyle is everything you need to work with Apple. In this episode of Apple @ Work, Aaron Morin and Lance Crandall from Iru join the show to talk about all things MCP servers and Apple device management.

This episode features Geoffrey Mattson, CEO of SecureAuth, joined by co-host Sarah Cicchetti, Director of Product Management at Semperis.Geoffrey has spent decades building and leading companies at the intersection of AI and cybersecurity, including MistNet.ai, an AI-native threat detection platform acquired by LogRhythm, and Xage Security, where he drove zero trust adoption across the U.S. military, global energy firms, and Fortune 500 enterprises. At SecureAuth, he leads a platform built around continuous, real-time identity authority across workforces, APIs, and AI agents.In this episode, Geoffrey argues that agents combine the speed of automation with the unpredictability of humans, making real-time per-action authorization the only viable control model. He discusses why “friendly fire” from well-meaning employees is the biggest threat vector right now, how MCP vendors are ignoring their own OAuth spec, and what a practical agent rollout with real guardrails actually looks like.This episode reframes authorization as the problem the identity industry has been deferring for years and can no longer avoid.Guest Bio Geoffrey Mattson is a serial entrepreneur and globally recognized cybersecurity and AI executive with decades of experience building market-defining companies and technologies that protect the world's most critical systems.He is currently CEO of SecureAuth, a leader in AI-driven identity and access management with its Continuous Authority, ensuring ongoing verification across workforces, customers, APIs, and AI agents. This is enabled through its Private Authority Platform, which puts authentication and authorization under your control through any deployment model (cloud, on prem, hybrid, air-gapped).Prior to SecureAuth, Mattson served as CEO of Xage Security, where he led the company in Zero Trust for critical environments from energy to agentic AI. Under his leadership, Xage achieved rapid adoption across the U.S. military, global energy firms, and Fortune 500 enterprises.Previously, Geoffrey Mattson was co-founder and CEO of MistNet.ai, an AI-native threat detection platform acquired by LogRhythm. He pioneered decentralized analytics and machine learning approaches for real-time cyber defense, and later served as SVP of Product at LogRhythm, driving global expansion and shaping the next generation of SIEM/SOAR solutions.Earlier, he held senior executive roles at Juniper Networks, overseeing a $2B product portfolio and leading major M&A efforts, and at Huawei Technologies as SVP and CTO for networking and data center platforms. His engineering leadership at Corona Networks, Caspian, and Bay Networks helped build foundational technologies in network and security architecture.Guest Quote “With agents, you have the power and the speed of an automated process with the unpredictability of a human. And in fact, we are seeing their behavior and their psychology makes them even perhaps less predictable than a human.”Time stamps 01:45 Meet Geoffrey Mattson: Serial Entrepreneur and Cybersecurity Executive 02:40 Why Identity Is Having a Moment 08:40 Defining Agent Identity 12:15 Behavioral Guardrails for Agents 14:37 Agent Identity Lifecycle 17:36 Just-in-Time vs. Standing Privilege 18:02 C-Suite Pressure and Friendly Fires 21:00 When Agents Live Off the Land 26:12 MCP, OAuth, and Token Pitfalls 28:04 Threat Models and Rollout Strategy 30:13 LLMs and Policy Authoring 31:23 Conclusion and Final ThoughtsSponsor The HIP Podcast is brought to you by Semperis, the leader in identity-driven cyber resilience for the hybrid enterprise. Trusted by the world's leading businesses, Semperis protects critical Active Directory and Entra ID environments from cyberattacks, ensuring rapid recovery and business continuity when every second counts. Visit semperis.com to learn more.LinksConnect with Geoffrey on LinkedInConnect with Sarah on LinkedInConnect with Sean on LinkedInDon't miss future episodesLearn more about Semperis

Acheter des cryptos depuis ChatGPT, brancher Claude sur son wallet via un MCP, ou laisser un agent rebalancer ses positions DeFi pendant qu'on dort : l'IA est entrée par toutes les portes de notre argent. Mais jusqu'où peut-on vraiment lui faire confiance, alors que la DeFi vient d'enchaîner les hacks à plusieurs centaines de millions de dollars et que Mythos est sur le point d'arriver ? On en parle avec Paul Laulan, fondateur de ZyfAI, qui développe un agent capable de gérer un portefeuille en DeFi de façon autonome. On parle MCP, skills, OpenClaw, sécurité, et de la nouvelle économie des agents-payeurs qui se prépare.Disclaimer : Cryptoast est investisseur dans ZyfAI.Pour essayer ZyfAI et profiter de 5% de boost sur vos points ► https://cryptoast.fr/go-zyfai/______________________________________________Vous avez quelque chose à partager et souhaiteriez le faire pendant une interview ?Envoyez-nous un mail à ⁠⁠⁠⁠⁠⁠contact@cryptoast.fr⁠⁠⁠⁠⁠⁠______________________________________________Nos podcasts sont aussi sur :

#362 | Dave sits down with Eddie Shleyner, founder of VeryGoodCopy.com, to talk about why great marketing copy can't be generated — it has to be written. They get into why AI writing feels flat, what separates copy that moves people from copy that just fills space, and why the process of writing is where the real work happens. Eddie also shares how he accidentally built one of the most-read copywriting newsletters on the internet and what he's learned about writing for humans after years of studying what makes people take action.Timestamps(00:00) - - - How Eddie accidentally discovered copywriting while writing job ads (02:58) - - - Why marketers are starting to push back on AI-generated content (02:59) - - - Why the best copy gives readers less — and makes them feel more (03:04) - - - Marketing is a human profession because empathy can't be prompted (03:13) - - - Where AI actually helps: research, not writing (03:17) - - - How VeryGoodCopy started as a private Google Doc no one was supposed to see (03:22) - - - Why AI writing tells you how to feel instead of letting you feel it (03:23) - - - The experiment: Eddie wrote the same story as AI, word for word, to prove a point (03:25) - - - What inspired writing has that AI writing doesn't (03:31) - - - Why shortcuts in the writing process produce worse work, not faster work Join 50,0000 people who get Dave's Newsletter here: https://www.exitfive.com/newsletterLearn more about Exit Five's private marketing community: https://www.exitfive.com/***Brought to you by:Optimizely - A no-code AI platform where autonomous agents execute marketing work across webpages, email, SEO, and campaigns. Learn how to deploy agents on your marketing team at Agents in the Mix. Learn more at optimizely.com/exitfive. Vector - A contact-level ads platform that lets you build audiences from actual people on your site, clicking your ads, and checking out your competitors. Learn more at vector.co, and get their new MCP server by clicking here. Customer.io - An AI powered customer engagement platform that help marketers turn first-party data into engaging customer experiences across email, SMS, and push. Learn more at customer.io/exitfive.Join us in Stowe, Vermont for Drive 2026 - three days away from your desk to learn what's working in B2B marketing from the people who are actually doing it. Grab your ticket at exitfive.com/drive.***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

In this episode of The Geek in Review, Greg Lambert and Marlene Gebauer welcome back Joel Hron, Chief Technology Officer at Thomson Reuters, for a timely conversation about the shifting relationship among foundation models, legal content providers, legal tech platforms, and the lawyers trying to make sense of the mess. Recent moves by Anthropic, including Claude's legal practice area tools and MCP connections into legal platforms, raise a larger question for the market. Is a model provider still sitting behind the scenes, or is it starting to become a legal work environment of its own?Hron explains Thomson Reuters' commitment to what it calls fiduciary-grade AI, a standard built around trust, verification, transparency, and accountability. For TR, legal AI needs more than a fast answer. It needs systems lawyers trust enough to stand behind. Hron points to Westlaw, Practical Law, KeyCite validity signals, citation ledgers, and verification tools as core ingredients in building AI systems suited for high-stakes professional work. In his view, almost right is not good enough when clients, courts, regulators, and professional obligations sit on the other side of the output.The conversation turns to how CoCounsel and Westlaw Deep Research use legal content across far more than traditional research tasks. Hron explains that when AI systems gain access to trusted legal content and verification tools, they begin researching throughout the workflow, even while revising contract language or analyzing provisions. He also describes Litigation Document Analyzer, internally nicknamed the BS Detector, a tool designed to review claims in a document and map them to supporting authority, weak support, or no support at all. For lawyers who spend as much time verifying AI output as generating it, tools like these aim to move verification from a manual scavenger hunt into a structured process.Greg and Marlene also press Hron on Anthropic's legal plugins, MCP, and the idea of headless legal technology. Hron argues that MCP changes access, not advantage. In his view, the application layer is shifting, but the real competitive value sits in trusted content, expert systems, governance, and domain-specific intelligence. CoCounsel's user interface represents one expression of TR's legal agent capabilities, while MCP opens other ways for those capabilities to appear inside broader work environments. Some work will still need a purpose-built legal interface; other work might happen through email, Word, Claude, or another agentic workflow with little visible interface at all.The episode closes with a larger discussion about what happens when AI starts performing more of the work itself. Hron shares TR's internal engineering OKR, where more than 50 percent of pull requests should be written by AI, and explains why 51 percent serves as a useful mental model. Once AI performs a controlling share of the work, the human role shifts from doing the task to governing the system. For legal professionals, the same transition is coming. The key question is no longer only whether AI produces useful work. It is whether lawyers have built the systems, context, safeguards, and verification layers needed to trust the work, defend the work, and remain accountable for the work.Listen on mobile platforms:  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ |  ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | Substack[Special Thanks to ⁠Legal Technology Hub⁠ for their sponsoring this episode.] ⁠⁠⁠⁠⁠Email: geekinreviewpodcast@gmail.comMusic: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Jerry David DeCicca⁠⁠⁠⁠⁠⁠⁠⁠⁠

Microsoft Build 2026 announced an end-to-end agentic AI stack. COMPUTEX Taipei confirmed heterogeneous AI infrastructure across ARM, Marvell, Intel, Qualcomm, and NVIDIA. Alphabet raised $80 billion. Cisco Live repositioned the network as the AI platform. Patrick Moorhead and Daniel Newman break it all down alongside earnings from Broadcom, HPE, Palo Alto Networks, and CrowdStrike, plus the token cost conversation, the edge AI push, and what Palantir and Oracle are saying about proprietary data as the real AI moat. The handpicked topics for this week are: Microsoft Build 2026 Announced an End-to-End Agentic AI Stack: Microsoft shipped MAI-Thinking-1, its first homegrown thinking model, alongside Scout, Microsoft IQ, Project Solara, and a Majorana 2 quantum update targeting a 2029 commercial timeline with claims of a 1,000x reliability gain. Pat describes MAI-Thinking-1 as likely better than Sonnet 4.6 in blind testing and delivering close to GPT 5.5 quality at a far lower cost. Scout is Microsoft's first autopilot agent, anchoring the M365 Agent Suite with Office Pilot Agent Mode and Agent 365. Microsoft IQ serves as the context layer, integrating M365, business data, boundary IQ, and web IQ with GitHub Copilot, Foundry, and Copilot Studio. Project Solara is a new Android-based platform built for agent-first devices across transportation, retail, and hospital settings. Microsoft also added 83 Unix commands to the Windows stack. Dan frames Microsoft's real play as distribution, not frontier model development, noting that the open model ecosystem being pulled into the platform will matter more to CFOs managing token costs at scale. (The Decode) The AI Stack Goes Multi-Silicon — COMPUTEX Taipei 2026 Confirms Heterogeneous AI Infrastructure: ARM's AGI CPU is in production with Google moving its TPU head node to ARM, and adding Oracle and ByteDance as new customers. ARM also introduced a new switch, the TT100, and put the 51T CPO switch on stage. Marvell received a trillion-dollar company endorsement from Jensen Huang, adding $90 billion in market cap on the comment alone. Intel announced disaggregated inference details and Xeon 6+ Clearwater Forest, its first 18A data center processor. Vista Equity and Cambium Capital announced a NeoCloud called Vector Core Compute, with Xeon 6 handling orchestration, Salmonova RUs handling decode, and Blackwell GPUs handling pre-fill. Qualcomm's Cristiano Amon announced the Dragonfly data center brand with Snapdragon C details coming at their June investor day. The WSTS raised the 2026 semiconductor TAM forecast by 90% to $1.51 trillion, with Pat noting the market could hit a trillion dollars if memory is excluded entirely. (The Decode) NVIDIA RTX Spark and the Edge AI Push: NVIDIA coordinated with ARM and Microsoft around the RTX Spark at COMPUTEX, with the shared message being that the future of Windows is here. Signal65's Ryan Shrout asked Jensen directly why NVIDIA wants to be in the PC business, given low margins and diminishing returns. Dan frames the answer in the context of devices increasingly becoming mobile data centers, capable of running models at much greater efficiency than cloud delivery. The edge AI conversation is also directly tied to token cost economics: as intelligence delivery moves closer to the device, the cost per token drops significantly. The jury is still out on whether NVIDIA will meaningfully disrupt the PC market, but its influence over OEMs like Lenovo and Dell that depend on it for data center gives it real leverage over SKUs. (The Decode) Token Economics and Frontier Model Cost Pressure: Dan and Pat discuss a substantive shift in how enterprises are thinking about AI consumption costs. Dan argues that "token maxing," the practice of defaulting to the most powerful frontier model for every task, has now effectively peaked, as bills have come due at scale. Companies paying for tokens in volume are starting to question whether they can afford the prices that frontier models actually cost to deliver. Pat pushes back, saying the dynamic is still present, but both analysts agree that the market is moving toward a model where token selection is matched to the job, with Microsoft's MOE approach and thinking models positioned to help CFOs manage that economics story. (The Decode) Continuum Goes Public at Highest Valuation for an AI Platform: Dan notes that Continuum, the Honeywell-spawned quantum company, went public this week at what he calls the highest valuation for an AI platform to date. He flags that IonQ will likely contest that characterization. The broader context is Microsoft entering the quantum conversation with Majorana 2 at Build, a name that has largely been absent from the quantum race, while IBM has received most of the attention. (The Decode) AI CapEx Has Outgrown Cash Flow — Alphabet's $80 Billion Equity Raise: On June 1, Alphabet announced an $80 billion equity capital raise, upsized to $85 billion, structured as $40 billion ATM, $30 billion underwritten, and a $10 billion private placement with Berkshire Hathaway anchoring. Pat frames the questions over CapEx returns as entirely dependent on whether you are an AI boomer or a doomer: if the payback comes, the raise is the right move. If it does not, the math doesn't close. Dan argues the investment is existential, drawing parallels to how infrastructure-first companies have always spent ahead of monetization, and notes that Google's equity is being used as a capital engine that may be more efficient than the debt markets right now. Both analysts flag the downstream implications for Broadcom, MediaTek, and Marvell given the TPU connection. (The Decode) The Network Becomes the AI Platform: Cisco Live 2026: Cisco launched Silicon One P200, the Secure AI Factory with NVIDIA and Spectrum X, AgenticOps, MCP-native automation, Cisco IQ, LiveProtect, and folded Astrix Security and Galileo into Splunk under one control plane. Pat identifies Cisco Cloud Control as the biggest announcement of the entire show, pulling together Catalyst, Meraki, Nexus, Firewall, and WebEx under agentic ops that run natively through MCP, with code running directly on smart switches that have x86 processors. Pat also credits Cisco for establishing Silicon One as a credible chip alternative for hyperscalers capable of taking on Tomahawk and Jericho. Dan frames the long-term opportunity as campus and branch enablement when industrial AI and robotics deployments accelerate, arguing that the numerator of AI's economic impact has barely started, as edge deployment spending has not yet begun. (The Decode) The Flip: Did Microsoft Build 2026 Effectively End the OpenAI Partnership? Pat argues the divorce decree has been filed. MAI-Thinking-1 was built with zero distillation from third-party models offering clean enterprise data lineage, with Maia 200 in production plus Anthropic chip supply, which signals vendor hedging. OpenAI is going all-in on AWS, which means you cannot be married to two people, and the full Build stack covering model, OS containment via MXC, agents via Scout and Agent 365, and context via Microsoft IQ removes every architectural dependency on OpenAI. Dan counters that Microsoft is hedging rather than leaving and predicts the partnership will run through the decade. Enterprise Copilot customers are explicitly showing in data that they demand GPT 5.5, internal benchmarks have not been independently validated, and Microsoft stands to make meaningful money from the OpenAI IPO. (The Flip) Broadcom Q2 FY26 Earnings: Broadcom posted revenue of $22.19 billion, a narrow miss depending on which consensus data set is used, with EPS of $2.44 beating estimates and AI semis at $10.8 billion. Hock Tan declined to raise the $100 billion full-year AI chip target, and the stock dropped 13% in premarket trading. Q3 guide came in at $29.4 billion. Pat calls the miss a timing issue driven by Google's multi-sourcing across Marvell, MediaTek, and Broadcom rather than a fundamental problem. Dan flags that Hock Tan opened the earnings call by accidentally reading from the 2025 print, calling it "not the best moment." Sell-side re-ratings held in the 500s across Jefferies, Mizuho, and Deutsche Bank despite the drop, with Futurum Equities having it at 600. (Bulls and Bears) Hewlett Packard Enterprise Q2 FY26 Earnings: HPE delivered revenue of $10.68 billion, up 40% year over year, and EPS of $0.79, up 100%. Juniper integration and AI servers both outperformed, and all FY26 guides were raised. The stock jumped 19% after hours before settling into a roughly 15% gain, with HPE up 68% over the last month. Pat frames HPE as a value play rather than a volume play, methodically targeting enterprise and sovereign cloud deals where it can maintain profitability, rather than competing for massive NeoCloud volume. Antonio Neri was clear on the call that the profitability pull-forward is a one-shot deal. Pat and Dan will both be at HPE Discover the week after next to interview Neri and the C-suite. (Bulls and Bears) Palo Alto Networks Q3 FY26 Earnings: Palo Alto posted revenue of $3.0 billion, up 31% year over year, beating the $2.94 billion estimate, with non-GAAP EPS of $0.85, beating the $0.79 to $0.81 range. NGS ARR reached $8.1 billion, up 60% year over year, including $1.6 billion from CyberArk and Chronosphere. RPO hit $18.4 billion, up 36%. Both FY26 revenue and EPS guides were raised. Adjusted FCF margin came in at 38.5% TTM, up 430 basis points. The stock jumped 11% immediately after hours, then drifted lower. Pat points to 2,200 platformized customers and 120% net retention as the most important metrics. Dan notes the SaaSpocalypse thesis continues to be wrong. (Bulls and Bears) CrowdStrike Q1 FY27 Earnings and the Proprietary Data Moat Argument: CrowdStrike posted revenue of $1.39 billion with EPS of $1.10 and ARR of $5.51 billion. Net new ARR of $255.8 million set a Q1 record, up 32% year over year. FY27 net new ARR guide was raised by $52 million to a $1.29 billion midpoint, and FY27 revenue was raised to $5.915 to $5.959 billion. A 4-for-1 stock split was announced effective July 2nd. The stock dropped 11% despite the beat after a 64% year-to-date run into earnings. Dan uses the results to make a broader argument against the software disruption thesis, referencing Palantir CEO Alex Karp daring customers to build without him using Anthropic or OpenAI, and Larry Ellison's argument that the real AI value unlock sits in proprietary enterprise data that is not accessible to frontier models. Enterprises with governed, secure, proprietary data will continue to need platforms like CrowdStrike regardless of what frontier models can do. (Bulls and Bears) Six Five Summit is coming. Salesforce CEO Mark Benioff will kick off the event. Register and stay current at sixfivemedia.com/summit. Watch the full video at sixfivemedia.com, and be sure to subscribe to our YouTube channel so you never miss an episode.   The Decode Microsoft Declares Independence — Build 2026 Ships an End-to-End Agentic AI Stack (MAI-Thinking-1 + Scout + Microsoft IQ + Project Solara + Majorana 2) https://www.theverge.com/tech/941738/microsoft-build-2026-biggest-announcements The AI Stack Goes Multi-Silicon — Computex 2026 Confirms a Heterogeneous AI Infrastructure (ARM + Marvell + Intel ASIC + Qualcomm + RTX Spark); WSTS Raises 2026 Semi TAM Forecast 90% to $1.51T https://www.tomshardware.com/tag/computex AI Capex Has Outgrown Cash Flow — Alphabet's $80B Equity Raise Is the Largest in U.S. Corporate History; Berkshire Anchors $10B https://abc.xyz/investor/news/news-details/2026/Alphabet-Announces-Proposed-80-Billion-Equity-Capital-Raise-to-Expand-AI-Infrastructure-and-Compute-2026-b0myAMewCa/default.aspx The Network Becomes the AI Platform — Cisco Live 2026 Launches Silicon One P200, Secure AI Factory (with NVIDIA), AgenticOps, Astrix Security + Galileo https://www.cisco.com/site/us/en/about/whats-new/index.html The Flip Did Microsoft Build 2026 Effectively End the OpenAI Partnership? MAI-Thinking-1 Beats Sonnet 4.6 in Blind Testing, Microsoft Claims GPT-5.5 Parity at 10x Cost Efficiency — Will MS Quietly Wind Down OpenAI Exclusivity by FY28, or Is OpenAI Still the Frontier Anchor Microsoft Needs?   FOR:  MAI-Thinking-1 beating Sonnet 4.6 in blind preference + GPT-5.5 parity at 10x cost efficiency is a frontier-model independence proof point https://www.latent.space/p/ainews-microsoft-build-mai-thinking Build 2026: Accumulating Evidence of Microsoft's AI Independence — EDN (June 4) — https://www.edn.com/build-2026-accumulating-evidence-of-microsofts-ai-independence/ Maia 200 in production + Anthropic-Maia chip talks signal Microsoft is hedging its inference vendor stack https://blogs.microsoft.com/blog/2026/01/26/maia-200-the-ai-accelerator-built-for-inference/ Microsoft canceled Anthropic's internal software licenses + pivoted to chip-supply pursuit — customer-not-competitor positioning https://www.cnbc.com/2026/05/21/anthropic-microsoft-maia-200-ai-chip.html   AGAINST:  Enterprise Copilot customers explicitly demand GPT-5.5 — internal benchmarks don't replace the brand https://learn.microsoft.com/en-us/microsoft-365/copilot/release-notes?tabs=all MAI-Thinking-1 benchmarks haven't been third-party verified — Microsoft is the only source https://www.latent.space/p/ainews-microsoft-build-mai-thinking The MS-OpenAI partnership is contractual through 2030+ — unwinding it is impractical and expensive https://blogs.microsoft.com/blog/2026/04/27/the-next-phase-of-the-microsoft-openai-partnership/ Microsoft's actual strategic risk is OpenAI leaving, not MS leaving — Anthropic + OpenAI IPOs make OpenAI exit risk the real concern https://www.anthropic.com/news/confidential-draft-s1-sec Bulls & Bears Broadcom (AVGO) Q2 FY26 ACTUALS — Rev $22.19B (Narrow Miss) + EPS $2.44 (Beat); AI Semis $10.8B; Hock Tan Refuses to Raise the $100B Full-Year AI Chip Target — Stock −13% Premarket; Q3 Guide $29.4B https://www.cnbc.com/2026/06/03/broadcom-avgo-earnings-report-q2-2026.html Hewlett Packard Enterprise (HPE) Q2 FY26 ACTUALS — Blowout: Rev $10.68B (+40%), EPS $0.79 (+100%); Juniper Integration + AI Servers Both Outperform; FY26 Guides All Raised; Stock +19% AH https://www.businesswire.com/news/home/20260601866494/en/HPE-Reports-Fiscal-2026-Second-Quarter-Results Palo Alto Networks (PANW) Q3 FY26 ACTUALS — Beat-and-Raise: Rev $3.0B (+31% YoY, Beat $2.94B), Non-GAAP EPS $0.85 (Beat $0.79-0.81); NGS ARR $8.1B (+60% YoY, $1.6B from CyberArk + Chronosphere); RPO $18.4B (+36%); FY26 Revenue + EPS Guides BOTH RAISED; Adj FCF Margin 38.5% TTM (+430 bps); Stock +11% Immediate AH, Then Drifted Lower https://www.paloaltonetworks.com/company/press/2026/palo-alto-networks-reports-fiscal-third-quarter-2026-financial-results CrowdStrike narrowly beats estimates on AI tailwinds, but stock falls 9% — CNBC (June 3) — https://www.cnbc.com/2026/06/03/crowdstrike-crwd-q1-2027-earnings.html  

Join Tyler Wells, Co-founder and CTO of BrainGrid, for a forward-looking discussion on how artificial intelligence is rewriting the rules of product development. Boasting over 25 years of distributed systems engineering—including a foundational tenure at Skype building Facebook's first video-calling engine and 7+ years directing Video and global SRE at Twilio—Tyler has built infra where structural failure was not an option. In this episode, we explore why the traditional constraints of software engineering—headcount, timelines, and budgets—are dissolving, leaving a brand-new bottleneck at the front of the innovation cycle: human imagination.

Lien pour réserver ton appel pour Le Tremplin : https://ecole.nevesformation.com/call-tremplinTu bosses plus que jamais mais ton business stagne ? Le problème n'est presque jamais technique, il est dans ta tête.Dans cet épisode Chill Business Talk, je déballe sans filtre tout ce que j'ai changé ces derniers mois : le système qui me fait rentrer ~100 clients/mois en quasi-automatique, pourquoi j'ai multiplié mes prix par 3, et le plafond invisible qui t'empêche de passer au niveau supérieur.Dans cette vidéo, tu vas découvrir :✅ Le Low Ticket Funnel qui me ramène ~100 clients/mois en automatique (et combien me coûte VRAIMENT un client)✅ Pourquoi j'ai ×3 mes prix et repris MES propres appels de vente (75% de closing, 0 closer)✅ L'auto-sabotage qui plafonne ton business — comment le repérer et le casser✅ Comment j'utilise réellement l'IA… et pourquoi je ne lui fais PAS créer mon contenu

This first highlights edition of the morning experiment tracks a week of fast-moving AI frontier news, from closed-door recursive self-improvement debates to OpenAI's call for independent model review. You'll hear why labs are betting on AI monitors, where safety plans still look thin, and how cheap scaffolds are already improving tax workflows. The episode also tests moderation progress and surveys AI science, cybersecurity, Vatican ethics, solo-business automation, and mental health support. Mercury: Run your finances with virtual cards, spending limits, merchant/category locks, and AI-friendly tools like API keys, MCP, and CLI. Check out Mercury at https://mercury.com Sponsor: Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr

Most recruiters think AI is coming for someone else's desk. Riece Keck isn't so sure. Two years ago he said AI wouldn't replace great recruiters, but it would replace the mediocre ones sooner than anyone wanted to admit. He still stands by it, and he thinks we're a lot closer now than we were then. He's also taking the stage at the AI Recruiting Summit 2026 to go even deeper on this, so if today's episode lands, that's where you see it built live: https://ai-recruiting-summit-2026.heysummit.com/ This conversation is the proof. Riece built a tech recruiting agency to a $1.2M year, then watched it nearly die twice before it folded into a merger for a token amount. He stepped away during a brutal personal stretch, then cold-started the whole thing again in early 2025, and billed roughly $800K in his first year back without leaning on his old client list. The difference the second time wasn't more hustle. It was systems. Riece walks Benjamin through the no-code BD engine he built end to end: scraping job boards on a daily auto-run, identifying the right hiring managers, enriching the contacts, drafting the outreach, and dripping it out, all while he sleeps. He breaks down how he uses funding signals, new-hire signals, and backfill signals to reach companies at the exact moment they're hurting, and how he's now rebuilding the entire stack inside Claude Code with MCP connections so an agent runs his ATS and his sourcing for him. But this isn't a go-all-in-on-tech sermon. Riece is just as direct about the other lane: if your zone of genius is relationships, build the offline communities that pull you out of the LinkedIn noise everyone else is drowning in. The one move he says gets you crushed in 2026 is staying in the middle, the undifferentiated recruiter who takes a job order, fires off templated outreach, and hopes. You'll also hear how he turned strategic referral partnerships into a steady deal source, why persona-based messaging quietly outperforms everything else in the inbox, and what he'd tell a recruiter trying to go from average to elite right now. If you've ever watched your desk go to zero and wondered if there's another side to it, this one is for you. There is. The recruiters who reinvent now are the ones crushing it in 2028. This episode is powered by Atlas, the AI-first recruitment platform built to eliminate the admin that eats your day. Atlas captures every candidate conversation automatically and turns it into something you can actually use. Ask it who mentioned wanting a four-day week or who's open to relocating next year, and Magic Search pulls the answer from your entire database instantly. No keyword guessing, no digging through old notes. Atlas customers have reported over 40% EBITDA growth and over 80% more monthly billings after switching. Unlock your exclusive listener offer at recruitwithatlas.com. Also brought to you by Millee, the AI deal strategist that turns the gut feel of big billers into real-time guidance on every live process. Millee preps you before every call and drafts your high-caliber follow-ups so you lead from the first minute. Try it free at millee.ai.

Jörgen Wigh, CEO of Lagercrantz Group Lagercrantz Group has completed 90+ acquisitions over 20 years and never sold one. CEO Jörgen Wigh runs 85 niche B2B companies under a 22-person headquarters with no integration, no exits, and no value realization targets. This is Part 2 of 2. Part 1 covers the deal model, while Part 2 is the operating culture. Jörgen gets into how 85 autonomous companies are governed without a matrix structure, why this model exists almost exclusively in the Nordics, what makes a founder walk away from a signed deal twice, why Lagercrantz deliberately targets a 10% failure rate, and what he would do differently starting from scratch today. What You'll Learn How Lagercrantz governs 85 autonomous companies with 22 people at headquarters Why the person who sources the deal always stays on the board post-close Why the Nordic compounder model exists here and almost nowhere else What makes a founder walk away from a signed deal twice What a 10% deal failure rate looks like when it's working as intended Why building this from scratch today takes at least a decade How cross-border deals get done when the legal contracts run 30 pages instead of 300 If you want to know how your team stacks up against the discipline Jörgen described across both episodes, take the M&A Competency Assessment. ____________________ This episode of M&A Science is presented by DealRoom. DealRoom just launched the only MCP server built for Buyer-Led M&A™ — so your AI and your deal data finally work together. Connect Claude, ChatGPT, or Copilot directly to DealRoom and let your AI read your pipeline, analyze due diligence documents, and automatically write findings back.  See for yourself: dealroom.net/mcp ____________________ Episode Chapters [01:14] Introduction and Part 1 recap [03:54] Deal governance: go/no-go process and board sign-off [04:31] No handoffs: why the deal sourcer stays on the board post-close [04:59] HQ structure: 22 people distributed across geographies [07:05] Why so many compounder platforms come from the Nordics [07:23] The cultural reasons: flat hierarchy, financial transparency, equality [09:19] Nordic management style versus US hierarchy [13:53] Cross-border deal friction: SPA length and legal complexity [24:43] Programmatic serial acquirer versus roll-up [25:18] The 100-day plan question: when Lagercrantz uses one and when it doesn't [25:59] The Bergman & Beving spinout ecosystem: six listed companies [26:45] Jörgen's role at Bergman & Beving and how conflicts are managed [29:57] Geographic expansion: Germany, Netherlands, DACH, Northern Italy [31:30] Starting from scratch today: why programmatic takes 10 years [33:01] EPS as the true long-term performance driver, not stock price [33:52] The perpetual ownership model and why it attracts certain sellers [34:17] The founder who backed out twice, patience won the deal [35:36] Failure rate: targeting 10%, what drives deals off course

#361 | In this episode, Matt Carnevale, Head of Community at Exit Five talks with three marketers doing impactful work in AEO. AI search is changing how buyers find products, and most B2B teams are still figuring out where to start. In this session, each marketer shares what's working and wins they've experienced — from earned media and technical audits to homepage fixes and tracking AI visibility. Whether you call it AEO, GEO, LLMO, or EIEIO – this one's for you. This session features guests Matt Dzugan, VP of Data Intelligence at Muckrack, Brett Bernath, Director of Product at Webflow, and Jess Joyce, Founder of Inbound Scope – an SEO and AI Search consultancy.Timestamps(00:00) - - - Why 80% of CMOs say AEO is a top priority — and most don't know where to start (02:48) - - - How Muckrack used original research to get cited in ChatGPT before their product launch (02:50) - - - Why top-of-funnel content is getting eaten by AI — and where to focus instead (02:53) - - - Quick win #3: authority — how to show up in Reddit and third-party platforms (02:56) - - - The sleeper tip: Bing Webmaster Tools is already giving you first-party AI data (03:07) - - - How to handle competitor comparison content without verifiable claims falling flat (03:23) - - - The four-bucket AEO maturity model: content, technical, authority, measurement (03:24) - - - Why your homepage is your worst-performing page for AI discoverability (03:27) - - - Quick win #1: technical hygiene — schema, meta descriptions, and structured data (03:28) - - - How to identify which journalists get cited most by AI in your niche (03:29) - - - Quick win #2: are you actually answering what your customers are asking? (03:34) - - - Why 1 in 3 B2B SaaS sites have technical blockers killing AI discoverability (03:36) - - - Why original research is the single best content type for earning AI citations Join 50,0000 people who get Dave's Newsletter here: https://www.exitfive.com/newsletterLearn more about Exit Five's private marketing community: https://www.exitfive.com/***Brought to you by:Optimizely - A no-code AI platform where autonomous agents execute marketing work across webpages, email, SEO, and campaigns. Learn how to deploy agents on your marketing team at Agents in the Mix. Learn more at optimizely.com/exitfive. Vector - A contact-level ads platform that lets you build audiences from actual people on your site, clicking your ads, and checking out your competitors. Learn more at vector.co, and get their new MCP server by clicking here. Customer.io - An AI powered customer engagement platform that help marketers turn first-party data into engaging customer experiences across email, SMS, and push. Learn more at customer.io/exitfive.Join us in Stowe, Vermont for Drive 2026 - three days away from your desk to learn what's working in B2B marketing from the people who are actually doing it. Grab your ticket at exitfive.com/drive.***Thanks to my friends at hatch.fm for producing this episode and handling all of the Exit Five podcast production.They give you unlimited podcast editing and strategy for your B2B podcast.Get unlimited podcast editing and on-demand strategy for one low monthly cost. Just upload your episode, and they take care of the rest.Visit hatch.fm to learn more

Ed sits down for a conversation with Matisha Ladiwala, Vice President and General Manager for Hyland's Content Intelligence Cloud (CIC), to discuss building, governing, and deploying domain-specific agents. They cover how governance helps AI pilots succeed through trust, and dig into Hyland's federation strategy, the agent mesh architecture, MCP as a semantic layer, and domain-specific knowledge graphs.

A lot of what we've been talking about lately is durable skills — the abilities that last regardless of how our tools and tech environment change. In today's episode, I want to step back from the AI conversation and focus on one of the most durable skills of all: feedback. We've all been on both the giving and receiving side, and we can probably count on one hand the times someone gave us feedback that genuinely drove a good change — that left us wanting to do better without feeling torn down. So how do we accomplish that kind of feedback, on both sides of the table? That's what this episode is all about. Start With Your Goal, Not Your Frustration: Before you give feedback, recognize that your gut impulse often comes from a negative emotion — frustration, feeling slighted, feeling disrespected. Those feelings are valid signals that something is off, but they aren't a sufficient reason to give feedback. Effective feedback is goal-oriented: ask yourself what you actually want to change before you say a word. Premature vs. Mature Feedback: Premature feedback is really about making sure someone knows how you feel — which can quietly turn into an attack so they share your pain. Mature feedback is forward-looking and aimed at improvement. Venting may give you catharsis in the moment, but if the behavior worsens or the relationship is damaged, the net outcome is negative. Why Asking for Feedback Changes Everything: Even hearing "can we meet for ten minutes, I have some feedback" measurably raises your heart rate and pushes you into a defensive state. But when you ask for feedback, your mind and body register that you're in control — same information, completely different physiological response. Make It Behavior-Based and Specific: Good feedback is about observable behavior — what a camera would have caught — not someone's core identity. If your feedback violates a person's self-concept (painting a competent engineer as incompetent), they have to change who they believe they are to accept it, and that gap rarely gets bridged in a 30-minute call. Use a Model — But Add the Intervention: The popular SBI model (Situation, Behavior, Impact) is a strong backbone, but it stops short. Don't just describe the past — partner with the person on what comes next. Think of it as SBI + Intervention: what can you commit to trying differently so the impact changes? That's where feedback becomes coaching. The Netflix Four A's: Aim to assist, make it actionable, show appreciation, and accept or discard. Lead with the intent to help, get specific about the behavior, appreciate the person's willingness and intent, and recognize that not every piece of feedback will be useful — both sides get to keep what's valuable and let the rest go. Receiving Feedback Well: When someone hands you messy, un-modeled feedback, you can walk them through the framework — "help me understand the situation, what behavior did you see, what was the impact?" People respect that you're engaging, shift into problem-solving mode, and give you more actionable feedback as a result. Episode Homework: Pay attention to patterns over time. One piece of feedback shouldn't be attached to your identity — but three or four that point in the same direction are worth introspecting on. Career development and feedback are two sides of the same door; walk through it and you grow.

This episode and the Identity at the Center podcast is supported by CrowdStrike. Learn more at crowdstrike.com.Jeff Steadman and Jim McDonald sit down with Scott Kriz, GM of Continuous Identity at CrowdStrike, for a deep dive into continuous identity, zero standing access, and the convergence of identity and security. Scott traces his path from co-founding Bitium, to selling it to Google Cloud, to building SGNL and ultimately joining CrowdStrike. The conversation covers how continuous identity works in practice, why traditional PAM and IGA fall short in a real-time world, and what the rise of agentic AI means for identity governance at scale. Connect with Scott: https://www.linkedin.com/in/scottkriz/Learn more about Crowdstrike: https://www.crowdstrike.com/en-us/platform/next-gen-identity-security/caep/?idacConnect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at http://idacpodcast.com00:00:00 Introduction and welcome00:01:21 How Scott got into identity and co-founded Bitium00:03:55 Selling to Google Cloud and the inspiration for SGNL00:05:02 Continuous identity and zero standing access explained00:09:13 Defining continuous identity at CrowdStrike00:10:20 How continuous identity differs from PAM and IGA00:15:06 Data as the foundation for continuous identity00:19:29 Open ecosystems, Shared Signals Framework, and CAEP00:25:26 Agents, identity chaining, SPIFFE, SPIRE, and MCP gateways00:33:02 Identity inside CrowdStrike's broader security strategy00:37:27 Identity security budgets and ROI-driven purchasing00:40:04 Agentic scale and the need for automated identity controls00:43:39 The SGNL acquisition: what it means for both companies00:50:25 Zero trust as a real architectural framework00:54:00 Helicopter skiing, avalanches, and staying presentKeywords: IDAC, Identity at the Center, Jeff Steadman, Jim McDonald, Scott Kriz, CrowdStrike, SGNL, continuous identity, zero standing access, PAM, IGA, zero trust, agentic AI, non-human identity, NHI, SPIFFE, SPIRE, MCP, identity security, real-time authorization, cybersecurity

Ali Behrouz, grad student at Cornell and Google researcher, discusses his potentially transformative work on new architectures for continual learning in AI. His paper "Nested Learning," praised by Jeff Dean as a possible paradigm shift, enables models to adapt to new context while preserving core knowledge by updating different layers at different frequencies, inspired by human memory systems. The conversation also covers his latest work on AI "sleep" for memory consolidation, why he sees all deep learning as associative memory, and the profound implications of continual learning for privacy, alignment, and the path to AGI. Mercury: The fintech trusted by ambitious companies and individuals to run their finances, with virtual cards, spending limits, merchant/category locks, and AI-friendly tools like API keys, MCP, and CLI. Check out Mercury at mercury.com Sponsor: Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr

How do you intelligently surface access to your database? While at NDC Toronto, Richard spoke with Jerry Nixon about Data API Builder, Microsoft's tool that enables data professionals using Microsoft databases, including SQL Server, Postgres, CosmosDB, and MySQL, to provide an API layer with security, schema extraction, and governance policies. You can expose the API as a REST interface, a GraphQL interface, and an MCP server! This is a powerful tool for providing controlled access to data while still allowing for ad-hoc access. The potential is huge - you need to check it out! Links Data API Builder GraphQL Recorded May 7, 2026

In today's Cloud Wars Minute, I look at Microsoft's latest moves to help organizations deploy and scale AI agents without sacrificing control. Highlights 00:10 — In the latest updates to Copilot Studio, Microsoft has introduced a series of improvements focusing on visibility and governance, allowing users to expand automation while maintaining control. Regarding visibility, the new analytics viewer role provides read-only access to an agent's analytics page. 00:36 — On top of this, Microsoft has expanded its agent usage estimator to include Dynamics 365 agents, enabling users to forecast Copilot credit consumption across both Copilot Studio and Dynamics 365 from one place. Microsoft already enables users to embed Copilot Studio agents into workflows using its agent node function. 01:16 — Other updates to workflows are designed to enable scaling without introducing governance risk. Workflows can connect to a larger toolkit, such as MCP server-enabled tools, to make it easier to take actions in systems, yet still within the Microsoft Security Framework. Users can also utilize agents built in Copilot Studio to bring interactive app experiences directly into Copilot Chat. 01:48 — This means they can review data, update records, approve requests, or create assets, all without having to switch tools. These are just some of the updates that Microsoft has been working on throughout April, and I really enjoy following the trajectory of these updates because they illustrate to me the current stage of our collective journey with AI. 02:11 — It's clear that agents are integrated into many systems, and now is the time to scale them securely. So, if you're still considering when and if to introduce AI-driven practices into your business, major directional changes like this should serve as a cautionary tale. Visit Cloud Wars for more.

Send us Fan MailThis week's enterprise software announcements highlight how rapidly the market is evolving toward agentic architectures, semantic intelligence, and AI-driven operational orchestration. Anthropic expanded MCP with a framework designed for full-stack agentic applications, reinforcing the industry's push toward composable AI ecosystems. Meanwhile, Hubbl Technologies raised funding to position itself as an intelligence layer for the Salesforce agentic environment, while Salesforce continued broadening its AI footprint through Agentforce for Communications. Sage enhanced the Sage Intacct Suite with new capabilities focused on finance operations, and Sinch introduced a collection of AI agent features targeting customer engagement workflows. On the operational side, Typeface unveiled a marketing orchestration engine, while Blue Yonder announced new AI agents and mobile applications aimed at supply chain execution and workforce enablement. At the same time, Zendesk moved deeper into AI-powered customer support through its acquisition of Forethought, and Actian launched an AI analyst designed to transform business glossaries into a live semantic layer, signaling the growing importance of governed enterprise context for AI-native operations.In today's episode, we invited a panel of industry analysts for a live discussion on LinkedIn to analyze current enterprise software stories. We covered many grounds, including the direction and roadmaps of each enterprise software vendor. Finally, we analyzed future trends and how they might shape the enterprise software industry.Video: https://www.youtube.com/watch?v=NCxtpqQ_vIwQuestions for Panelists?

A fundamental structural shift underway is the movement of AI from isolated features to operationalized, production-level workloads in MSP tooling and client environments. This transition is not primarily about the capabilities of individual AI models but about their integration into existing operational platforms and workflows. Companies such as PDQ, Senteon, Domotz, and Zoom are incorporating AI agents directly into management layers, endpoint automation, and workflow orchestration, thereby increasing both the scope and complexity of AI impact. The locus of value is shifting from features to workflow control and integration, creating new demands for governance, consumption monitoring, and exit strategies. The most consequential development referenced is the transition in AI billing and operational models from static user or seat licenses to variable, usage-based consumption. He cites TechCrunch's coverage of GitHub Copilot's move to token-based billing and Semafor's reporting of Uber's rapid exhaustion of its 2026 AI budget in four months due to unbounded consumption by generative tools. F5's State of Application Strategy report is referenced to confirm that multi-cloud and parallel model operations are now common, with significant instances of AI-related security incidents already reported. Secondary developments reinforce this structural realignment of risk and accountability. PDQ, for instance, is expanding multi-tenant management and integration capabilities, while Senteon enables endpoint hardening and drift control directly in Rewst's platform. Domotz's MCP server allows AI agents to operate across 40,000 networks globally, and Zoom is packaging AI context protocol features for workflow automation. Each of these changes is designed to increase operational efficiency, but also expand the surface area for unintended consequences, elevated operational complexity, and potential budget overruns. For MSPs and IT leaders, the operational implications center on governance, spend control, and clear accountability over AI-driven tools and workflows. The risk is that without adequate monitoring, policy setting, and contractual clarity—especially around data portability and exit costs—MSPs may face liability for unplanned consumption, misconfigured automation, or governance gaps. The evidence indicates the need to proactively audit AI integrations, set usage thresholds, instrument logging and budgeting controls, and renegotiate vendor contracts to ensure service boundaries and oversight mechanisms are in place before workflows become too deeply embedded. 00:00 MSP Stack Resets  04:09 AI Needs Governance 06:45 Govern AI or Pay 09:22 Why Do We Care?  Supported by:  Nerdio Zero Networks   

I'm excited to work with Microsoft once again as the presenting sponsors of the AI Engineer World's Fair! We'll streaming live from MS Build today for a special crossover pod with our friends at No Priors and the one and only Satya Nadella. However we did not hold back with this interview - we asked all the burning questions about uptime and Copilot that we know you have in your minds. Lets go!For almost two decades, GitHub has been the home of software, where both open source and closed flow, through commits, pull requests, reviews, actions, etc.This ecosystem flourished as open-source maintainers and contributors would continue shipping code for the benefit of the community. However as coding agents began to ship mass quantities of code - growing 1400% in 2026, it marked a new era that was both extremely exciting and challenging for GitHub.While these agents help more people ship more projects, they also significantly increase the floor of how much code is shipped, how often it is shipped, how many people commit code, and basically orders of magnitude multiples in every dimension of GitHub infrastructure:Now GitHub inevitably experiences more pressure on their infrastructure which was originally designed around human developers moving at human speed. This has resulted in a very publicly notable uptime story:So it begs the question of whether current systems around code can absorb what AI produces. Can CI/CD keep up when every idea becomes a build? Can open source maintainers survive floods of AI-generated slop contributions? Can GitHub preserve the human social contract of software while becoming the operating layer for agents?Which brings us to the perfect person to answer these questions: GitHub COO Kyle Daigle. In this episode, he joins swyx to unpack what happens when AI doesn't just autocomplete code, but starts changing how companies operate, how open source works, how pull requests get reviewed, and how GitHub itself has to scale. We go deep on GitHub's internal AI workflows: micro-skills, WorkIQ, MCP, Slack, Teams, email, Copilot workflows, the new Copilot desktop app, CLI, cloud agents, and how Kyle uses agents to look backwards across company context before deciding what to do next. Kyle also reflects on GitHub's history building webhooks, APIs, Actions, npm, Dependabot, and Semmle, why the AI era is breaking GitHub in new ways, how Actions became a general-purpose compute layer, and what Copilot becomes after code completion.Full Video PodWe discuss:* Kyle's expanded role across GitHub* How AI got Kyle coding again after years in leadership* Why GitHub rolls out AI through existing workflows instead of forcing new tools* WorkIQ, MCP, Slack, Teams, email, and GitHub as company context* Why massive “mega-skills” are giving way to small, atomic micro-skills* How AI changes summarization, communications, marketing, and analyst work* Why former developers in leadership may have a unique advantage in the AI era* Kyle's “15 agents on Saturday” workflow* How Kyle built an AI-generated executive presentation for CRO/CFO teams* Why AI changes the chief of staff role without removing the human work* GitHub Actions, webhooks, arbitrary code execution, and secure agent compute* The npm acquisition, supply-chain security, 2FA, and token invalidation* Slop forks, vendoring, and whether AI agents change dependency management* What pull requests become when most PRs come from agents* Prompt requests, vouching, AI review, and trust in open source* What counts as a “developer” when AI lowers the barrier to building* GitHub Spark, low-code, and why GitHub refuses to hide the code* 14x commit growth, Actions load, databases, monorepos, and availability* Copilot's evolution from completion to CLI, desktop app, cloud agents, and SDK* Context, memory, rules, and making GitHub “act like Kyle wants it to act”* Ambient AI, OpenClaw, enterprise security, and the new operating system for agents* What swyx should ask Satya Nadella about Microsoft's AI futureKyle Daigle* LinkedIn: https://www.linkedin.com/in/kyledaigle* X: https://x.com/kdaigleTimestamps00:00:00 Introduction00:03:36 Why AI Got Kyle Coding Again00:07:04 Running GitHub with AI: WorkIQ, MCP, Slack, Teams, and Skills00:15:39 The Golden Age for Former Developers in Leadership00:17:31 15 Agents on Saturday and AI-Generated Executive Work00:20:20 How AI Changes the Chief of Staff Role00:21:45 GitHub's History: Actions, npm, Webhooks, and Open Source00:28:45 Slop Forks, Vendoring, and AI Dependency Management00:33:57 Pull Requests, Prompt Requests, and Trust in Agent-Generated Code00:41:21 GitHub Stars, 200M+ Developers, and the New AI Builder Wave00:45:15 GitHub Spark, Low-Code, and Why GitHub Still Shows the Code00:47:38 GitHub's Hardest Era: 14x Growth, Reliability, and Scale00:59:21 Actions as the Compute Layer for CI/CD and Automation01:02:04 The State and Future of GitHub Copilot01:08:24 Ambient AI, Background Agents, and the Future of the SDLC01:13:09 OpenClaw, Enterprise Security, and the New OS for Agents01:18:03 Build Announcements, WorkIQ, FoundryIQ, and Microsoft Context01:21:41 What Should swyx Ask Satya?TranscriptIntroduction: Kyle Daigle's Expanded Role at GitHub and MicrosoftSwyx [00:00:00]: We're here with Kyle Daigle, COO of GitHub. Welcome.Kyle [00:00:07]: Hey, thanks for having me.Swyx [00:00:08]: You're not just CEO of GitHub. People know you as that. You have a new role.Kyle [00:00:11]: So I have an expanded role now. I've been working at GitHub for thirteen years and doing all things developer. Joined as a developer myself. And now, I'm also responsible as the CMO of Developer for Microsoft. And so all the kind of learnings and passion for developers and how we work with them and how we communicate and how we bring our products to market, we're also bringing that expertise to the broader Microsoft ecosystem and helping every developer that uses a Microsoft product or would like to have a sort of similar experience that they've had with GitHub over the years. So it's a different role in some ways, but it's also just building on the experience that I've had at GitHub of just sort of tell the truth, be authentic, show people how to use it and then let the products speak for themselves. Now just doing that with, all of Microsoft.Swyx [00:01:09]: We'll be releasing this in conjunction with Build. You got lots of stuff planned, and we can sort of touch on that whenever it's appropriate. I think one of the interesting things is I rarely meet a COO who's also a CMO. I think you're a very outward facing and you're very confident publicly. That's rare. Do you actually view yourself as COO? What's What is your thing?From GitHub Developer to COO/CMO: Building the Platform and Operating GitHubKyle [00:01:33]: I think for me, it's been funny. The titles have always been, a— have always felt a little strange to me. I joined GitHub as a developer? I wrote so much of theSwyx [00:01:46]: Let's bring that up. You wrote the back ends?Kyle [00:01:48]: I was going through, I was going through, some old photos, when folks were talking about how things were being built or how there was a build GitHub. I built, webhooks and worked with teams building the API, built the platform layer. Anything that integrated with GitHub, up until really twenty eighteen, I built or ran the engineering teams. And that's kind of where my the beginning of my passion always was helping people build things, deliver them to, their customers. And so being a developer, building for developers was always super unique. In a— I think as my role expanded, it became my ability to talk to not just developers, but also enterprise customers or business leaders and have this translation layer. And then through all those years, GitHub has always operated pretty uniquely. Post-pandemic, working remotely was not as novel as it was when GitHub started in two thousand and eight. But all that expertise of running remote teams, doing it well, became this sort of bigger role, ultimately turning into the COO role of how do we operate GitHub in the way that GitHub's always operated after the Microsoft acquisition. And kind of so on from there. So like for me, I think the— I've, I still code. I love coding but the problem has always been, people. It's a much harder problem to both support our own employees, a harder problem to communicate to developers and enterprise buyers what we're building why it matters, ‘cause those are two very different messages. And so getting to work in the mix of COO, CMO, also just being a dev, I think is what's kept me at GitHub for so long.AI Workflows for Leadership: Commits, Retrospectives, and ContextSwyx [00:03:40]: Apparently, you have— your commits have gone up. What's this? What's going on?Kyle [00:03:45]: Rui's called me out pretty aggressively. So I think— as you can imagine, right, you can see my normal era of being a dev In the twenty thirteen, twenty fourteen era, and then moving into management, and then ultimately the COO role. I think what you see there is me, really getting back to coding thanks to AI. I— similar to, attaching problems between how to market and how to operate a business and how to code, I find, building agents and workflows that are connecting very disparate problems to be what's driving this. So that's, some of it's writing software. A lot of it is, connecting a ton of a different data sources to, help me out. But that is completely me really diving in on the AI side in trying out our tools, trying out everyone's tools, But building for me, building for the non-technical leader, though I'm technical and how we're, able to use these tools more than just the simple, call and response that I think a lot of the non-technical, your employers, you have to get— you have to use AI, and so everyone uses, ChatGPT or Copilot or Claude or whatever. To really get into, how is this going to help me out, it— I find that it's not the I need to write a blog post, I need to those simple examples. Helping people find the workflows of, “Okay, I need you to go through all the PRs today. I need you to go through everything that we've posted online. I need you to go through what we did the last three months. Go through all of my Obsidian notes for any mentions of this then go through my transcripts at work.” We use, Teams, so, using WorkIQ, go call that MCP server, grab all the transcripts, go through all the Slack, and then build me out the plan of, what this week's messaging actually was. That's something that was, impossible because for me, I find AI in a what most of this launch here is actually, less building forward. It's actually, a recursive loop backwards. I'm always looking at what had happened first. Go back through the week and tell me what we did, what worked, what didn't work? And then tell me in the next three or four days-What would you tweak based on this sort of like looking backwards and then looking ahead a little bit? I find that to be so much more valuable, especially for like non-technical, because that retrospection is actually LLMs are very good at that. Like finding all the patterns, pulling them out, and then applying that retrospection to just a couple of days or just like a short period of time. Is all a bunch of apps that I've built and launched a bunch of, internal tools. I use the new, GitHub Copilot app, the desktop app with workflows. Every time I crack open my laptop, it's running workflows for me. It's just a ton of different stuff and of course, it all ends up on, it all ends up on GitHub.Swyx [00:06:47]: Of course. That's where, that's where, stuff is hosted. Man, there's so much to ask you. I was going to leave the how do you run a company with AI thing at the end. I have to ask one— double click one thing. You said, you are looking back at the week. You're, you're understanding what happens. When you say we That's three thousand people. How?Rolling Out AI Internally: Skills, CLIs, and Company ContextKyle [00:07:09]: I think when we started rolling out AI internally beyond engineering, right? One of the things that I was really, passionate about is like we have to do this in a way where no one has to change how they work. I don't want to have to teach you a tool. I don't want to have to teach you something new. And so for us, we tried out a few tools. Most of them don't work because I got to get you on board? I got to teach you how to use it. What we've actually ended up doing is we've built like a set of skills internally. We have we each have our set of skills, and we've just been distributing even to the non-technical folks, the CLI. And then effectively, we're just giving it access to like read about everything that we're writing. So that's for us, that's usually GitHub, Teams, Email, and Slack. So Teams for, video chat, generally speaking.Swyx [00:08:03]: Teams and Slack?Kyle [00:08:04]: so we use Teams for video communication, but we don't use it for chat. W-we— GitHub for a long history, right? We're alwaysSwyx [00:08:13]: Also SlackKyle [00:08:14]: Talking about ChatOps and like everything is built into Slack. Like every command, every flow.Swyx [00:08:18]: So even though you have been acquired for I don't know, eight years nowKyle [00:08:22]: we stillSwyx [00:08:23]: You still use Slack?Kyle [00:08:23]: it's a purpose-built tool for us, and I think the reality is that moving off of it would be so bluntly expensive? Simply because all the tooling is, baked in with that paradigm. And they both have their pros and cons but they don't work the same way at all. We still use a bunch of different tools Because it's the purpose-built tools that We need. And thenSwyx [00:08:47]: Well, the same doesn't go for the rest of Microsoft, presumably.Kyle [00:08:50]: like the like various teams like operateSwyx [00:08:53]: They make their own decisionsKyle [00:08:54]: Various ways. I think it just matters what you're trying to what you're trying to do. But we do we do work across kind of every tool that we use, and then by giving everyone access to all of that context and the new WorkIQ MCP server, which is quite cool if you do live in the M365 like world. I can ask it all these backwards-facing questions, and it's incredibly important for our teams that are working remotely. There's a lot of stuff you miss when you're not in an office, and we are spread out all over the world. So most of that is looking back. And then we post, we post either auto-automatically into GitHub issues or discussions, these sorts of like findings or like our industry reports. Like what's happening this morning, today, yesterday. A little automation gets run. We'll use the app. We might use GitHub Actions like with, our agentic workflows just to go do that run, and then we push it into GitHub, and w-we keep having a conversation. So usually for us, it's about that sort of like looking back, looking forward on the non-technical side. And then of course for a lot of those folks, it's also building an app, pushing it to GitHub pages or pushing it somewhere to host it et cetera. But it's just like enabling everyone with that power of it's going to take me a week to figure this out. Instead, we're going “Okay I built a skill. Let's put it into a repo. We'll all share that skill together, and then we'll use the CLI or now the app-” “just to run it.”Micro Skills vs. Mega Skills: How GitHub Uses AI at WorkSwyx [00:10:26]: All right. I think, I think we're going straight into like the team management and productivity thing. I think a lot of people are getting various levels of LLM psychosis. How do you manage the bloat of skills? Like everyone Has their thing, and they're Like trying to promote it to the rest of their peers in their org, right? And obviously, whoever becomes a skill influencer internally becomes like an AI leader, right? Of sorts. I assume you have those.Kyle [00:10:50]: like I think we haveSwyx [00:10:52]: And I assume it's a mess a Yeah.Kyle [00:10:54]: there's like I— like I think the reality is there's two pieces. Like first is I think that we're ending the era of these like massive, beautiful, perfect skills that are just like not any of those things. ‘cause for a while, right every tweet every day is like go download the skills, the perfectly managed thing to do this entire workflow. And I think that like what we've found and what— I was just with my team, this week, and we were talking about the skill side, and we're really talking about these like incredibly micro skills that are just doing one thing for us very well Versus a skill that's going to do I said, that full report. That doesn't really exist on our side anymore. It's usually how do— like a single skill that's going to identify the most important marketing information given any MCP server. Like this is the most important thing. Less about stitch a bunch of tools together and have it produce this mega output because then weeks go by, months go by, things change, and you want to tweakSwyx [00:11:58]: It's brittleKyle [00:11:58]: Your mega skill and you're screwed? You can't do that. And so now we're really just talking about the Legos we're using and just letting the instruction book be something we're all putting together. Whereas I think a lot of AI skills for a while have been that mega instruction book style.Swyx [00:12:15]: I've, thought a lot about Postel's law. I don't know if that's a term that is, means things to folks. It's the idea that you should be liberal in what you accept and strict in what you output, right? And I think that's like a good framing principle for skills. This is my skills, obviously on GitHub. I feel like everyone should have like how like some repos In GitHub are special repos? I feel like we should sort of reify the slash skills and everyone like give it some kind of special presentation. Anyway, so, yeah, this is one of those like download Download anything, transcribe anything, and then you can string together the atomic skills that do one thing well Into like some kind of orchestration skill that calls other skills. I assume, does that match?Kyle [00:12:56]: I like I think so. I think that theSwyx [00:13:00]: Summarize anything.Kyle [00:13:01]: Like I think the- For me, summarizing something for I do communications and PR and analyst relations and marketing and customer activities, and so my summarize everything is very different for each one of those like Contexts. What ‘Cause if I'm summarizing something for an analyst, that's a very different thing than, probably how I'm going to summarize something for like a customer meeting or an engagement. So that's I think like the difference when we're talking about the like the tools I might use on Saturday or the skills I might use on a Saturday when it's just for Kyle. Yeah, those are kind of like they have an atomic actual tool underneath or maybe skill, and then Kyle cares about X. But I think when we're talking about work and enabling the the marketers, communicators there, it's the atomic, this is what good summarization is, and then this is what I care about as for marketing for communications For whatever. And that I think is like the interesting matrix problem when we go from like a developer set of concerns to all kinds of different professions, is that what that word means to me is different than it means to you is different than it means to the analyst or the salesperson, and that's where I think the matrix mess is that we're starting to like still starting to find. It's about these mega skills but they're all just slight permutations, but those permutations are really important. It's the difference between someone reading this and going “Did AI make this?” what Or “This makes total sense, and I would expect this when I'm giving a briefing to Gartner,” or like whatever else.Swyx [00:14:37]: I think the beauty of it maybe is that you don't have to be that careful about what goes in there. It doesn't have to exactly fit as long as it like roughly is contained in there. I used to complain about plugin hell, basically. Like when you have a framework and then you have a hundred things that you need to integrate, everyone does like the GitHub used to be bloated full of these things. And now we don't need them anymore ‘cause now you just use skills.Former Developers in Leadership: AI as a Creation MultiplierKyle [00:15:00]: And like I think the most magical thing is the just that like I can just also crack it open. Like Like yes, I could go like change the how the plugin is coded, or like I could go do that now with AI, but I think there's just something more magical about getting a response back and being “That's not right,” and then you just crack the skill open, you just type English words and it's different. That building block is just, I think very unique. Once I get everyone to kind of understand how to best how to best make those changes to get the most power out of them.Swyx [00:15:36]: Is there a— you have a your peer group that Of people like you. Is there a common framing for Something I'm feeling is, which is true, is that is this a golden age for former developers who are now in leadership? Because you can wield the tools, you would know the right words, you're maybe not too close to the details. Doesn't matter. But like you're more effective than someone who doesn't come from that background.Kyle [00:15:59]: I think that like the secret has always been your ability to identify patterns and solve problems, and I think that for folks that like myself that don't code day to day anymore, that has made me successful as a developer, made me successful as a COO and now CMO. And so now that I have access to get and write code, I'm now applying that sort of like pattern finding and problem solving, and I know enough still about how to then go and say, “Oh, I want to make an app, but I don't want to break into jail or create something that's not going to be able to work or to be deployed scale or whatever.” that ability to apply all that additional business knowledge and still code I think is what makes that so interesting to me. Slightly different than I think some of the other like technical leaders that became business leaders and now are going back to their apps and updating them. Good for them? But I think the more, much more interesting thing is, well, now I have this whole new set of expertise over ten plus years. Why not take that and use that as a developer with these AI tools? So I definitely think that makes me more powerful, but I think that's true for like every dev as well. Most of the dev friends I still have also have some other underlying skill and passion. There's really talented, very kind of linear computer science software devs, absolutely. I just find that the folks that came from a different career, went to school for something else, went off and did this random thing, and then became a software dev, or were a dev, did a random thing, came back. Learning that extra set of information, learning those extra skills, and now having the power of an AI where I can crank up fifteen agents on Saturday while my kids are doing lacrosse, That's like really powerful. And I think it gets me back to that feeling of like creation, and it's very hard to replicate that in most other senses? That first time you build an app and you click it and you show someone that's magical. And so being able to do that not just in code, but across all kinds of different assets that's, that's huge. We were doing we're doing our every year we do our revenue planning. We talk about okay, what is it going to look like for next year? And of course as you imagine, there's, slideshows everywhere talking about what are we going to talk about, what's the narrative, et cetera. And so as you said I'm “Okay, well, I could probably just like build something to build this and then that way I don't have to go build the whole spreadsheet or I have to pass it to my team.” So we went through this process, and I got all the information and used the skills I mentioned. I built like a little app just to make it so I could look at some of the information in a SQLite database, more easily. And I ultimately built this entire presentation without touching any of it and I was “Okay, I'm just going to present this to our CRO, the CFO, their teams,” without mentioning I'd built it with AI. I like built a skill to make it look very much not AI driven. Just not pretty.AI-Generated Presentations, Human Taste, and the Changing Chief of Staff RoleSwyx [00:19:03]: Like a design. Yeah.Kyle [00:19:03]: Not pretty. But just like very clearly not AI. Kind of like don't do anything interesting.Swyx [00:19:08]: That's, yeah, that is valuable.Kyle [00:19:08]: Just go Exactly. We did the whole thing through. It used my notes from Obsidian, it used all the context I mentioned before, the plans, and Never came up once that it was AI generated.Swyx [00:19:20]: It didn't matter.Kyle [00:19:20]: Never once. D It didn't matter. And so now I takeSwyx [00:19:23]: This is a toolKyle [00:19:23]: I can take that tool and go, “Look, I don't want you to go build slideshows.” They're just helping us share information with each other. If this thing can do it With a little bit of crafting from you and then we can look at it together, awesome. There's no value in all that extra work. I think that the ability to, make it look humanly bad and and build a little app to, manipulate the data I think is part of, that upside for devs that are now in leadership roles. Because, the thing that I feel like I said before, this that's all a people, that's all a people problem. I know if you've used a coworker or not to build a slide deck, unless you spent a bunch of time to not do it.Swyx [00:20:07]: I know, but like it was so, I think there's a certain charm to just being blatantly AI. ‘Cause I think that you're well, you're just honest about There may be mistakes here that I cannot vouch for. So how much value is there? But anyway I think, actually the real question I want to ask is, there's a— You were a chief of staff To Thomas. And in the pre-AI world, the that job would've been a chief of staff job of like Can you prep me these slides and all that? And now you do it yourself.Kyle [00:20:35]: I still, I still have a chief of staff. Because, the difference is it's sort of the discussion every time we have some sort of technology evolution is it's not that the jobs the roles don't all go away, they just change? And so yeah, I don't have someone spending all their time building out slides for me and presentations ‘cause I don't need that anymore. But now I need that person that is able to go and find all the different connections between humans in those discussions to help me find out, okay, I should be meeting with this group and this team, and they have an opportunity, and I'm going to be in San Francisco today, I'm going to be in Seattle tomorrow. Those sorts of human connection aspects are still incredibly valuable and has always been a big part of that chief of staff role. But now just like chiefs of staff are not opening up, letters to process, they're doing emails. What It's the same thing. And now they're, they're not building out as many of these presentations because they have the the ability to have a AI take it on for, and share that with me and great. Let's keep moving ‘cause it's allowing us to go faster and make better decisions more quickly.Swyx [00:21:45]: Awesome. Well, so we can dive into more sort of, Productivity insights as you go. I did want to do a little bit of a brief history of colleague and hub. Because, we started here. And then you also involved the NPM acquisition. I did, I do want to touch upon that. And then more recently, I just want to bring up to present day where we're having uptime issues Which transparently we've already Addressed publicly, but we'll, we'll discuss in the pod. Did I miss anything? Like what, any other major highlights? Obviously, it's, it's a lot of years to cover.A Brief History of GitHub: Webhooks, Actions, Acquisitions, and Platform EvolutionKyle [00:22:15]: No the I think one of one highlight was right before the acquisition closed in twenty eighteen, I got to launch the first version of ActionsSwyx [00:22:27]: OhKyle [00:22:27]: At GitHub Universe. So it was OSwyx [00:22:29]: They're that young?Kyle [00:22:30]: It was October of twenty eighteen, I think. Yeah. Yeah.Swyx [00:22:33]: Gee, Jesus.Kyle [00:22:34]: I got to I was the engineering leader on that project and got to launch that. And then, yeah, we did acquisitions of NPM you said, Semmle, Dependabot Pul Panda a whole bunch of things. That was a bigSwyx [00:22:47]: Pul Panda.Kyle [00:22:48]: Abi is doing well.Swyx [00:22:51]: DX. Holy crap.Kyle [00:22:52]: Did well on DX. I and like that was a that was the big shift, after the acquisition. I had to join the sort of business side.Swyx [00:23:00]: So I need to hit you on some of these things ‘cause you were there. Right? And how often do I get to talk to someone who was there? But yeah, Actions. Is that the number one source of security issues on GitHub?Kyle [00:23:11]: Oh, sh I think that the number one source of, security issues is probably like all, the literal code in everyone's like underlying repositories. I would say back further than that is, if you remember I had to show in this graph was this is, I'm, didn't say this before, this is ultimately webhooks.Swyx [00:23:30]: You yeah.Kyle [00:23:31]: Like circa whatever it was.Swyx [00:23:32]: It says Hookshot in there.Kyle [00:23:32]: I forget. Yeah. Yeah, Hookshot's in there. And so like back then, it says GitHub Services. Do you see, it says Hookshot FE for front end, and then it says GitHub Services. GitHub Services back in the old days, right? You we had a repository that was Ruby code, and you could write any Ruby code in there, and then we would execute that On your behalf As a service, and then that way if an if you were trying to integrate with something, it didn't we would run it for you.Swyx [00:23:57]: And of course no containers ‘causeKyle [00:23:58]: No, ‘cause it wasSwyx [00:23:59]: Well, no containersKyle [00:24:00]: Twenty fourteen. And so there was some isolation obviously, but it was mostly the separations on the server level. That's like an example as long as the very old version of Pages, which ran on its own containerization infrastructure, not on Actions.Swyx [00:24:15]: Which like all-time great product.Kyle [00:24:16]: Pages powers the internet at this point to some degree. Those were places where like clearly there were no like issues like to my knowledge. But it was those things where I'm looking at and going “Okay, well we can't be running arbitrary Ruby code,” like on everyone's behalf. Then containerizing all of that up intoUh into actions now where yeah the containerization, is r-really good. The pinning most folks aren't pinning it the like to a particularSwyx [00:24:48]: ImagesKyle [00:24:48]: Sha, et cetera like their workflows, and so that's a big that's a big place Of pain for folks if they're just doing similar to any dependency management, just V1 or newest or latest, I think. But, that journey from that day to “Okay, we're just going to run all this arbitrary code, and, it'll basically be okay,” to now, no, we have, really good containerization. We have a new, underlying, ag-agent, containerization, service. It's like we're using it under the hood. It's through Azure. They recently announced it. The Azure, Dev Compute, but it's, very fast, very fast compute to be able to, spin up your own cloud agents, or whatnot. We're using it under the hood for some parts of the new,Swyx [00:25:36]: Microsoft Dev Box?Kyle [00:25:37]: No. Dev Compute, yeah.Swyx [00:25:41]: Hmm. Not finding it just yet.Kyle [00:25:44]: Oh, it's, it's in there somewhere.Swyx [00:25:46]: All right. Well, we'll cut that out.Kyle [00:25:47]: Sorry. But with, Dev Compute, you can, run, really fast, spin up really, small VMs really quickly, so you're doing a tool callSwyx [00:25:58]: Same conceptKyle [00:25:58]: Just do it containerize exact-exactly. So we're using that so definitely moving that direction to protect us from every every piece of code that we're ultimately running.Swyx [00:26:07]: look, that grows into the full SDLC? Code hosting was just the start and and then it's grown beyond that. Let's talk about NPM may-maybe ‘cause I think that's also, a very major point in the industry. I do think, it was looking for a home. It was, kind of struggling as a business, right? I don't know, I don't know how you would characterize that whole acquisition and how itNPM, Package Security, and Keeping the Internet RunningKyle [00:26:33]: like when we were talking to the team, I think the big thing for the both of us was to find a way to keep NPM, which was basically powering the internet then and way more so now to some degree running. Keep it going keep continuing to scale. It was having scaling problems, if I recall, back at that time. They were doing some rewrites. ItSwyx [00:27:00]: that's cute compared to now.Kyle [00:27:01]: Well, that's the thing is like when I'm talking to folks now, there's there's so many more underlying uses of NPM than there were back when we had them join in with GitHub. But that was ultimately the goal. It was really okay, we used to have pages. We have, the world's code. Let's make sure that we can keep NPM running well for the world. And we put a bunch of time and investment into fixing some of the underlying backend, changes, some of which we talked about some of the manifest work, et cetera. And then now, really trying to bring the the security posture of NPM up to speed. But, it is a unique challenge in that every move that we make to make it more secure will break a lot of people. And security is paramount. And also, we take it very seriously. We're, the any time that we have a problem with GitHub or we make a change that makes us more secure but hurts, there's, a snow day for developers or a really bad fire that they have to go put out. And so we've, have changed the 2FA policies. We've changed the way the tokens work. When we find tokens that have been exposed or potentially, exposed, we invalidate them, andSwyx [00:28:22]: I love that feature in GitHub. Yeah, it's greatKyle [00:28:23]: That creates issues, but, the but that's the thing is we're trying to push the community, forward without necessarily, doing something that is going to break the contract that's been for 15 years or close to it or some amount of years on NPM.Slop Forks, Vendoring, and the Future of Open Source Supply ChainsSwyx [00:28:43]: I think the— So now we're talking about, open source and publishing. And I think there's something here with what people are calling slop forks, which, I think Malta from Vercel is doing. And, part of me thinks, well, the way to get past any vulnerabilities, we just, let's just get rid of the concept of NPM. And we only publish source code. And anytime you want to import it you have your coding agent look at it and then adapt whatever subset you're going to use into your vendor it. But, the AI vendor it. Is that realistic? I don't know. Is it— Will that solve all our security issues? I don't know.Kyle [00:29:24]: I don't think it'll solve I so Mitchell was just talking Mitchell Hashimoto Was just talking about this today, and I think that I-in some ways, it's all all things, old or new again? Yeah, absolutely vendoring everything. Like I do I do remember twenty thirteen, twenty fourteen.Swyx [00:29:42]: This is Yeah. Let's, we must return toKyle [00:29:43]: That's what is We were vendoring everything. We were having actual discussions around, or at least I remember we were “Should we take this full thing?” “Why is this so big? We only need this one file.” And so I do think there's something true there where having either taking only what you need or the dependencies just getting incredibly small over time, I think will help to some degree, but it's not going to solve the fundamental problem, I don't think, because the vulnerabilities in an agent looking at them, there's time and time again, there's a million different ways in which we can convince an agent that this thing is, secure or not and pull it in. Or we can do static code analysis or runtime testing to say whether the code works or not. That is, I think, the step that needs to continue to be, invested in. The question is just on, how much scope. Should it be this enormous project that I'm pulling down, or should it be this piece? Either most companies are running some amount of security checking on the on the packages that they're bringing in or vendoring. That I think won't change. That's like what advanced security does to some degree, Socket does some degree. Like everyone is doing a piece of that. How we each do that like especially when we're talking to enterprise customers, is just like very different. No there's no one wants one single way to do it. And I think that's always been GitHub's, unique position in the world. I talk a lot to maintainers, I talk a lot to folks about this. It's we're— we rarely start like a process and a practice and like push it onto the community. We usually wait for the sort of like RFC process socially or literally, everyone agreeing, and then we'll cement something in. Because otherwise we'reMaintainers, RFCs, Vouching, and the Social Layer of TrustSwyx [00:31:35]: That fits your role in the ecosystem, yeahKyle [00:31:36]: We're GitHub. Yeah, we don't want to shape the whole thing. We want it to be figured out. But like how do you balance that like sort of Role in the industry to keep everything as secure as is possible and make sure that you're you're not going to be compromised as a human, ‘cause that's usually how it all happens. And Not not create a process or lock us into a flow that you're not going to or like Mitchell's not going to or other open source projects aren't going to like. That's always been a tricky balance for us, and I think that's something that we haven't talked about enough is we're not going to be able to fix everything for everyone in a way that everyone is going to like. So tell, help us, tell us what is working. When Mitchell was talking about, the Upvote, the upSwyx [00:32:22]: I was going to bring up his thing. Yeah.Kyle [00:32:23]: I forget what it Yeah. When he's talking to us, I was chatting with him and talking to him about this and I put it on Twitter and we talked to, also over DM, was “We're going to keep working.” but I think the important thing is I do actually want to hear what isn't working for you. And as, be as specific and clear for your project as is possible. And to every piece of credit over the many years that we've known each other through the industry, he's always done that and I appreciate that ‘cause there are places that we need to fix up, and we hear from him, and we'll fix up just like we do all other kinds of maintainers. But that that process between making those types of improvements and being more secure and like creating, I forget what he calls it's not the proof process, not the claims process. Do what I'm talking about? He has that he his projects have a way for you to kind of like,Swyx [00:33:13]: VouchKyle [00:33:13]: Vouch. Thank you. Yeah. He has like the vouch system for saying, “Hey, you should accept my PRs.” That's beenSwyx [00:33:20]: I just built this into GitHub. I don't know.Kyle [00:33:22]: Well, see, but that's the thing is that you say that and like he and his community really likes this and then I'll go talk to other maintainers and other maintainers, globally, and they're “No, this doesn't work for me.” And that is the tension, but also the kind of beauty of GitHub, depending on which way you look at it is we want to help maintainers, so we create all these tools to let you have more control over how much you take in from AI and PRs. But you can also use this. What You can go use this project, and if it takes off and becomes the kind of mostly standard, then yeah, we probably wouldn't enforce it but we would add it in because that's the flow that we tend to do?Swyx [00:34:02]: I hear a lot of people don't know the history of the pull request. And like like that's how, that's something that GitHub standardized basically.Kyle [00:34:08]: Yeah. It was a very messy process Like beforehand, and now the we have the benefit of it being the process? And now we have to go and Figure out the next best process or what adaptations change, or what does a pull request look like when eighty percent of your PRs are just coming from your agents and not From other devs?Swyx [00:34:31]: Do you like the prompt request idea from Peter?Kyle [00:34:34]: like I think that for each like each idea I think has its merits. I'm not, I'm not avoiding saying anything good or bad, but I feel like I've seen a version of we have that we have entire Thomas' store. Take all the assets of what you've built and put that in. I think that's got great ideas. There's all these various permutations of the PR flow, but I think the reason why there's not a single answer is ultimately we're trying to codify trust. We're trying to say “Okay, if Sean reviews this I'm going to trust it because you're Sean or you're the senior dev or you're the whatever.” And right now, when we are working in a flow where an agent writes code and another agent reviews code and then Kyle goes and looks at it the trust is kind of diffuse. And most of the tools that we're talking about are talking more about verification flows. We have more assets to look at, so I can probably say whether this is a good PR or not. But that still doesn't solve, I think, the human problem of I'm looking at a PR and I want to know if I can trust it. And we're still, we still tend to use human signals for that? Mitchell approving it or Kyle approving it or whatever. And so I think that's, I think that's why most of these options haven't really solved it is because, it's a social problem ultimately. It's a it's a human problem to review it and agree. Or you fully trust the tool and you're imbuing that tool with full trust Which I think in some cases that absolutely exists.AI-Generated PRs, Trust, and the Waymo AnalogySwyx [00:36:08]: And so like in the same way that there will be a tipping point in society when we don't allow humans to drive anymore Because machines are measurably better than Than humans. I'm looking for that tipping point, right? Like Mythos is ridiculously expensive. Someday we'll have Mythos on a desktop. I don't know. Will, does that change the equation?Kyle [00:36:30]: I think it's more I took a Waymo here, and I was on my phone and not looking around at all. There are other, self-driving, vehicles that I would not trust while, staring at the road. And I think that trust is something that isSwyx [00:36:48]: Is this a Zoox thing? What is itKyle [00:36:50]: I think that is both. I think that is both. LikeSwyx [00:36:53]: There's Zoox in this robo taxi. That's it. It'sKyle [00:36:56]: Well, depending on what level Of self-driving. But, my point is sort of that I think part of that is I strongly believe that's, a mixture of verifiable proof. Like how many accidents, how much data, and so on, and the human aspect of how I feel when I'm in this car, what it tells me, et cetera. And so that's why I think some of the like Some of these some of our AI tools tend to, imbue me with more of that feeling of trust, even if the data says this is 100% accurate. I feel like it takes more time for us to go, “Should I trust this or not?” And that's in the soft sense of, startups with high agency, weekend projects, and open source. And then there's enterprises and regulated industries and everything else, and that is an even harder problem to go solve because even when it is fully verified, not only do you have to have trust from the humans on the team, you probably have to have trust from multinational,Swyx [00:37:55]: Oh my GodKyle [00:37:55]: Multi governments around the world and regulating agencies. And so that's where I feel like until we tip over to your point on the sort of like human EQ side of it. I feel okay this feels okay I've been proven enough. Then the ball will start to roll a lot faster, where we'll end up getting to the “Okay, we can trust this,” and feel good about it in the Most difficult of cases.Reputation, Sponsors, Stars, and Bot Activity on GitHubSwyx [00:38:18]: If human trust is the thing that matters, I feel like GitHub as the developer social network could maybe do more there. Like vouchers are one system But, we have star counts, and then we have Contributor rights, and that's it. And I feel like there should be more in that space. I don't know if there's any other design decisions there.Kyle [00:38:37]: I think that one of the places that we don't really expose right now in this sort of way is, some degree of like hard trust and support, which would like for me is like sponsors is a good example of that.Swyx [00:38:49]: Ah.Kyle [00:38:49]: It like costs you something. To prove that I believe in your project and I trust you To some degree or I want to support you at the very least.Swyx [00:38:56]: Solve payments for open source. Why not?Kyle [00:38:58]: I think that I think that like as we keep moving forward, right, there's more and more projects where I'm, adding more and more dollars into sponsors personally because I want to like support them, but I also like know of I've probably never met them in person, but, I know of enough of their work that I want to support them. I think the thing that I don't love about stars or commit counts or anything else is ultimately, even with all of the various, abuse and de-spamming and deduplication work that we do or anti-abuse work that we do, these are all, not active social signals. They're passive ones that are ultimately gamifiable. And you may trust me, but another open source maintainer may not. And on what heuristic should you be, trusting me? That I think, is kind of where some of our thinking is right now. What signal from me is most important to you? You— If you can define that potentially, honestly in an agentic workflow that's what we see some of these open source projects do, where you have GitHub actions, and then you have like an agentic workflow that's calling AI, and you're setting these rules. Like if Kyle has submitted and gotten accepted PRs across any given project and has a social handle tied to his account in GitHub, and that social account's older than a certain amount. Really complex measures that matter to you ‘cause most open source projects have that heuristic built into their heads, if not written down in the contributing guidelines. You could take that and then go apply that and then just say, “Oh, we're not going to accept this PR.” Building something that is, I think, malleable to everyone's needs, is a little bit better, rather than going “Hmm, this account's too young.” Because what happens? The attackers just go and go and create a multitude of accounts, and they wait Until it ages up. Needs to have a certain amount of stars. That's how star inflation happens. Need to have a certain amount of reposSwyx [00:40:46]: Oh my God. YeahKyle [00:40:47]: With PRs. They all just create repos and submit PRs to each other, and then they come in and do something nefarious. And so, it's hard. It's hard to find the measure. So I think we're, we're looking more at how can we provide you tools so you can kind of choose what's best for you. And of course, we'll give you some standards. But the trust vector, gets down to I don't know, some version of like human digital ID like everyone's been talking about. Like how do I prove that it's meSwyx [00:41:13]: Give me your eyeballsKyle [00:41:14]: On the internet. Give me your eyeballs. Exactly.Swyx [00:41:18]: The I got to keep moving on Topics, but obviously I can go all day on this stuff because, I've been involved in GitHub and open source My entire professional career. Stars. Very superficial. Everyone knows it. But I think time to one hundred thousand stars is the fastest I've ever seen. Like people just reached that in I don't know, months. And then like at the same time I don't trust it right? Like how many of these are real or bot or like whatever. I don't know how to ask this but like what can we do about it? LikeKyle [00:41:49]: JustSwyx [00:41:49]: Is stars broken? Is stars fine?Kyle [00:41:51]: I think that there's kind of two, there's like two pieces. Obviously we're constantly like trying to find ways in which like your users are producing spam, which would, I would include like be like only doing star gamification. When we find them, we pluck ‘em out and we,Swyx [00:42:08]: But it's like a Whac-A-MoleKyle [00:42:10]: It's a hundred percent like a Whac-A-MoleSwyx [00:42:11]: There's no wayKyle [00:42:11]: Now, powered by AI to be helpful. But I think more so what I'm seeing is, a lot of the like fastest time to X tends to be because we're now inviting so many more people into like software development on GitHub That like the zeitgeist is just swarming? And it'sSwyx [00:42:32]: It's not just developers anymoreKyle [00:42:33]: And it's not you and I. Like like however you want to say like what a developer is it's not just folks who have been coding for a very long time. It's folks that have maybe started coding or only joined in since the AI era. And nowSwyx [00:42:44]: what's the latest Octoverse number? I know eighty million was my lastRem- member that a number of developers on GitHubKyle [00:42:50]: Oh, we're over 200 million now.Swyx [00:42:53]: Okay. Well, so you see?Kyle [00:42:55]: Like over 200 million developers now.Swyx [00:42:56]: But it's not developers, right? It's, it's people with a GitHub account.What Counts as a Developer in the AI Era?Kyle [00:43:00]: So, so this is, this is the biggest debate that I would say, everyone loves to have at GitHub at this point. From my perspective, right, I think that there's, there's clearly a difference between, professional enterprise developer and then developers. But I think that I think that the idea that we should be I don't know, splitting hairs or segmenting developers in the early era of software development is, not worth our not worth the time. SoSwyx [00:43:29]: When you get into gatekeepingKyle [00:43:31]: 100%Swyx [00:43:31]: What is a developer?Kyle [00:43:31]: 100%. ‘Cause I wasn't a developer when I started writing code? I was going toSwyx [00:43:36]: Oh, no. I made— I cloned a thing, seven years before I learned to code. And then I and then I wrote about my learning to code journey, and people Just called me a fraud ‘cause I had a GitHub account. And I'm “Well, no, I just use GitHub, but I don't know-” “I didn't know what I was doing.”Kyle [00:43:49]: I I remember that. I remember those sets of posts, and like that's, that's b******t. So I fight very clearly on the line of, if you create code, if you have an idea and you create it into some way of, I'm, I'm going to run it and use the app right now, you may still use AI in that moment, but that's okay. At some point you're going to do the next thing. You're going to create a big— You're going to have to learn about this database. You're going to fix a bug, whatever. We're all on some same journey, and those people are also hearing about the great new agent skill package or a new CLI tool or a new whatever. And those projects are going up because you want to be a part of this moment, just like I wanted to be a part of the Ruby community when Ruby was popping off when I started becoming a developer, and now I can just click the star button. And so I think that yes, there's clearly some amount of like spamming and game gamification that we're working against, but I really think we're just seeing this whole new cohort of folks that are moving from technology to technology because they're not working on a 20-year-old software application. They're working on a side app that they built on the weekend for their friends or for their new idea or whatever. And that's how you see these enormous charts going up and to the right with With stars.Swyx [00:44:59]: I think something that's remarkable is the persistence or, that GitHub extends to those folks. Usually when I see platforms go into a new audience, they usually have to, have like a second platform with a different name that wraps the main platform. But somehow GitHub has been able to sort of persist and extend, and it's friendly and whatever? So it's, it's nice.Spark, Low-Code, and Always Showing the CodeKyle [00:45:19]: I that's partially why I think as we've tried to move into I don't know, more like low-code-y things. We so we started working on Spark as like a way to, build an app and run it. I think that the reality is that we anytime we try to, kind of put even a veneer on top of it without when we put a veneer on top of something, we still always show you the code. That's kind of like a tenant. We're never going to, hide the code from you ever, because whatSwyx [00:45:52]: Why would you?Kyle [00:45:52]: That's, yeah, that's the whole point? However, I think that what we learned with things like Spark is that really the value of Spark for most devs is, easy runtime. And you may have a runtime or a host that you're going to use for that or you just build something and run it but, the package of making that even more simple isn't really needed for folks that are trying to build software and not just trying to build, an app, which is, slightly different, a slightly different goal. So I want to get you in, I want to get you comfortable. I think the best thing for me as, someone that did not traditionally come into software dev way back, I want anyone to be able to breach that chasm and not be in the I don't know, I feel like we're, we're still in an era of, STEM. I've got a 12-year-old and an eight-year-old, and it's “We got to get ‘em into STEM,”? Over and over. And I like I do, I do the things that good parents do. I was “Oh, you want to do coding?” “Yes, I want to do coding.” Do coding classes. But now they're just not afraid of doing software. And that's, I think, the thing that's honestly kept me at GitHub for so long. Anyone should be able to go and build a thing, just like I can go change a light switch in my house. I'm not going to go into the breaker box ‘cause I'll probably kill myself? But, I can go change that light switch. Everyone should be able to go and say, “This fricking app doesn't do what I want. I want it to work like this.” And that I think, is what's kind of kept us all connected with GitHub through the years and some and during the easiest of times or in the hard times because of that opportunity of, we're the home for all developers, and we want everyone to be able to have that feeling that we've had of, had an idea, I created it and holy s**t here it is.Swyx [00:47:37]: Here it is. All right, I'm going to try to do more spicy questions.GitHub's Hardest Scaling Moment: Growth, Agents, and UptimeKyle [00:47:42]: Great.Swyx [00:47:42]: Is it an easy time now or a hard time?Kyle [00:47:45]: Oh at GitHub? It's a hard time. Like, it's a hard time and also, I was just with my team and I said, “This is also, the best and most exciting time that I think I can remember at GitHub.” BecauseSwyx [00:47:57]: Best of times, worst of times. It's never oneKyle [00:47:59]: ‘cause we've we were talking about Octoverse reports and, usually we do an Octoverse report once a year, and we look at the numbers, and we say, “Oh my goodness.” I was at Universe in October saying, “This was the fastest year of growth that we've ever had,” right? And now we're doing more in a month than we did in a year last year.Swyx [00:48:20]: You're talking about PRs.Kyle [00:48:21]: Commits.Swyx [00:48:21]: Commits, yeah.Kyle [00:48:22]: PRs. Kind of like you name it by roughly every measure that we're looking at, there's some amount of sort of growth that is much bigger, and that is breaking our system in new ways, not old ways. Like webhooks were always notoriously, unreliable over the years?Swyx [00:48:38]: Whose fault is that?Kyle [00:48:39]: not anymore mine, but for a period of time, I'm sure you could pull up a tweet that was “It was me. I'm sorry.” but, now, that got rewritten at a scale level that is still working and is not having problems today. Now what we're finding isn't just the isn't the-The simple stuff that folks are on the sometimes on Twitter or on the internet are “Hey, why is this like this?” Sure. There's absolutely silly problems that we shouldn't exist. But now we're talking about, unique, novel permission problems that happen only at a scale across all different objects or whatever, that now we have to go rewrite this underlying system. And so it's, there are problems that yeah, caught us off guard, which I think I said. Like the growth is astronomical, but also we're making such material progress in that I'm excited once we're once we've kind of like reimagined the underlying foundation layer, or pieces of it at least, what's going to be possible when it's not just all of us and all the new people that are being developers and all of their agents and all the tools like working together. Because that'll still happen in that in that GitHub tool, that GitHub community. But it's a it's a hard day anytime we can't give you what you're looking for. We have the same problem internally. We operate through github. Com. Of course, we have backups when things go down and whatnot for our own operations but we feel it too. If it's not working it's not working for us, and that's kind of like the promise of dogfooding for GitHub. It's always been true. We're using the same tool you're using. We're not using a super secret version. We and so we also need it to be great for us for our customers of course for open source. And now an exponential growth of agents, Doing it too.Swyx [00:50:32]: I wanted to load for audio listeners who maybe haven't seen your tweets, whatever. So one billion commits in twenty-five. Now it's two hundred and seventy-five million per week on pace for fourteen billion this year, if growth remains linear. Is that still the pace? I don't know. It's been aKyle [00:50:48]: it's, it's speedingSwyx [00:50:50]: Roughly.Kyle [00:50:50]: It's still speeding up.Swyx [00:50:51]: It's, it's April, so yeah.Kyle [00:50:51]: Exactly. This was in April.Swyx [00:50:53]: All right. So basically you have fourteen x growth, right? Year on year on year. And I think that's a scaling issue. I think, I'm going to like try to really steel man this thing. People have experienced fourteen x growth. They haven't had your downtime. And that's like— C-can we go dig into that? Why? Like what's the— what broke? What are we doing to fix it? Like just anything for the community to reassure them.Why GitHub Reliability Is Breaking in New WaysKyle [00:51:18]: so there's a Like I was saying, there's a couple different places that we've seen the growth issues. Some of the growth issues, which is why we're t— I was talking about pushing hard on more CPUs is in actions in particular. More tools, more agents, more PRs mean more builds, more builds mean more CPUs. And so we are expanding through not just our data center, but obviously we were talking about moving to Azure and moving to, adding an additional cloud compute because we simply need more CPUs. Not as much GPUs. We definitely need GPUs too, but now CPUs are becoming a factor.Swyx [00:51:53]: It's very CPU heavy.Kyle [00:51:54]: Underneath the hood when it comes to some of the underlying services, we've been breaking up over the years our database infrastructure, so that way we have, more cognitive separation between our the various services. The place that we continue to have pain is in, permissioning. And so right now m-many of our permissioning layers sit into a database that we like internally call MySQL One, and old Hubbers will know what I'm talking about. And so we've been pulling things out of MySQL One for many years, because like and we use we use Vitess and we use other technologies to shard and we do it as one bigSwyx [00:52:31]: Famous thing, PlanetScale was born from this andKyle [00:52:32]: A hundred percent. Sam Old Hubber and friend. And so finding these opportunities to like break this out and then do that globally. The other thing that I think is interesting and both a unique opportunity and tricky is we also run everything I just talked about in a black box container with GitHub Enterprise Server for people that work on-prem. So we take everything I just said, and we also do it on-prem, and we also do all of that and we do it in a data residence setup for customers that need to have their data in a single location. Each of these has the unique characteristic around how we're sort of storing that data in MySQL or in a permissioning setup. That's where some of these outages have oc-occurred, where you're seeing it more like across the board rather than just like the one pieceSwyx [00:53:17]: Filling the databaseKyle [00:53:17]: Isn't quite working. Exactly. And so part of it is that. I think there's been some other places where agents are much more or more projects appear to be moving towards monorepo versus we were going the other direction for many years in the industry. Repos were smaller, but there were more of them, and now we're seeing the opposite. Repos are bigger, and there's, not fewer of them per se ‘cause there's new growth, but, we're just seeing many more big repos. Big repos, big monorepos have always had, a unique performance problem. Because each one, is slightly different if, particularly if the underlying blobs are incredibly big Inside the repos. And so we've done a ton of work that you pro— like most people haven't probably experienced, unless you're in this case of the monorepo. But that Git, infrastructure layer improvement does help the overall, system because, many of the improvements that make monorepos work better make all repo infrastructure work better. And so, I could kind of keep going down the line where it's another thing where we're moving out of, We're changing how we do j I'll just say job queuing for lack of a better, explanation changing the underlying technologies there.Swyx [00:54:32]: I spent two years being a job queuing guy, so.Kyle [00:54:34]: And so it's kind of a little bit of a little bit of piece by piece, and it's mostly because as we were— as it was built, we built everything in a way that assumed, I guess in some ways that the size of the pipe of work was going to remain the same. There's just going to be more people coming through each of those pipes. But instead now in places whereA git push was, generally a certain size for example, is now, no longer true.Swyx [00:55:03]: Oh, yeah.Kyle [00:55:03]: OrSwyx [00:55:05]: I push a thousandKyle [00:55:06]: On the average. 100%Swyx [00:55:06]: A thousand line commits like dailyKyle [00:55:07]: Same thing with PRs. Like PRs same thing. And like we've talked about optimizing that and making changes where, and there were technology choices that did not work there? And it got slow, and it didn't It was not fast. It did not do what the users wanted. And so we've been reeling that all out and going “Okay, that's just not right. Let's stop putting good money after bad and do it the do it the right way or the right way now.” So there's It's a it's a lot of things, not quite when I've experienced scale at GitHub historically, it's almost always two options that we've used. We go vertical scaling, particularly with databases, right? And we go horizontal scaling. Oh, we just have more people using this service. Great. We're going to add more servers, and we rack them in our data center, or we use it in a cloud. And now we're sort of in a like diagonal, where like vertical doesn't really work anymore. Horizontal isn't work either because we're all We all have some CPU or GPU constraints in the world now, and now we have to go in and like crack open services that have been running for 10 or 15 years and go, “Okay, the rules of this service have legitimately changed, and now we have to rewrite them.” None of this is an excuse. This is like we're We have to do the work. We have to make it better.Swyx [00:56:22]: actually as an infra guy, I'm “This is like one of the most fascinating scaling challenges I've ever seen.”Kyle [00:56:26]: That's that's, that's the thing that's the thing that it's hard for Like when we weren't talking about it publicly, and I was like I came out, and I was “Hey, I just want to explain what's going on.” Part of it comes from a very old GitHub ethos, which is it's our it's our uptime. It's down. W What I know you're a developer, so you're, you're inclined to want to understand more what's going on. But at the same time us going “Hey, this service didn't, perform the way we expected, and now we have to go change it,” we weren't We're not trying to hide anything from you i

I left work early recently to volunteer at my daughter's ice cream social and sit through her spring concert without checking my phone once. And if you're a solopreneur, you know that's a big deal. It's all thanks to my startup and shutdown routines. And I know I've talked about them on the show before, but something interesting has happened over the last year.As LLMs and AI tools have been able to connect to more services through MCP, I've been doing my shutdown routine differently. It's MUCH more automated now. As a result, I have an even better picture of what I've gotten done, and what I need to do…you know, the next time I'm at my desk.I cover:The weekly plan I rely on mostThe daily three-task journal that replaced my startup routineHow I use Whisper Memos, Todoist Ramble, and a Claude Cowork in this processIf you want to find where your own time is leaking, try the Task Audit Matrix at https://streamlined.fm/matrix. You input your tasks, label them planned/reactive and focused/processed, and get back a report showing what you can move off your plate.LinksTiny Experiments by Anne-Laure Le CunffObsidianAudioPenWhisper MemosTodoistEp. 530: How I Achieve Inbox Zero SystemStreamlined Feedback ————Streamlined Solopreneur is the podcast for solopreneurs who want to automate their business and take time off worry-free. Each week, Joe Casabona shares practical systems, tools, and strategies to help you reclaim your time and run your business without sacrificing your the rest of your life, or your health. Start with the free Solopreneur Sweep — a step-by-step method for finding where your business is losing time: https://streamlined.fm/sweepIf this episode helped you, leaving a review on Apple Podcasts helps other solopreneurs find the show — it only takes a minute and means a lot.Connect with Joe on LinkedIn: https://www.linkedin.com/in/jcasabona/

Sun, 31 May 2026 15:00:00 GMT http://relay.fm/mpu/851 http://relay.fm/mpu/851 I Have Contraband 851 David Sparks and Stephen Robles David and Stephen answer listener feedback: rebuilding Apple Home with Aqara power-over-Ethernet cameras, smart scales, raw photo editing, connecting AI to email, off-site backups, the new TRMNL X display, and DEVONthink's MCP server. David and Stephen answer listener feedback: rebuilding Apple Home with Aqara power-over-Ethernet cameras, smart scales, raw photo editing, connecting AI to email, off-site backups, the new TRMNL X display, and DEVONthink's MCP server. clean 4580 David and Stephen answer listener feedback: rebuilding Apple Home with Aqara power-over-Ethernet cameras, smart scales, raw photo editing, connecting AI to email, off-site backups, the new TRMNL X display, and DEVONthink's MCP server. This episode of Mac Power Users is sponsored by: Squarespace: Save 10% off your first purchase of a website or domain using code MPU. 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 Robot Assistant Field Guide Aqara Camera Hub G5 Pro Aqara Doorbell Camera G400 Robin Home HomePass for HomeKit & Matter HomeCam for HomeKit HomePaper for HomeKit Multi-State Sensor P100 – Aqara LLC Tailwind iQ3 Smart Garage Door Controller iSmartgate MINI THIRDREALITY Smart Garage Door Opener Govee Permanent Outdoor Lights Pro Govee Smart Cordless Table Lamp Classic IKEA launches new smart home range with Matter 5 Smart Home Upgrades I Should've Done Sooner - YouTube Aqara UWB Smart Lock U400 WITHINGS Body Smart Scale Immich Spokenly Introducing Shortcuts Playground - MacStories AI Built These Shortcuts - YouTube Stream Deck + XL | Elgato Prompter XL | Elgato MPU Timestamp Shortcut Audio Hijack Script Canisteo Motorized Blinds Roller Shade TRMNL | ePaper Dashboard DEVONthink 4.3 Herschel Menuwhere · Many Tricks Short Run — Sindre Sorhus DJI Osmo Pocket 4 Creator Combo DJI Mic 3 Bundle Shure MV7+ KU XIU Qi2.2 25W Magnetic Wireless Charger Anker Prime 3-in-1 Charging Station StealthTech Living Room Sound System | Lovesac Pixelmator Pro MacWhisper Fastmail MCP Server Superhuman DEVONthink TRMNL X Supercharge Elgato Stream Deck Elgato Key Light Air BetterTouchTool Keyboard Maestro Audio Hijack Bear Backblaze Parachute Carbon Copy Cloner Tailscale Snazzy Labs Hollyland Lark Wireless Mics Ulanzi RODECaster Pro 2

TR is joined by Rebecca Johnson to talk about how she uses Desmos to provide immediate, differentiated, and meaningful feedback to her math learners Show Notes Desmos Rebecca's progress monitoring tracker Amplify Learning Experiences for the Upcoming Week Join the Modern Classrooms Project on June 23rd for the Leadership Collaborative Summit 2026 — a free, virtual event for K-12 school and district leaders. This year's theme is "Personalized by Design," and it's built around a core belief: every student learns differently, and the best learning communities are built to act on that. You'll hear from principals, district leaders, and coaches from across the country who are making instruction more human through mastery-based, student-centered approaches — and getting real results. Every session is led by practitioners and designed to give you strategies you can act on this fall. Register here Help us spread the Modern Classrooms Model! Refer a Friend, Earn SWAG! Know a colleague or school leader who would love our summer learning opportunities? Fill out this form and MCP will take it from there! MCP will reach out to them with information on the model and their exclusive discount code. Contact us, follow us online, and learn more: Email us questions and feedback at: podcast@modernclassrooms.org Listen to this podcast on Youtube Modern Classrooms: @modernclassproj on Twitter and facebook.com/modernclassproj Kareem: @kareemfarah23 on Twitter Toni Rose: @classroomflex on Twitter and Instagram The Modern Classroom Project Modern Classrooms Online Course Take our free online course, or sign up for our mentorship program to receive personalized guidance from a Modern Classrooms mentor as you implement your own modern classroom! The Modern Classrooms Podcast is edited by Zach Diamond: @zpdiamond on Twitter and Learning to TeachSpecial Guest: Rebecca Johnson.

Today, we are dropping another episode in our "chats" series, specifically on the founder side - hearing from those scaling the companies themselves.In this episode, we are talking with Daulet Amirkhanov, Founding Engineer of Bead AI. Daulet is going to take us through his years at Meta and Cognee, leading into how he is building Bead AI, to take on compliance audits and AI automation.QuestionsTell me and my audience a little bit about you. You've gone from three years on high-throughput reliability infrastructure at Meta, to engineering the GraphRAG engine and semantic memory systems at Cognee, and you're now Founding Engineer at Bead AI — an a16z-backed startup building autonomous agent infrastructure for compliance audits. How did that journey shape the way you think about engineering for the age of autonomous systems?Let's zoom into the Meta years. For listeners who haven't worked at that scale — what was the exact piece of logging and reliability infrastructure you owned, what does "high-throughput" actually mean in numbers there, and what's one specific architectural decision from those years that still shapes how you build today?A lot of infra engineers stay in infra. You made a deliberate move from human-scale systems at Meta to agent-scale systems at Cognee. What did you see in that moment that convinced you AI agent infrastructure was the next distributed systems frontier — and not just the current hype cycle?Cognee is a GraphRAG and semantic memory company, and your work there was on the agent infrastructure side. Your biggest design call was decoupling the MCP architecture so multiple agentic systems can share unified memory through a standalone process, rather than each one coupling to its own Python runtime. Walk us through what problem that was solving and the key design decision you made.Give us a concrete example: an agent task that breaks when each agent has its own vector store, but works once they share unified state through the decoupled MCP architecture you built. What's the actual mechanism that makes the difference?Most engineers in this space come from an ML or applications background. You're coming at agent infrastructure from a pure distributed systems lens. What does that lens let you see that the ML-native crowd is missing?Bead is a16z-backed and going after compliance audits, which isn't the obvious first market for autonomous agents. You joined as Founding Engineer in January and are shaping the technical core now. From your seat: what makes compliance audits the right wedge for agent infrastructure, and what are the foundational decisions you're making today that will define what the product can do two years from now?Make a technical claim about agent infrastructure that most people in this space would push back on — and defend it. Where are you the dissenting voice?Without breaking anything confidential — what's the hardest unsolved problem on your plate at Bead AI right now, and how are you approaching it?Two years from now, what's the piece of agent infrastructure that we'll consider "obviously necessary" but doesn't exist yet? Who builds it, and what does it look like?SponsorsUnblockedBraingrid.ai.TECH DomainsMezmoLinkshttps://usebead.ai/https://www.linkedin.com/in/amirdnur/Our Sponsors:* Check out Cash App and use my code CASHAPP10 for a great deal: https://click.cash.app/ui6m/mt82fpxl #CashAppPod. Cash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. See terms and conditions at https://cash.app/legal/us/en-us/card-agreement. Cash App Green, overdraft coverage, borrow, cash back offers and promotions provided by Cash App, a Block, Inc. brand. Visit http://cash.app/legal/podcast for full disclosures.* Check out Plaud AI and use my code CODESTORY for a great deal: https://plaud.aiAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy