Podcasts about dns

Dave Crosland and Scott McNally are back to answer your bodybuilding and PED questions! We break down a new study comparing trenbolone users to steroid users who don't use tren and discuss what it may tell us about the long-term health risks of running tren, including the idea of using it year-round. Then we answer your questions on combining tren and Deca, DHB vs tren, TRT, Dianabol, chest training, cardiovascular health, and much more. ⏱️ TIMESTAMPS 0:00 Should You Run Tren & Deca Together? 1:00 Welcome Back to DNS 1:40 Technical Difficulties! 3:10 New Trenbolone Health Study 9:20 Is Year-Round Tren Ever Worth It? 14:30 Fear of Coming Off Cycle & Losing Gains 19:10 Staying On During the Diet Rebound 23:25 Low Hematocrit in Endurance Athletes 26:00 Can Dianabol Replace Testosterone? 29:30 TRT While Serving in the Military 33:25 Tren + Deca: Pros & Cons 41:00 How to Bring Up a Weak Chest 55:00 Higher Dose One Compound vs Lower Doses of Many 58:20 Testosterone Propionate & Estrogen 59:40 Dave & Scott Quit the Podcast...? 1:02:45 Uncle Dave's Advice 1:14:45 Left Ventricular Hypertrophy in Bodybuilding 1:17:35 DHB vs Trenbolone 1:19:00 Credit Scores & Final Thoughts UK Blood Work Get your Labs done by Dave in the UK : https://evalbloodanalysis.com/home/ Support the Podcast Patreon — Help keep the show growing. Even $5/month makes a difference. https://www.patreon.com/thinkbigbodybuilding Sponsors TRUE NUTRITION — Custom supplements for serious lifters Use code THINK to save https://www.truenutrition.com/THINK STROM SPORTS — Performance supplements trusted by athletes UK: https://tinyurl.com/ydmbfa54 US: https://stromsportsus.com Supplement Source Canada — Top brand supplements with fast shipping http://www.supplementsource.ca Merch Official THINK BIG Merch — Train, represent, support the brand https://think-big.printify.me/products

This week, the guys are talking about a duress-wipe phone case that's landed someone federal charges, GCC's new policy on AI-generated code, and yet another AUR malware wave that's got Arch disabling package adoptions entirely. There's a from-scratch Rust rewrite of the X server called YServer, ShadowFetch Linux brings local AI to the desktop, Nouveau is enabling atomic mode setting by default, and GOG is finally building an official Linux client. For tips, we have DNS Globe for watching DNS propagation, the bash builtin complete for custom tab-autocompletion, uv for fast Python environment and package management, and Miller for slicing and converting CSV, TSV, and JSON data. You can view the show notes at http://bit.ly/4vZejHf, and have a great week! Host: Jonathan Bennett Co-Hosts: Jeff Massie, Rob Campbell, and Ken McDonald Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

This week, the guys are talking about a duress-wipe phone case that's landed someone federal charges, GCC's new policy on AI-generated code, and yet another AUR malware wave that's got Arch disabling package adoptions entirely. There's a from-scratch Rust rewrite of the X server called YServer, ShadowFetch Linux brings local AI to the desktop, Nouveau is enabling atomic mode setting by default, and GOG is finally building an official Linux client. For tips, we have DNS Globe for watching DNS propagation, the bash builtin complete for custom tab-autocompletion, uv for fast Python environment and package management, and Miller for slicing and converting CSV, TSV, and JSON data. You can view the show notes at http://bit.ly/4vZejHf, and have a great week! Host: Jonathan Bennett Co-Hosts: Jeff Massie, Rob Campbell, and Ken McDonald Download or subscribe to Untitled Linux Show at https://twit.tv/shows/untitled-linux-show Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord. Sponsor: bitwarden.com/twit

A weekly live show covering all things Freedom Tech with Max, Q and Seth.HELP GET SAMOURAI A PARDONSIGN THE PETITION ----> https://www.change.org/p/stand-up-for-freedom-pardon-the-innocent-coders-jailed-for-building-privacy-tools DONATE TO THE FAMILIES ----> https://www.givesendgo.com/billandkeonneSUPPORT ON SOCIAL MEDIA ---> https://billandkeonne.org/TO DONATE TO ROMAN'S DEFENSE FUND: https://freeromanstorm.com/donateVALUE FOR VALUEThanks for listening you Ungovernable Misfits, we appreciate your continued support and hope you enjoy the shows.You can support this episode using your time, talent or treasure.TIME:- create fountain clips for the show- create a meetup- help boost the signal on social mediaTALENT:- create ungovernable misfit inspired art, animation or music- design or implement some software that can make the podcast better- use whatever talents you have to make a contribution to the show!TREASURE:- BOOST IT OR STREAM SATS on the Podcasting 2.0 apps @ https://podcastapps.com- DONATE via Monero @ https://xmrchat.com/ugmf- BUY SOME STICKERS @ https://www.ungovernablemisfits.com/shop/FOUNDATIONhttps://foundation.xyz/ungovernableFoundation builds Bitcoin-centric tools that empower you to reclaim your digital sovereignty.As a sovereign computing company, Foundation is the antithesis of today's tech conglomerates. Returning to cypherpunk principles, they build open source technology that “can't be evil”.Thank you Foundation Devices for sponsoring the show!Use code: Ungovernable for $10 off of your purchaseCAKE WALLEThttps://cakewallet.comCake Wallet is an open-source, non-custodial wallet available on Android, iOS, macOS, and Linux.Features:- Built-in Exchange: Swap easily between Bitcoin and Monero.- User-Friendly: Simple interface for all users.Monero Users:- Batch Transactions: Send multiple payments at once.- Faster Syncing: Optimized syncing via specified restore heights- Proxy Support: Enhance privacy with proxy node options.Bitcoin Users:- Coin Control: Manage your transactions effectively.- Silent Payments: Static bitcoin addresses- Batch Transactions: Streamline your payment process.Thank you Cake Wallet for sponsoring the show!MYNYMBOXhttps://mynymbox.ioYour go-to for anonymous server hosting solutions, featuring: virtual private & dedicated servers, domain registration and DNS parking. We don't require any of your personal information, and you can purchase using Bitcoin, Lightning, Monero and many other cryptos.Explore benefits such as No KYC, complete privacy & security, and human support.

Sandra und Daniel berichten über DNS-Probleme, Camping im Garten sowie darüber, dass man auch im hohen Alter mathematische Probleme lösen kann.

(Presented by Thinkst Canary: Most Companies find out way too late that they've been breached. Thinkst Canary changes this. Deploy Canaries and Canarytokens in minutes and then forget about them. Attackers tip their hand by touching 'em giving you the one alert, when it matters. With zero admin overhead and almost no false-positives, Canaries are deployed (and loved) on all 7 continents.) Three Buddy Problem - Episode 107: Proofpoint's Greg Lesnewich joins the show to break down Laundry Bear, the "half-click" webmail exploits that let a Russian GRU cluster hack inboxes the moment an email was opened, and what it took to publish alongside the NSA, FBI and sixteen allied agencies. Plus, Anthropic and OpenAI both admit their models escaped test sandboxes and popped real companies, why JAGS wants the CFAA burned down and vulnerable devices bricked, and a heartfelt detour into how threat hunters actually build intuition and skills. Cast: Greg Lesnewich, Juan Andres Guerrero-Saade, Ryan Naraine and Costin Raiu. Timestamps: 0:00 Sponsor - Thinkst Canary 1:34 Greg Lesnewich introduces the Proofpoint threat-hunting team 5:23 Inside the NSA ‘Laundry Bear' advisory 7:15 What does "half-click" mean? 9:58 Laundry Bear's Zimbra exploit: DNS exfil and app-specific password persistence 12:59 Ferrari model numbers, F1 UNC names, and ESET's Operation RoundPress 17:05 Targeting Ukraine, US universities, and magnetic fusion research 19:34 How threat hunters actually build intuition 32:35 Systems thinking, Donella Meadows, and Costin's laptop under the dinner table 54:48 The dopamine hit of a real find and the deleted "never mind" messages 1:00:42 Magnets of threats: under 1% of customers ever see an APT 1:25:21 Getting detections into the product, and coordinating a release with NSA 1:53:22 Anthropic and OpenAI models breaking out of the eval sandbox 2:17:45 The case for killing the CFAA and bricking vulnerable devices 2:43:44 AI in the lab, malware paleontology, Google's new names, and AngrySpark

professorjrod@gmail.comIf “the internet is down” is the sentence that makes your brain freeze, this guide is for you. We take the pressure off by turning networking into a set of simple ideas you can picture and repeat under stress, the same way you'll need to think on the help desk and on the CompTIA A+ exam. I walk through what a network is, why businesses rely on it, and how PAN, LAN, and WAN show up in real life, from Bluetooth devices to the internet itself.From there, we connect the dots across the gear and the settings: what switches do inside a LAN, why routers matter for getting online, how modems talk to your ISP, and what an access point really does for Wi‑Fi. We cover IP addressing with clean examples, the difference between private and public IPs, and how NAT translates between them. Then we hit the “must know” services: DHCP for automatic configuration, DNS for turning names into numbers, and the core ports that show up again and again. I also share a quick help desk story that proves why “never guess, always verify” saves time.The second half expands beyond networking into the wider set of IT support fundamentals: laptop design and upgrade limits, battery safety, modern connectors like USB‑C power delivery, docking stations, mobile tech like NFC and biometrics, and printer troubleshooting that becomes easy once you understand the printer type and the laser process. We wrap with practical explanations of virtualization and cloud computing, including IaaS, PaaS, and SaaS, plus why the smartest solution is often the right service, not more hardware.Subscribe for more CompTIA A+ and IT certification study help, share this with a friend who's studying, and leave a review so more new techs can find the show.Support the showArt By Sarah/DesmondMusic by Joakim KarudLittle chacha ProductionsJuan Rodriguez can be reached atTikTok @ProfessorJrodProfessorJRod@gmail.com@Prof_JRodInstagram ProfessorJRod

China embraces open AI models, then worries it's become a national security risk. The cyberattack on Minnesota water systems proves larger than first reported. CISA updates its SBOM guidance. AI supercharges dangling DNS attacks. Researchers uncover a self-propagating Copilot worm. A critical Rails flaw demands urgent patching. Mac users are lured into installing malware through fake Claude guides. Amazon links a string of NPM compromises to North Korea. And Russia charges Telegram founder Pavel Durov with aiding terrorism. Ben Yelin joins us with a border search case that's breaking new ground. Don't bite the North Korean hand that feeds you. Remember to leave us a 5-star rating and review in your favorite podcast app. Miss an episode? Sign-up for our daily intelligence roundup, Daily Briefing, and you'll never miss a beat. And be sure to follow CyberWire Daily on LinkedIn. CyberWire Guest Today we are joined by Ben Yelin from University of Maryland Center for Cyber Health and Hazard Strategies talking about a border search case that's breaking new ground. If you enjoyed this conversation, check out Ben on the Caveat podcast here.  Selected Reading As China's A.I. Gets Stronger, It Poses New Risks to Beijing (New York Times) Minnesota Water Utilities Suffer ‘Coordinated Cyber Attack' (GovTech) CISA Updates Software Bill of Materials Guidance to Strengthen Supply Chain Security (HSToday) ‘DangleGeddon': AI Could Weaponize Forgotten DNS Records at Global Scale (SecurityWeek) Word worm crawls into Copilot, spreads chaos (The Register) Possible arbitrary file read and remote code execution in Active Storage variant processing (GitHub) Fake Claude Install Guide Leads to MacSync Stealer and RAT: What We Pulled From the Attacker's Servers (Huntress) Amazon identifies North Korean hacker group behind open-source supply chain attacks (AWS Security Blog) Russia accuses Telegram CEO Pavel Durov of aiding terrorism in its latest digital crackdown (AP News) North Korea's elite hackers turned on their own government — and got caught (Bitdefender) Share your feedback. What do you think about CyberWire Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show. Want to hear your company in the show? N2K CyberWire helps you reach the industry's most influential leaders and operators, while building visibility, authority, and connectivity across the cybersecurity community. Learn more at sponsor.thecyberwire.com. The CyberWire is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc.

In the security news: 2.2 million cars, one shared Bluetooth key JFrog tries to spin an AI 0-day into a win Sextortion scammers recycling ShinyHunters' leaks The first hack ever, from 1966 Prompt injection as a service, $150 a month Cisco's mystery "static credential" BMCs still on the internet, still handing out hashes Scattered Spider duo sentenced over the TfL hack Air-gapped data sneaking out over the video cable A ghost in the network DNS poisoning checks into hotel WiFi Microsoft's cut-rate cybersecurity AI Learning to trust USB drives again Agentic pentesting shows up just in time for Black Hat Microsoft rethinks security for the AI age, again Visit https://www.securityweekly.com/psw for all the latest episodes! Show Notes: https://securityweekly.com/psw-937

In the security news: 2.2 million cars, one shared Bluetooth key JFrog tries to spin an AI 0-day into a win Sextortion scammers recycling ShinyHunters' leaks The first hack ever, from 1966 Prompt injection as a service, $150 a month Cisco's mystery "static credential" BMCs still on the internet, still handing out hashes Scattered Spider duo sentenced over the TfL hack Air-gapped data sneaking out over the video cable A ghost in the network DNS poisoning checks into hotel WiFi Microsoft's cut-rate cybersecurity AI Learning to trust USB drives again Agentic pentesting shows up just in time for Black Hat Microsoft rethinks security for the AI age, again Show Notes: https://securityweekly.com/psw-937

In the security news: 2.2 million cars, one shared Bluetooth key JFrog tries to spin an AI 0-day into a win Sextortion scammers recycling ShinyHunters' leaks The first hack ever, from 1966 Prompt injection as a service, $150 a month Cisco's mystery "static credential" BMCs still on the internet, still handing out hashes Scattered Spider duo sentenced over the TfL hack Air-gapped data sneaking out over the video cable A ghost in the network DNS poisoning checks into hotel WiFi Microsoft's cut-rate cybersecurity AI Learning to trust USB drives again Agentic pentesting shows up just in time for Black Hat Microsoft rethinks security for the AI age, again Visit https://www.securityweekly.com/psw for all the latest episodes! Show Notes: https://securityweekly.com/psw-937

In the security news: 2.2 million cars, one shared Bluetooth key JFrog tries to spin an AI 0-day into a win Sextortion scammers recycling ShinyHunters' leaks The first hack ever, from 1966 Prompt injection as a service, $150 a month Cisco's mystery "static credential" BMCs still on the internet, still handing out hashes Scattered Spider duo sentenced over the TfL hack Air-gapped data sneaking out over the video cable A ghost in the network DNS poisoning checks into hotel WiFi Microsoft's cut-rate cybersecurity AI Learning to trust USB drives again Agentic pentesting shows up just in time for Black Hat Microsoft rethinks security for the AI age, again Show Notes: https://securityweekly.com/psw-937

In this episode of All Hands on Tech, we sit down with Michael Hebert of Turtle Island Technology Solutions to explore why organizations need to rethink what security really means.The conversation examines the growing convergence of physical security and cybersecurity, why many businesses remain vulnerable despite strong digital protections and how organizations can take a more holistic approach to managing risk. Michael also reflects on Turtle Island's recent acquisition of Castellan Information Security Services, the company's vision for building an Indigenous-led technology and security ecosystem and the emerging security challenges every organization should be preparing for.Produced by Unbound Media

Security Conversations: Kenneth Kinion, founder and CEO of Validin, joins Ryan Naraine on the show to unpack what "internet intelligence" really means for the analysts and responders chasing malicious infrastructure. We trace his path from Georgia Tech through Microsoft and Amazon to the frustrations that led to the creation of Validin, the competition from big AI, the value of AI-powered tools to speed up infrastructure hunting, and why defenders keep falling further behind fast-moving attackers. Timestamps: 0:00 – Intro: What does Validin do? 0:51 – Who uses Validin: CTI teams, SOCs, incident responders 2:19 – Atlanta and Georgia Tech's cybersecurity pipeline 5:31 – Lessons from Microsoft and Amazon: waterfall vs. agile 8:29 – Filling gaps in passive DNS data 9:57 – Misunderstood things about threat intelligence 14:17 – The value of "cyber paleontology" 16:00 – What makes one data set better than another? 19:39 – How Validin works: from one suspicious domain to a full pivot 21:27 – AI as existential threat or force multiplier for Validin 26:55 – Dual-use AI: are defenders losing ground to attackers? 31:11 – Closing: the next hard problem Validin wants to solve

A weekly live show covering all things Freedom Tech with Max, Q and Seth.FOLLOW JON ON TWITTER: https://x.com/prevhashnonceHELP GET SAMOURAI A PARDONSIGN THE PETITION ----> https://www.change.org/p/stand-up-for-freedom-pardon-the-innocent-coders-jailed-for-building-privacy-tools DONATE TO THE FAMILIES ----> https://www.givesendgo.com/billandkeonneSUPPORT ON SOCIAL MEDIA ---> https://billandkeonne.org/TO DONATE TO ROMAN'S DEFENSE FUND: https://freeromanstorm.com/donateVALUE FOR VALUEThanks for listening you Ungovernable Misfits, we appreciate your continued support and hope you enjoy the shows.You can support this episode using your time, talent or treasure.TIME:- create fountain clips for the show- create a meetup- help boost the signal on social mediaTALENT:- create ungovernable misfit inspired art, animation or music- design or implement some software that can make the podcast better- use whatever talents you have to make a contribution to the show!TREASURE:- BOOST IT OR STREAM SATS on the Podcasting 2.0 apps @ https://podcastapps.com- DONATE via Monero @ https://xmrchat.com/ugmf- BUY SOME STICKERS @ https://www.ungovernablemisfits.com/shop/FOUNDATIONhttps://foundation.xyz/ungovernableFoundation builds Bitcoin-centric tools that empower you to reclaim your digital sovereignty.As a sovereign computing company, Foundation is the antithesis of today's tech conglomerates. Returning to cypherpunk principles, they build open source technology that “can't be evil”.Thank you Foundation Devices for sponsoring the show!Use code: Ungovernable for $10 off of your purchaseCAKE WALLEThttps://cakewallet.comCake Wallet is an open-source, non-custodial wallet available on Android, iOS, macOS, and Linux.Features:- Built-in Exchange: Swap easily between Bitcoin and Monero.- User-Friendly: Simple interface for all users.Monero Users:- Batch Transactions: Send multiple payments at once.- Faster Syncing: Optimized syncing via specified restore heights- Proxy Support: Enhance privacy with proxy node options.Bitcoin Users:- Coin Control: Manage your transactions effectively.- Silent Payments: Static bitcoin addresses- Batch Transactions: Streamline your payment process.Thank you Cake Wallet for sponsoring the show!MYNYMBOXhttps://mynymbox.ioYour go-to for anonymous server hosting solutions, featuring: virtual private & dedicated servers, domain registration and DNS parking. We don't require any of your personal information, and you can purchase using Bitcoin, Lightning, Monero and many other cryptos.Explore benefits such as No KYC, complete privacy & security, and human support.

Scans for ESAFENET CDG 3 Document Management System Weak Logins https://isc.sans.edu/diary/Scans%20for%20ESAFENET%20CDG%203%20Document%20Management%20System%20Weak%20Logins/33184 DNS Poisoning Tactics Expand to Hospitality Wi-Fi https://reliaquest.com/blog/threat-spotlight-dns-poisoning-tactics-expand-to-hospitality/ Silent Replacement of Trusted macOS App Executables https://mysk.blog/2026/07/23/macos-overwrite-app-executables/ GitHub and PyPi Defense updates https://github.blog/security/supply-chain-security/the-case-for-a-cooldown-why-dependabot-now-waits-before-issuing-version-updates/ https://blog.pypi.org/posts/2026-07-22-releases-now-reject-new-files-after-14-days/ https://www.bleepingcomputer.com/news/security/github-pypi-add-time-absed-defenses-against-supply-chain-attacks/ My Upcoming Classes https://www.sans.org/profiles/dr-johannes-ullrich

Hotel Wi‑Fi steals Microsoft 365 logins, ShinyHunters sextortion spam, and Chick‑fil‑A stuffed again Hotel and conference Wi‑Fi networks are being hijacked to harvest Microsoft 365 credentials by compromising captive portals and DNS, redirecting travelers to convincing lookalike logins and even abusing Microsoft's device code flow to obtain OAuth tokens in ways MFA may not stop. Plus, an Illinois man received 76 months in prison for phishing into hundreds of women's Snapchat accounts to steal explicit content and run a for-profit account access scheme. Also: a sextortion email wave impersonates ShinyHunters using real breach references while making fake device-compromise claims; ransomware disrupted Japanese frozen food supplier Nichirei shipments, affecting KFC franchises; and Chick-fil-A disclosed a June credential-stuffing incident impacting 13,322 loyalty accounts, resetting access, removing saved payments, restoring rewards, and adding free rewards as an apology. 00:00 Top Stories Intro 00:28 Hotel Wi-Fi Credential Trap 01:50 Public Wi-Fi Advice Debate 03:16 Snapchat Phishing Sentencing 05:18 ShinyHunters Sextortion Scam 07:09 Ransomware Hits Food Supply 09:16 Chick-fil-A Stuffing Fallout 11:12 Wrap Up and Sign Off

La ICANN anunció que el 11 de octubre de 2026 cambiará el anclaje de confianza del Sistema de Nombres de Dominio (DNS), un proceso técnico conocido como traspaso de la KSK (Key Signing Key) de DNSSEC. En este episodio de Vida Digital conversamos con Nicolás Antoniello, Gerente Sénior de Participación Técnica para América Latina y el Caribe de la ICANN, para entender qué significa este cambio, qué será completamente transparente para la mayoría de las personas y qué deben revisar quienes administran redes, servidores o el sitio web de su negocio.Nicolás parte desde lo más básico y explica por qué al DNS se le llama la guía telefónica de internet: así como una guía asocia el nombre de una persona con su número de teléfono, el DNS asocia un nombre de dominio fácil de recordar con la dirección IP que las máquinas necesitan para comunicarse. Sobre ese cimiento aparece DNSSEC, las extensiones de seguridad que permiten verificar que la respuesta que entrega el DNS es auténtica y no fue alterada en el camino. Un punto que aclara con cuidado: "la información no está encriptada... Lo que se agrega es una firma que permite validar información."¿Qué ocurre realmente en octubre? En sus palabras, "esto que va a suceder en octubre es nada más y nada menos que un cambio de firma." La clave que hoy firma la zona raíz se reemplaza por una nueva, más robusta, y por eso los operadores de servidores recursivos de validación deben asegurarse de conocer las dos firmas antes de la fecha. La buena noticia es que la nueva clave se publicó hace más de dos años y la mayoría de los sistemas la adoptan de forma automática. Aun así, Nicolás recomienda hacer la verificación: es cuestión de minutos y evita sorpresas.También hablamos del vínculo entre DNSSEC y el correo electrónico, del riesgo que corren las instituciones con servidores antiguos que llevan años sin tocarse y de cómo la falsificación de información de DNS habilita ataques que suelen empezar con un phishing que imita la página de un banco. Para quien administra infraestructura sin DNSSEC, la recomendación es planificar su despliegue a corto y mediano plazo.Para la persona que navega, compra en línea o revisa su banca desde el celular, el mensaje es tranquilizador: "El impacto en los usuarios finales es cero, absolutamente imperceptible." Si tu proveedor y los operadores hacen su parte, ni siquiera notarás que hubo un cambio.Un episodio para entender, sin tecnicismos innecesarios, una pieza invisible que sostiene la confianza de todo internet.¿Administras una red, un servidor o un sitio web? Cuéntanos en los comentarios si ya verificaste que tus sistemas conocen las dos firmas de cara a octubre, o qué dudas te deja este tema. Nos encanta leerte y con gusto trasladamos tus inquietudes al equipo de la ICANN.Si este episodio te resultó útil, comenta, comparte con quien creas que lo necesita y suscríbete para no perderte lo que viene.Gracias a Nicolás Antoniello y a todo el equipo de la ICANN, y a Radio Ancón por permitirnos llegar a ustedes cada semana.Marcadores de capítulo0:00 Bienvenida y presentación de Nicolás Antoniello (ICANN)1:39 Qué es el DNS y por qué es la guía telefónica de internet11:56 DNSSEC, correo electrónico y falsificación de dominios12:43 Qué deben verificar los operadores antes del 11 de octubre20:30 Servidores desactualizados: riesgos y qué notará el público21:32 Impacto cero para el usuario final y defensa ante el phishing24:14 Despedida y cierre#VidaDigital #ICANN #DNS #DNSSEC #KSKRollover #Ciberseguridad #SeguridadInformática #Internet #Phishing #AdministraciónDeRedes #InfraestructuraTI #Panamá #RadioAncón #Tecnología

Episode 818 is here! We have a packed show for you this week: ?? Nintendo is facing a tariff refund lawsuit and has filed a motion to dismiss it. ? Splatoon Raiders reviews are out, averaging 8s and 9s from major outlets! ? In Change the System: – Justin brings updates on RayNeo and playing "big screen" in bed. – Brandon is playing DENSHATTACK!!!! and Starfox Multiplayer, and is officially done with the Fire Emblem mobile game. – Eugene is working on his Steam Machine setting up EmuDeck, alongside troubleshooting some DNS problems and Home Assistant quirks.

This week, we discuss Bun's move to Rust, OpenAI hunting for revenue, and Stripe's bid for PayPal. Plus, Coté's Dad wisdom. Watch the YouTube Live Recording of Episode 582 Runner-up Titles Turn off my dog Dad Wisdom Amazon subscription and let it fly Dad box Talking about modernization Skin in the game The ivory tower of “Things Actually Work in the Real World” Business fortnight No SegwaysBanking works everywhere else in the world, not America Rundown Rewriting Bun in Rust OpenAI OpenAI's No. 2 Executive to Step Down in Latest Leadership Shake-up OpenAI's Chief Futurist Is Leaving the Company AI Devices Are Coming. Will Your Favorite Apps Be Along for the Ride? OpenAI Executive Kevin Weil Is Leaving the Company OpenAI power consolidates under co-founder Greg Brockman ahead of prospective IPO Barret Zoph is out at OpenAI again after just five months Apple sues OpenAI, accuses ex-employees of stealing trade secrets OpenAI Appears to Be Missing Its Sales Goals by a Vast Margin Stripe, Advent make $53 billion takeover offer for PayPal, sending stock soaring Relevant to your Interests SpaceX, AI Bubble Fears, and The Age of the Trillion-Dollar, Zero-Profit Company Microsoft Frontier Company: AI engineering that amplifies and protects your intelligence Building a CLI for all of Cloudflare California founder fired for ignoring his company's own return-to-office mandate Samsung chip division's single-year profits beat its past 40 years of profits, combined Did a lottery winner become "the company of the future"? Power company hikes data center bills by 30%, cuts residential electricity costs by 1.3% Where AI Works: Slack is the Conversational Interface for Headless AI Salesforce MCP Servers: AI, Data & Analytics for Tableau & Data 360 in Slack IBM and Red Hat launch Lightwell to defend open-source code from AI attacks Infoblox acquires Kentik, adding network observability to its DNS and DDI platform Apple 'Hide My Email' Vulnerability Reveals Peoples' Real Email Addresses House passes bill to make daylight saving time permanent United Airlines' new upsell: Keeping other travelers out of the middle seat Linux creator Linus Torvalds puts foot down on anti-AI comments 1Password now lets Claude sign in to websites without seeing your passwords 1Password and Anthropic Bring Secure Credential Access to Claude forum, an accountable orchestrator for AI agents Google continues its renaming streak by turning NotebookLM to Gemini Notebook 'Pretty revolting': LG's TV are getting huge backlash from users due to installing software What is a micro-retirement? Inside the latest Gen Z trend You paid me, a long-time Linux user, to use Windows 11 exclusively for a month China delivers a one-two punch to America's AI dominance Can Lenovo's World Cup AI Moment Cement Its Enterprise PC Dominance? AI Coding Tools Deliver Speed but Not Business Value Unless Wrapped in Process: China's Moonshot in Talks on Pre-IPO Funds at $50 Billion Value OpenAI and Hugging Face partner to address security incident during model evaluation AI won't fix your broken culture, but you can fix your broken culture Oracle Is Now Down 28% in a Month. Will the 52-Week Low of $132 Hold or Fold? IBM stock craters 23% after issuing second-quarter earnings warning Sheetz is quitting VMware, migrating 11,000 virtual machines Meta in Talks to Lease Computing Power to Anthropic in Potential $10 Billion Deal AWS cloud lead Dave Brown heads to Meta - report - DCD Amazon senior cloud executive departs after 18 years Meta Caps Internal AI Token Spending After Costs Approach Billions in 2026 Meta Compute Launch Sends AI Compute Stocks Tumbling Globally Agentic ransomware for automated database extortion Dark-Moon: Autonomous AI pentesting engine Sponsors Signadot: making sure AI-written code actually works. Nonsense Messi Beats Ronaldo in 2026 World Cup Password Breach Data Rankings United Airlines' new upsell: Keeping other travelers out of the middle seat Yes, you can now order DoorDash from the command line House passes bill to make daylight saving time permanent Listener Feedback Biogen hiring Associate Director, Enterprise Architecture in Triangle, NC Tim releases a font for developers whose close-up vision isn't what it used to be The Java Story | Official Trailer | Full Film Coming July 17th Conferences DevOpsDays Graz, Sept 4-5, 2026 Cloud Foundry Summit, Sept. 21st to 22nd, Heidelberg, Coté speaking. DevOpsDays Rockies, Sept. 22 – 23, 2026, Discount Code: 26DODSWEDEFTALK WeAreDevelopers NA, Sept 23-25, 2026, Discount Code: DEVPOD26 25 Free Tickets DevOpsDays Dallas, Sept 28-29, 2026 DevOpsDays Vilnius, Sep 30 - Oct 1, 2006 DevOpsDays Istanbul, Oct 24th, 2026, Coté keynoting. 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Hey friends! Welcome back to another Tales of Pentest Pwnage — my favorite mini-series where I share the good, the bad, and the "why didn't I check THAT first?!" moments from real-world engagements. Today's story has a little bit of everything: a legit path to domain admin, some late-night rabbit holes, a lesson in humility, and a villain you've definitely met before. (Spoiler: it's DNS.) A couple of quick plugs before we dive in: Private GOAD training is going strong! — We just wrapped a 3-day private session (7 students — that's max capacity!) of our Active Directory pentesting class built on the Game of Active Directory (GOAD) framework. Over three days, students enumerate, attack, and fully pwn three separate AD environments. The private format is just *chef's kiss* — when it's a team from the same company, the conversation gets real fast. Like, "hey I just checked Bloodhound on break and Bob from accounting has full rights over the DC" real. If you want to send 3–7 people from your org, hit up 7MinSec.com/training to line up a private session. Support the show over at 7MinSec.club — That's our Substack, where every Tuesday I drop a short TuesdayTOOLSday video about security tools. Free subscriptions are welcome and mean a lot — you'll just get pinged when new content drops. No spam, no blindly-sent Outlook calendar invites. I promise. Pentest tips and scripts live at 7MinSec.wiki — I reference it throughout today's episode, including some step-by-step guidance on the techniques we'll talk about below. Now — onto the pwnage. Fair warning: I've been burning the candle at three ends lately trying to catch up after a tough few weeks of grief (if you want the backstory, the last couple episodes cover my dad passing away). The good news is my head is semi back on straight and I put it to work on a recurring client environment — one that keeps getting better year over year. Machine account quota locked down? Check. No Kerberoastable or AS-REP roastable users? Check. No local admin rights, no web client running? Check and check. All good signs. And then PingCastle smiled right into my eyeballs with a big red finding: The DC's LAN Manager authentication level was weak enough to coerce and capture a downgraded hash — Specifically, an NTLMv1 SSP hash. Using Coercer to nudge the DC into authenticating to my Kali box (with Responder running), I captured the goods. Pretty little hashes all in a row. Cracking that hash: enter Vast.ai — The old go-to for this type of crack used to be crack.sh, but their cracker has been offline for years. What they do still have is a walkthrough pointing to a tool from EvilMog on GitHub that helps you prep the raw hash material and figure out exactly how to crack it with Hashcat. For the GPU horsepower, I rented a beefy multi-GPU instance on Vast.ai — filter for 16+ GPUs, pick a Hashcat Docker image, and SSH in. The whole crack job took about 16 hours at ~$4/hr. Do the math: $64 to reconstruct the DC's NTLM hash. Worth it. Tmux sidebar — seriously just learn it — Vast.ai is actually what finally got me into tmux, because the Hashcat Docker container drops you right into a tmux session. This is clutch: you can kick off a 16-hour crack job, detach, and reattach later without killing anything. On a pentest, my workflow now is SSH in → tmux → name a few session windows for Responder, Exegol, packet captures, etc. I used to fumble around with Linux screen sessions. Not anymore! From hash to DA — the usual playbook — Once you've got the DC's NTLM hash, you can request a Kerberos ticket and load it up, then run a DCSync to pull the KRBTGT hash. From there it's god mode: dump hashes, pass-the-hash as domain admins, and you have yourself a cool privesc POC. Except this time…the POC didn't work. The part where I Jean-Claude Van Damme helicopter kick myself in the face — DCSync failed immediately. Like, suspiciously fast — barely two lines of output and done. I tried every version of every tool I could get my hands on. I tried Windows, I tried Linux. I even asked the client to check if their endpoint protection was blocking me (it wasn't). I touched grass. I played guitar. I played some Splinter Cell Blacklist (old game, highly recommend if you like the Hitman-style vibes). Came back fresh. Rebooted both VMs. Still nothing. It was DNS. It's always DNS. — The thing that finally caught my eye: the commands were failing too fast. Like it wasn't even reaching the DC. I catted the resolv.conf inside my Exegol instance (heads up: Exegol has its own resolv.conf and hosts file, separate from your base Kali system!) and found a stale DNS entry pointing to an old DC that was no longer serving anything. Nuked the bad entry, added static hosts file entries for the live DC, ran the command again, and — hash rain. Pennies from heaven. It was midnight and I literally pushed back from my desk like a baby pushing away from a high chair going "Baby Brian is all done!" The lesson: — I know the meme. "It's always DNS." I just personally hadn't hit it hard in my security life since my sysadmin days back before 2013. Now I have. So going forward I'll check DNS first (and often). Vacation attempt #3 incoming… pray for me — My wife nearly died in Punta Cana earlier this year. Then our summer cabin trip was cold and rainy with zero water time. And now we've got families flying in from multiple states for a lake weekend — except we just found out our reservation through Booking.com was basically vaporized because the resort changed hands and never updated their website. My wife (who is an absolute saint and my better three-quarters) almost had a 360-degree head spin (like in The Exorcist) talking to customer service. But we scrambled, found a last-minute place, and I'm choosing to believe it's not in Jason Voorhees' back yard. Could this be my last episode? Maybe. But hey — it was a good one. Talk to you next week (hopefully).

This week on The Back of the Pack Podcast: Second Wind, our July series Why Are We Like This? continues with a topic every runner eventually meets: FOMO, the fear of missing out. Runners and endurance athletes love filling the calendar with races, run clubs, team events, traditions, medals, group photos, and shared suffering, but sooner or later, life gets in the way. Whether it is a scheduling conflict, family responsibility, injury, illness, weather, travel, burnout, or simply needing rest, sometimes we do not make it to the start line. This episode digs into the emotional weirdness of a DNS, or Did Not Start, and why missing a race can feel like everyone else went to summer camp without us. We talk about registration guilt, medal FOMO, social media FOMO, group photo FOMO, and the way runners can convince themselves that missing one race somehow means they are less committed. Spoiler: it does not. Sometimes the smartest race decision is not showing up, especially when another responsibility, another priority, or the bigger picture needs to come first. We also look at the race calendar problem, because runners have a special talent for seeing an open Saturday and immediately trying to turn it into a bib number. At the heart of the episode is a reminder that races are supposed to be fun, not mandatory life sentences with finish-line bananas. The running community will still be there next weekend, next month, and next year. Missing one start line does not erase the miles, memories, consistency, or commitment already built.

I veckans odpod: Kinesisk blodsport, DNS-historia, tidskriften Häpna, Crocker Land-expeditionen och den sista Widow's Bay-cirkeln. Om du kan, stöd oss på http://patreon.com/odpod

Andrew Palardy joins the hosts to discuss how Tayga, the open-source NAT64 daemon for Linux, is being updated to ensure it is up to date with all the current RFCs. Andrew explains how recent RFC compliance and UDP checksum fixes help resolve connectivity issues and how the new eBPF CLAT support arriving in NetworkManager promises... Read more »

Andrew Palardy joins the hosts to discuss how Tayga, the open-source NAT64 daemon for Linux, is being updated to ensure it is up to date with all the current RFCs. Andrew explains how recent RFC compliance and UDP checksum fixes help resolve connectivity issues and how the new eBPF CLAT support arriving in NetworkManager promises... Read more »

AI ready website pet business structure is what gets you recommended by ChatGPT and Perplexity instead of skipped, and most pet sitting and dog walking websites are not built for it yet. In this episode of Bella In Your Business, pet sitters, dog walkers, and pet care business owners will learn exactly what AI checks on a website before it recommends a business, and what to fix first. Web developer Erika Godwin of Barketing Solutions joins Bella Vasta to explain why keyword-stuffed pages don't work anymore, what trust signals AI is actually looking for, how schema markup works behind the scenes, and why raw server data matters more than Google Analytics for tracking AI traffic. This is Episode 473 of Bella In Your Business. IN THIS EPISODE: • How AI decides if your AI ready website pet business setup earns a recommendation, and what it skips over • What schema markup actually does, and why your site needs it • Why a generic services page keeps you invisible to ChatGPT and Perplexity • How raw server data shows AI traffic Google Analytics cannot track • What to do first if your website hasn't been touched in over a year Erika also breaks down why an AI-built website alone is not enough. A developer still handles the schema markup, DNS setup, and page structure an AI tool will never get right on its own, the exact parts AI is reading closely before it decides whether to trust your pet business. She walks through why a bio page for the owner matters, why one general services page is not enough anymore, and why AI cross-checks your directory listings and news mentions against what your website says before it trusts any of it. Bella and Erika also cover directories, citations, and how a hub and spoke content strategy helped one client's Bloomfield Hills dog walking page jump in rankings across search engines within one month of launch. Bella shares her own shift away from social media as the main marketing focus toward content, schema, and signals, and explains why she believes pet business owners who build this now will be trusted ahead of anyone who tries to catch up later. If you want to know exactly where your own site stands, Bella runs a free AI ready website pet business audit at jumpconsulting.net/audit that checks your schema, your AI citations, and what ChatGPT and Perplexity currently say about your business. TIMESTAMPS: [0:00] Cold open, pet parents are asking ChatGPT, not Googling [0:45] Welcome back Erika Godwin, web developer and referral partner [2:00] How search behavior for pet businesses has changed in 2026 [4:00] Why an AI-built website still isn't enough on its own [6:30] What AI actually reads, trust signals and schema explained [10:00] The raw server data most pet business sites are missing [13:00] From static brochure to trust engine, how site design has shifted [17:00] Building real service pages, location pages, and FAQs [19:30] Hub and spoke content, the Becky Lea Bloomfield Hills case study [22:00] Predictions, what happens to pet businesses that wait [26:00] Citations, directories, and getting featured in the news [29:30] How to check your own website's AI health today [32:00] Closing thoughts and where to find Erika RESOURCES: Get your own AI ready website pet business audit: https://jumpconsulting.net/audit Barketing Solutions: https://barketing.co Jump Mastermind: https://jumpconsulting.net/mastermind Book 20 minutes with Bella: https://jumpconsulting.net/20 AI For The Busy Human: https://bellavasta.com/busyhuman The AI booking story, Episode 465: https://jumpconsulting.net/episode-465 CONNECT: Website: https://jumpconsulting.net Instagram and Facebook, search Bella Vasta: https://bellavasta.com #AIReadyWebsite #PetBusiness #BellaInYourBusiness

Andrew Palardy joins the hosts to discuss how Tayga, the open-source NAT64 daemon for Linux, is being updated to ensure it is up to date with all the current RFCs. Andrew explains how recent RFC compliance and UDP checksum fixes help resolve connectivity issues and how the new eBPF CLAT support arriving in NetworkManager promises... Read more »

Most people use technology all day without giving much thought to what is happening behind the screen. We trust routers that may not have been updated in years, depend on internet systems few of us understand, and now turn to artificial intelligence for everything from travel plans to home repairs. That convenience comes with tradeoffs. In this episode, we look at the weaknesses built into our connected world and what happens when our technical knowledge fails to keep pace with the technology surrounding us. Sherrod DeGrippo leads threat intelligence for Unit 42 at Palo Alto Networks, where she oversees teams working to identify and respond to increasingly complex cyber threats. During more than two decades in information security, she has held senior leadership roles at Microsoft and Proofpoint, along with positions at Nexum, Symantec, Secureworks, and the National Nuclear Security Administration. Sherrod was named Cyber Security Woman of the Year in 2022 and regularly shares her expertise at industry conferences and through outlets including BBC News, The Wall Street Journal, CNN, and The New York Times. She is also the author of *Threat Driven Software Development: Defending Modern Online Services*. We discuss how outdated home routers become tools for criminal and nation-state attacks, what could happen as technical knowledge disappears, and why foundational internet systems may be more vulnerable than people realize. Sherrod also explains why the greatest danger from AI may not be a dramatic technological disaster, but the gradual loss of human connection as people choose frictionless answers over real conversations. Along the way, she shares practical examples of where AI genuinely helps, where its limits become obvious, and why slowing down still matters when emotion or urgency begins driving a decision. Show Notes: [01:48] Sherrod introduces herself and traces her 23-year career from government network security to threat intelligence. [03:00] An early lesson in buffer overflows sparked a lasting interest in both hacking and protecting systems. [05:10] Growing up around AT&T gave Sherrod firsthand experience with telephone networks, copper lines, and beige boxing. [08:17] Technology has shifted from something people opened and explored into consumer devices few people truly understand. [11:24] As technical knowledge disappears, the people who understand how systems actually work are becoming increasingly rare. [13:41] Older internet users often understood where their data traveled, while today's devices constantly communicate in the background. [16:22] Network security remains critical because once malicious traffic reaches a device, the problem has already become much larger. [17:57] Outdated home routers provide an attractive attack surface for criminals and foreign governments. [20:12] The conversation turns to the fragility of DNS, internet protocols, and the systems supporting the global network. [22:13] Chris shares his experience with denial-of-service attacks and seeing legitimate online services exploited by malware. [25:48] Sherrod explains why AI's greatest danger may be the gradual loss of human relationships rather than a dramatic physical threat. [29:23] AI works well for tedious tasks, but human connection, creativity, and judgment should not be handed over so easily. [31:35] Living intentionally means deciding what experiences matter instead of allowing technology or other outside forces to decide for us. [34:10] Frictionless technology may leave people more vulnerable to scams by training them to expect immediate and effortless results. [35:33] Social engineering uses modern tools, but manipulation, espionage, and theft have existed for thousands of years. [38:14] AI may eliminate certain jobs while also giving people more time to focus on customers, relationships, and other human-centered work. [40:44] A furnace repair shows how AI can guide basic troubleshooting and reduce the need for some professional diagnostic visits. [43:08] AI can help someone become competent in many areas, but having access to it does not make anyone an expert. [45:44] Used intentionally, AI can remove unwanted planning and create more opportunities for people to spend time together. [47:18] Strong prompts, detailed skill files, and personalized instructions may become more valuable than traditional prompt engineering. [49:10] A gift card scam demonstrates how fear and urgency can push someone to ignore repeated warnings from others. [51:30] Sherrod advises pausing whenever a strong emotion begins driving an unusual financial or personal decision.  Thanks for joining us on Easy Prey. Be sure to subscribe to our podcast on iTunes and leave a nice review.  Links and Resources: Podcast Web Page Facebook Page whatismyipaddress.com Easy Prey on Instagram Easy Prey on Twitter Easy Prey on LinkedIn Easy Prey on YouTube Easy Prey on Pinterest Sherrod DeGrippo Sherrod DeGrippo - LinkedIn Palo Alto Networks Unit 42 Threat Driven Software Development: Defending Modern Online Services

Michael Saylor's "never sell" era is OVER. Strategy sold 3,588 BTC (~$216M) at a 20% loss to cover $1.8B in dividend obligations — and Canadian pensions are holding close to $1B of MSTR stock. On June 29, Strategy's board adopted its "Digital Credit Capital Framework": $1B stock buybacks, a 12% STRC dividend, and a $1.25B "BTC Monetization Program" authorizing Bitcoin sales "when strategic." CEO Phong Le calls it "evolving from one-way capital issuance to active capital management." The mNAV premium that financed five years of buying has collapsed from 2.66x to around 1x — and one in three Bitcoin treasury companies now trades below the value of its coins. In this episode of the Canadian Bitcoiners Podcast:- Strategy's pivot: the mNAV death spiral, coins sold at a 20% realized loss, insider selling, and JPMorgan's $2.8B-$11.6B index-exclusion warning- The Canada connection: CPPIB, AIMCo, National Bank, RBC and HOOPP hold ~$1B of MSTR — your pension bought the wrapper trade- A quantum-proof recovery tool that works for everyone except Satoshi's 1.1M BTC- OkoBot: malware that fakes your Ledger/Trezor recovery screen- Global hashrate is shrinking — but Pakistan is up 733%- CleanSpark signs a $6.6B, 20-year AI data center lease- Canada bans crypto political donations under Bill C-25- Clown World North: Stan Cho's $16,203 hotel bill, Canada Post's $30.8M in bonuses against a $1.57B loss, and $159,800 in flight catering- The BIS confirms Canada's housing crash is the biggest on record as 56,400 Canadians leave in a year The leverage cycle is unwinding — the conviction cohort isn't. Long-term holders just hit a record 14.85M BTC. ETF outflows and treasury-company stress are paper Bitcoin changing hands; the base layer doesn't care. Hold your own keys. — Canadian Bitcoiners Podcast- Website: https://canadianbitcoiners.com- X: @CanadianBTCPod- Subscribe & turn on notifications ————————————————————————————————SPONSORS

In this episode of PING, APNIC Chief Scientist Geoff Huston and I discuss a traffic behaviour in the DNS which Geoff has noticed in the labs advertising based experimental data capture. Virtually every DNS query Geoff sees, he sees twice (or more). For a cohort of about 150 million unique DNS labels on a given day, The Labs system is collecting 270 million incoming DNS queries. Thats a lot of duplication. What's going on? The advertising data collection depends on issuing unique DNS queries, which in turn generate unique web page serves. This allows measurement of internet-wide behaviour on about 30 million browsers, games, devices every day. Not to say that these names and web URLs are not routinely seen by more than one entity, intermediary systems such as caches and proxies as well as re-visiting old open website tabs on a browser can cause this. The point is that after the "first" fetch, the subsequent fetches can usually be held to be re-presentations of the same experiment and can therefore often be discarded (in the case of the web). But, for the DNS which has always had an element of unreliable transport, and which in turn invites measurement of features like DNSSEC which cause SERVFAIL messages, and demand repeated attempts to "find the DNS name-to-address mapping, re-fetching is itself something under test. How many resolvers lie behind a given users systems? How many kinds of resolver (DNSSEC enabled, or not) does the user depend on? Finding that almost all DNS queries are repeated, and it turns out repeated very quickly invited Geoff to have a look at what's going on "under the covers". There are some patterns behind what's being seen which Geoff explored in the APNIC Labs Blog.

Michael Saylor's "never sell" era is OVER. Strategy sold 3,588 BTC (~$216M) at a 20% loss to cover $1.8B in dividend obligations — and Canadian pensions are holding close to $1B of MSTR stock. On June 29, Strategy's board adopted its "Digital Credit Capital Framework": $1B stock buybacks, a 12% STRC dividend, and a $1.25B "BTC Monetization Program" authorizing Bitcoin sales "when strategic." CEO Phong Le calls it "evolving from one-way capital issuance to active capital management." The mNAV premium that financed five years of buying has collapsed from 2.66x to around 1x — and one in three Bitcoin treasury companies now trades below the value of its coins. In this episode of the Canadian Bitcoiners Podcast:- Strategy's pivot: the mNAV death spiral, coins sold at a 20% realized loss, insider selling, and JPMorgan's $2.8B-$11.6B index-exclusion warning- The Canada connection: CPPIB, AIMCo, National Bank, RBC and HOOPP hold ~$1B of MSTR — your pension bought the wrapper trade- A quantum-proof recovery tool that works for everyone except Satoshi's 1.1M BTC- OkoBot: malware that fakes your Ledger/Trezor recovery screen- Global hashrate is shrinking — but Pakistan is up 733%- CleanSpark signs a $6.6B, 20-year AI data center lease- Canada bans crypto political donations under Bill C-25- Clown World North: Stan Cho's $16,203 hotel bill, Canada Post's $30.8M in bonuses against a $1.57B loss, and $159,800 in flight catering- The BIS confirms Canada's housing crash is the biggest on record as 56,400 Canadians leave in a year The leverage cycle is unwinding — the conviction cohort isn't. Long-term holders just hit a record 14.85M BTC. ETF outflows and treasury-company stress are paper Bitcoin changing hands; the base layer doesn't care. Hold your own keys. — Canadian Bitcoiners Podcast- Website: https://canadianbitcoiners.com- X: @CanadianBTCPod- Subscribe & turn on notifications ————————————————————————————————SPONSORS

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president could still fire commissioners, as the Court now permits, but if those firings broke quorum, the agency would be unable to proceed until replacements were confirmed. The guardrail would

This week's video transcript summary is here. You can click on any bulleted section to see the actual transcript. Thanks to Granola for its software.There was an issue with this only going to paid subscribers, so sending it again. Apologies to those who get it twice. I appreciate being paid so feel free to upgrade if you enjoy TWTW.EditorialIntelligence: Who Owns it?This week the word “AI” feels too small.AI is a technology. Intelligence is its product. And if intelligence is the product, the question is no longer just: Which model is best? Who has the cheapest tokens? Who owns the weights? Who controls the data center? Those are important questions, but they are lower in the stack.The bigger question is simpler and more political:Who owns intelligence?That sounds abstract until you make it concrete. Intelligence is becoming something companies can capture, package, serve, meter, route, improve, and sell.It can write code, answer questions, design molecules, automate offices, run agents, draft legal work, advise scientists, serve consumers, and reshape workflows. It is not merely software. It is a general-purpose capability. And all humans could benefit from more of it.General-purpose capabilities have a habit of becoming public questions. But the default answer, that public good is best delivered by government, is the wrong answer in this context.The Product Is IntelligenceWe should stop talking about AI as a feature and start talking about intelligence as the universal thing that is delivered as an input to the world.Water is an input. Electricity is an input. Literacy is an input. Connectivity is an input. Once a society depends on them, access stops being optional. Nobody needs government to build every well, power plant, school, or network. But everybody understands that a civilization cannot be organized around less than universal and reliable access to foundational inputs.Intelligence is reaching that level of importance now that we all know it is real.Government should not own it, operate it, or develop it. Quite the opposite. Companies are the right actors to build fast, compete hard, improve models, serve customers, and discover the real use cases. Self-interest is a useful framing here. Markets are good at finding demand, reducing costs, and turning invention into services people actually use.Companies are the right operators, developers, and owners. But that does not settle the real question of who owns the benefits. That is an economic question.If intelligence becomes metered infrastructure, what happens to the value it creates?The Ownership StackThis week's articles keep circling the same issue from different directions but in the nature of ‘circling' never quite nail it.Jamin Ball's “Own Your Weights” starts with the enterprise version of the question. Owning a model file is not enough. The durable asset is the loop: the data flywheel, the evaluations, the reinforcement system, the workflow learning, and the operating context that lets capability compound.Benedict Evans' “Ways to Think About Token Pricing” adds the market layer. Tokens may become essential, abundant, and cheap, like mobile data. But being essential does not guarantee that the token layer captures the value. The money may move up the stack to whoever owns the workflow, the customer, the distribution, or the application.Alex Karp's fight with the labs, reported in “Alex Karp Is Saying What Every Angry CEO Is Thinking About AI”, is the same argument in sharper enterprise language. Companies are afraid that model providers will not just sell intelligence, but learn from customer workflows and then move into the markets where those workflows create value. The “All-in” group are echoing Karp's view.And “What Is Loop Engineering, and Who Owns It?” names the new contested terrain. The loop is where intelligence meets the world. Whoever owns the loop owns the learning. Whoever owns the learning owns the compounding asset.That is why “who owns intelligence?” is not a slogan. It is the question under the model layer, the application layer, the enterprise layer, and the economic layer.Because intelligence is the product, the tools creating it are fragmented and competitive. So there is no logic in trying to discuss this at the level of a single company or set of tools and models.The Old Promise Was That Commerce Would Tame PowerThe essays this week give the historical backdrop.Deirdre McCloskey, in “What Really Caused the Industrial Revolution”, argues that modern growth came not simply from capital accumulation, but from a change in permission: ordinary people were allowed to innovate, trade, build, and be honored for it.That matters because intelligence could be another expansion of permission. It could make more people capable of building, learning, creating, coding, researching, translating, selling, and coordinating. It could lower the cost of competence.But only if access is broad.Paul Krugman's “AI in an Age of Oligarchy” warns that the same technology lands differently in different political economies. A new general-purpose technology entering a broad, open, upwardly mobile society is one thing. The same technology entering a concentrated economy, with extreme wealth and weak counterweights, is another.Tim O'Reilly's Economist essay, “Elon Musk is building a form of capitalism that Adam Smith would hate”, makes the governance point more directly. The old liberal hope was that commerce would tame arbitrary power. Markets, boards, courts, shareholders, disclosure, and competition would discipline the prince.But what if the prince uses markets to escape discipline?Henry Farrell's “political economy of billionaire derangement” pushes the same point. Founder culture, monopoly ambition, peer rivalry, weak correction mechanisms, and vast private control can amplify appetites rather than restrain them.The danger with intelligence is not that companies build it. They should. Companies build it, meter it, use public tolerance and public infrastructure to scale it, learn from everyone who uses it. All of those things are inevitable and healthy. Market forces will sort out winners from losers. The real danger is that the winners treat all of the surplus produced as purely private.Metered Intelligence Creates SurplusIf metering is not the problem, what is?The problem is pretending that metered intelligence creates value only for the metering entity. Metering water is only tolerated as a public good. If the public were blackmailed by a private water company with the threat of no water we would all rebel.Once we understand that the product of AI is intelligence we can see that every time intelligence is used, there is the immediate transaction: the user pays, the provider serves.But there is also system value. Usage creates signals. Workflows reveal patterns. Prompts, corrections, failures, preferences, integrations, edge cases, and business processes all help define where intelligence is useful and how it should improve. Intelligence breeds intelligence.Even when customer data is contractually protected, the market learns. The platform learns where demand is. The product team learns which workflows matter. The ecosystem learns which jobs are vulnerable, which tasks are automatable, and which parts of the economy can be reorganized around machine intelligence.So the surplus is not born in a vacuum.It rests on public science, public education, public data exhaust, public law, public infrastructure, public energy systems, public tolerance for data centers, and billions of human interactions. It is served by companies, but it is not made only by companies.This is why “Americans Deserve a Dividend From AI Companies' Riches” belongs at the center of this week's issue. The detail can be debated. The principle is harder to dismiss. If intelligence becomes a new foundational resource, then some part of the wealth it creates should flow back to the people whose society makes it possible. Intelligence did not suddenly appear. AI is built on the entire history of human intelligence. It benefits from it and at the same time evolves it.Not Nationalization. A Human Wealth Fund.If intelligence belongs to everybody, some conclude that government ownership of intelligence is the right outcome.Governments are not well suited to build, operate, or improve intelligence. They will move too slowly, regulate too early, politicize the wrong things, and confuse economic participation with operational control.Andrew McAfee's “Why I Didn't Sign the AI Open Letter” is useful here. His objection is not that the technology is unimportant. It is that steering too hard before we understand the shape of the change can become its own failure mode. Marc Andreessen's satire of AI regulation is less policy than temperament, but it captures a real Silicon Valley fear: that regulation can become permission, capture, and incumbency before it becomes wisdom.That fear should be taken seriously.But it does not answer the economic question. It answers only the operational one.How can the economic benefits of intelligence be distributed? The better answer is a sovereign human wealth fund.Call it a sovereign wealth fund if you must, but the phrase is too national. Intelligence will not respect borders. The leading companies are global. The models, chips, data centers, agents, platforms, and workflows will be transnational from the beginning. If the value created by intelligence is global, then the mechanism for sharing some of that value should begin with the companies global enough to capture it. The nice thing about xAI, OpenAI, and Anthropic is that they are supranational.These companies own and operate intelligence. Let them compete. Let them profit. Let them keep the incentives that make the system improve. But if intelligence is the new water, the wealth it creates cannot belong only to the companies that meter it. And they, themselves, have the power to fix it, even more than governments.Access will become a Human Right; Ownership Is the Economic DesignThis is where human rights come in. There is no right to access an AI model, yet. But there will soon be a need to change that.Not as a claim that every person is entitled to every frontier model at every moment for free. That is not serious. Capacity has costs. Models have costs. Inference has costs. Data centers have costs. Although those costs will decline over time, possibly quite quickly as self-learning models address costs.The claim is more basic: in a world where intelligence becomes a primary input into education, work, health, science, citizenship, creativity, and economic agency, baseline access to intelligence starts to look like a civic requirement.That could mean public access layers. It could mean education credits. It could mean open models. It could mean AI dividends. It could mean public-interest compute. It could mean taxes on rents. It could mean a company-initiated human wealth fund that returns some of the upside to society without handing the operating system to the state. The latter could couple wealth growth with universal distribution of ownership.The exact mechanism matters. But the distinction matters more.Government should not own intelligence. It should be universally available. And people should have a claim on the wealth intelligence creates.The Frontier Is Also PhysicalThe abstraction is not weightless.“The Fight Against AI Data Centers Is Just Beginning”, “New York becomes the first state to enact a data center moratorium”, Reuters on pollution from Musk's xAI power project, and DataGravity's “Who Captures Value in AI Infrastructure?” all say the same thing from the ground up.Intelligence uses land. It uses power. It uses water. It uses chips. It uses grid capacity. It uses neighborhoods. It uses public patience.That makes the value question unavoidable. A society can accept the buildout if the buildout is legible as shared progress. It will resist it if the costs are local, the profits are private, and the benefits feel enclosed.Who Owns the “Loop”?The week ends where it began.“Anthropic and Blackstone” are betting that implementation is the next trillion-dollar business. “Vint Cerf” is working on identity for agents on the open internet. “GPT-Red” points toward systems that improve their own robustness. “Kimi K3” adds another open frontier model to the global mix.The model race continues. The deployment race is accelerating. The governance race is behind.My view is this:The central product of this era is intelligence. Companies have figured out how to capture it, package it, serve it, and meter it. That is good. It should stay in the hands of builders who have the incentive to make it better.But intelligence is too foundational to become just another private toll booth. A significant part of it will turn out to be free to users.As intelligence becomes a general-purpose resource, then access to it becomes a human-capability question, and the surplus from it becomes an economic-justice question. Not because government should run it. Because government should not run it. The operating layer belongs with companies. The wealth question belongs with everyone. But companies are best placed to turn that into a process of distribution.The question is not whether companies should build intelligence. They should.The question is whether humanity gets a stake in the wealth created by the thing that may soon become its most important shared input.Contents* Essays* Deirdre McCloskey on What Really Caused the Industrial Revolution* AI in an Age of Oligarchy* Elon Musk is building a form of capitalism that Adam Smith would hate* Murky Mirror: Truth and Consequences* The political economy of billionaire derangement* Is there any “oligarchy” to fight?* AI* Nearly 200 Economists and Tech Leaders Warn of A.I. Threats* Why I Didn't Sign the AI Open Letter* Own Your Weights* Ways to Think About Token Pricing* Alex Karp Is Saying What Every Angry CEO Is Thinking About AI* The AI Agents Are Coming for Microsoft Office* What Is Loop Engineering, and Who Owns It?* The Fight Against AI Data Centers Is Just Beginning* 6 months to live for open models* Americans Deserve a Dividend From AI Companies' Riches* Who Gets to Define the Frontier?* GPT-Red: Unlocking Self-Improvement for Robustness* Anthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just models* Vint Cerf is working on a plan to unleash AI agents on the open internet* xai-org/grok-build, now open source* The Pulse: What can we learn from Bun's rapid Rust rewrite with AI?* Orphan risks at the frontier of artificial intelligence* The Lab of the Future Should Feel Like a Data Center* Why AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”* Kimi K3 Tech Blog: Open Frontier Intelligence* Venture Capital* Three Years In* Venture Has Rarely Looked More Bifurcated* The Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active Now* Are Prediction Markets Doomed to Fail?* Regulation* Exclusive: The Next Frontier of the Deportation Wars: College Campuses* The Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.* India's crackdown on a new WhatsApp feature risks setting a global precedent* Let's build a children's public internet* Computer cops* Google is better at playing the AI regulations game* Infrastructure* Who Captures Value in AI Infrastructure?* New York becomes the first state to enact a data center moratorium* Pollution from Musk's unpermitted xAI power project hits hardest in Black communities* Interview of the Week* The End of the End of Geography* Startup of the Week* Radical AI's Joseph Krause: The Scientist Building The “Waymo” Lab For New Materials* Post of the Week* Marc Andreessen on AI RegulationEssaysDeirdre McCloskey on What Really Caused the Industrial RevolutionYascha Mounk and Deirdre McCloskey | Persuasion | July 11, 2026Yascha Mounk interviews Deirdre McCloskey about her argument that the modern world's economic liftoff came less from capital accumulation than from a change in ideas. McCloskey says both left and right versions of the conventional story rely too heavily on investment: the left stresses exploitation and surplus value, while the right stresses virtuous saving by capitalists. Her objection is historical and economic. Human beings had always invested, from irrigation works and Roman roads to seed grain, and simple accumulation quickly runs into diminishing returns.McCloskey's alternative is that northwestern Europe, first Holland, then Britain and Scotland, and then the North American colonies, developed a liberal ideology that changed who was allowed to innovate and be honored for it. The conversation links that shift to the erosion of inherited hierarchy, the spread of dignity for ordinary commercial life, and a moral vocabulary in which liberalism is not merely procedural but connected to virtues and values. The point is not that machines, coal, trade, and institutions did not matter, but that they do not explain the scale and timing of modern enrichment without a cultural permission structure for innovation.The interview also turns to the contemporary defense of liberalism. Mounk frames the series around the worry that liberalism is often treated as too thin to command allegiance, while its opponents speak more directly to moral passions. McCloskey's case is that liberal societies became rich because they dignified experimentation and ordinary enterprise, and that liberals need to recover the moral language behind that claim.Read moreAI in an Age of OligarchyPaul Krugman | Paul Krugman | July 12, 2026Paul Krugman frames AI as a major technological shock arriving inside an already unequal political economy. The post says AI's economic and social effects may take years to understand, but argues that the setting matters now: America has much greater wealth concentration and political inequality than it did in the 1950s and 1960s, when progressive taxation, stronger regulation, and more active antitrust might have contained some of the destructive effects of a new technology.Krugman's opening claim is that the same technology would likely have different consequences in a more level society. In today's United States, he writes, extreme wealth is both a cause and effect of policies that favor a small elite, including low effective taxes on capital and high incomes, weak enforcement of worker protections and antitrust, and cuts to programs that benefit ordinary Americans.The article is explicitly more about oligarchy than AI. Krugman says the paid sections document the rise of the “.0002%,” the economics and politics of extreme wealth, how oligarchy will shape AI's impact, and possible policy paths. His caveat is that AI itself may still produce a pushback against oligarchy, but absent that, he expects the pre-existing concentration of wealth and power to magnify AI's downsides.Read moreElon Musk is building a form of capitalism that Adam Smith would hateAuthor: Tim O'Reilly Published: July 12, 2026Tim O'Reilly argues that Elon Musk is using the legal forms of shareholder capitalism to escape the restraints that shareholder capitalism was supposed to impose. The article begins with SpaceX's public-market structure: ordinary public investors get little meaningful governance power, Musk keeps roughly 85 percent of the votes through super-voting shares, buyers waive jury trials and class actions, the company qualifies as controlled, and removal of Musk depends on the share class he controls. In O'Reilly's framing, that is not ordinary founder control; it is a design for being answerable to no one, possibly beyond Musk's own lifetime.The killer detail is the article's turn through Albert Hirschman, Montesquieu, James Steuart, Adam Smith, and Keynes. Older defenses of commerce held that markets would tame princely passions because the self-interest of merchants was safer than arbitrary rule. O'Reilly says Musk reverses that hope. The market discipline that was supposed to cage the prince has become the lever by which the prince raises capital, removes feedback loops, and carries private power into politics, government, Mars, robots, AI, or whatever ambition comes next.The pull is the link to AI governance. O'Reilly says corporations are already a kind of artificial intelligence: narrow-input systems that act at a scale no individual human can match. Their partial controls include independent boards, shareholder votes, courts, disclosure, regulators, public pressure, and activism. If the leaders building frontier AI strip those alignment mechanisms out of their own companies, the governance of the company becomes a preview of the governance of the machine.Read more: The EconomistMurky Mirror: Truth and ConsequencesAuthor: Esther Dyson Published: July 14, 2026Esther Dyson argues that today's institutional crisis is better viewed through the 14th century than through recent political history. Using Barbara Tuchman's A Distant Mirror as her frame, she compares a world of famine, plague, church schism, feudal predation, and purposeless war with a present in which institutions again feel brittle, incentives are badly aligned, and power is shifting into forms that are hard to govern.The killer detail is the historical analogy between land, corporations, and AI. Dyson moves from nobles who controlled serfs and territory, to the East India Company as a quasi-sovereign business, to today's AI systems and data centers as a possible new sector that crosses and weakens both nation-states and companies. The question is whether AI becomes a new kind of private land, owned by a new nobility, or an open prairie that many people can cultivate.The pull is human attention. Dyson says the central question is not what AI will do to people, but how people will react to it: whether they can value love, kindness, embodied attention, and artisanal human presence in a world of seductive artificial offerings.Read more: SourceThe political economy of billionaire derangementAuthor: Henry Farrell Published: July 15, 2026Henry Farrell argues that the visible political radicalization of some Silicon Valley billionaires is not a random personality quirk, but a product of the political economy that made them. Starting from Tyler Cowen's dismissal of “billionaire derangement syndrome” and Tim O'Reilly's warning that Elon Musk is using shareholder capitalism to escape shareholder restraint, Farrell flips the phrase: the question is why billionaires themselves can become deranged.The killer detail is Farrell's use of Peter Thiel as both theorist and example. Thiel's Stanford lectures described startups as monarchies and founders as figures vested with unusual power, while Silicon Valley culture rewarded eccentricity, monopoly ambition, and founder exceptionalism. Farrell says those ideas combined with dense founder-investor networks, peer rivalry, and weak correction mechanisms to amplify rather than discipline princely appetites.The pull is the ideological problem for classical liberals who once saw tech wealth as an ally of markets and freedom. Farrell says commerce did not tame the passions; in parts of Silicon Valley, the passions have begun to devour markets, institutions, and the liberal story that justified them.Read more: SourceIs there any “oligarchy” to fight?Matthew Yglesias | Slow Boring | July 16, 2026Matthew Yglesias argues that “oligarchy” is a rhetorically powerful but analytically loose way to describe American politics. The post begins from Bernie Sanders' “Fighting Oligarchy” tour, Amy Klobuchar's warning about a MAGA “broligarchy,” and the long afterlife of the Martin Gilens and Benjamin Page paper that was widely summarized as showing that only the rich matter in policy outcomes. Yglesias says the evidence supports a weaker claim: affluent people and business leaders have unusual access and influence, but that is not the same as rule by a small cabal.His main distinction is between inequality and oligarchy. The Gilens-Page measure treated the top 10 percent of households as “the wealthy,” and later critics found that rich and middle-class preferences usually align; in the cases where they differ, the rich win about 53 percent of the time. Yglesias also says business executives get special access partly because their decisions are materially important to communities, jobs, investment, and local tax bases, not only because of campaign donations.The post preserves Jerusalem Demsas' counterpoint from their podcast discussion: privileged donor and business access can still violate democratic equality even if the oligarchy label overstates the structure of power. Yglesias' narrower claim is that Democrats should be precise about what problem they are trying to solve, because donor influence can also push the party left on climate and cultural issues in ways that alienate many voters.Read more: Slow BoringAINearly 200 Economists and Tech Leaders Warn of A.I. ThreatsAuthor: Ben Casselman Published: July 13, 2026Ben Casselman reports on “We Must Act Now,” a statement warning that artificial intelligence could transform the economy faster than any previous technology and that policymakers need to move faster to understand and respond. The statement says AI may become radically more powerful over the next 10 years, bringing risks such as large-scale job displacement as well as opportunities such as higher living standards. Nearly 200 people signed, including 15 Nobel laureates, the chief economists of OpenAI and Anthropic, Anthropic co-founder Jack Clark, former Google CEO Eric Schmidt, and venture capitalist Vinod Khosla.The killer detail is who joined the warning. Casselman notes that the signatories include economists who have historically been skeptical of Silicon Valley's most dramatic AI job-loss forecasts, including Daron Acemoglu and Simon Johnson, the MIT professors who won the 2024 Nobel in economics. Erik Brynjolfsson, who helped organize the statement, says there has been a notable change in the profession and that economists and policymakers are not ready for the “tsunami” he sees coming.The pull is the measurement problem. The statement does not offer a specific policy menu, but calls for economists, policymakers, and industry leaders to understand the economics of transformative AI and steer it toward complementing humans. Brynjolfsson says one high priority is better data on AI's spread and impact, because current measures tell conflicting stories about job losses and which workers are most exposed.Read more: The New York TimesWhy I Didn't Sign the AI Open LetterAuthor: Andrew McAfee Published: July 13, 2026Andrew McAfee explains why he did not sign “We Must Act Now,” the AI economy statement organized in part by his longtime collaborator Erik Brynjolfsson. McAfee agrees with the letter's starting point that AI is likely to become radically more powerful over the next decade and that it is a general-purpose technology. His objection is not to urgency or to studying AI's economic effects, but to the framing of risk, displacement, and institutional steering as the first move.The killer detail is McAfee's line edit. He says the original letter comes close, then “bounces off the crossbar” by calling for incentives, guardrails, and institutions to steer AI before we know enough about its actual impacts. He points to mixed current evidence: labor-market canaries, but also rising software job postings, low unemployment for younger workers, rising real median income, and claims that AI-adopting companies are adding workers faster than low-adopting peers. His worry is that the letter leans toward upstream governance and dirigisme when the evidence may call for capability building instead.The pull is his replacement statement. McAfee keeps the three-paragraph structure but changes the emphasis: AI is likely to become radically more powerful; like earlier world-changing technologies it will raise living standards while also bringing harms and shocks; and economists, policymakers, and technology leaders should build the capabilities to respond quickly and effectively. It is a concise version of the permissionless-innovation case inside the AI policy debate.Read more: The Geek WayOwn Your WeightsAuthor: Jamin Ball Published: July 10, 2026Jamin Ball argues that the enterprise AI debate about whether companies should “own their weights” or rent models from frontier labs is asking too narrow a question. A model weight file gives a company control over a point-in-time artifact, but not durable control over the capability stack. In his framing, the weight file is a melting ice cube: it does not get worse in absolute terms, but it falls behind as frontier systems improve and enterprise needs change.The killer detail is what Ball says companies really need to own: the data flywheel, reinforcement learning infrastructure, and evaluation harness that produce and improve the model. Simply deploying an open-weights model and declaring sovereignty leaves the enterprise with yesterday's capability and no way to compound workflow-specific learning.The pull is that enterprise AI control may be less about model ownership than operating ownership. The defensible layer is the system that turns company data, edge cases, business definitions, and evaluations into continuously improving performance.Read more: Clouded JudgementWays to Think About Token PricingAuthor: Benedict Evans Published: July 9, 2026Benedict Evans argues that today's AI token prices are a temporary signal from a supply-constrained market, not a reliable guide to long-term value capture. The open question is whether foundation models keep durable pricing power or become commodity infrastructure as data-center capacity, inference efficiency, and model competition all shift. His current read is that the visible market dynamics point toward commoditization unless something materially changes.The killer detail is the mobile data analogy. Evans says cellular networks became a trillion-dollar industry with hundreds of billions in capex after data usage exploded, but carrier stocks went nowhere because value moved up the stack. Tokens may behave similarly: an opaque unit tied to marginal cost, sold through bundles, essential to everything, yet not necessarily where profits accrue.The pull is uncertainty, not prediction. Evans lists paths to model dominance, including network effects, less competition, regulation, export controls, or a lab pulling ahead on execution, but says each requires a new fact not yet visible. Without that change, the model layer looks more like infrastructure beneath the products that capture value.Read more: SourceAlex Karp Is Saying What Every Angry CEO Is Thinking About AIAuthor: Tim Higgins Published: July 11, 2026Tim Higgins reports that Palantir CEO Alex Karp has turned corporate frustration with AI labs into a public argument about enterprise control. Palantir released a white paper, “Institutional Sovereignty in the Age of AI,” laying out steps companies and governments can take to protect themselves from OpenAI, Anthropic, and other foundation-model providers. The article links that paper to Karp's CNBC appearance, where he said “something has gone completely wrong” in the relationship between AI labs and customers and argued that enterprises are paying for tokens that create little value.The killer detail is the value-capture question. Higgins writes that Karp's critique has resonated because AI labs may gain power and insight from customer data, workflows, and decision-making, even when enterprise policies say customer data are not used for training. David Sacks amplified the concern by arguing that Anthropic is moving from the model layer into vertical applications such as science, security, legal, and coding, raising the fear that model providers will watch where value is being created and then move into those markets directly.The pull is that Karp is not alone, even if his style is unusually combative. Higgins notes that Satya Nadella has also warned that companies need to retain the learnings created when they use AI models, while Mark Zuckerberg has framed Meta's new model release partly around lower-cost frontier intelligence. The article presents Karp's campaign as one sign that established technology companies and large enterprises are trying to define where they fit when AI labs become central infrastructure, application competitors, and potential IPO giants at the same time.Read more: The Wall Street JournalThe AI Agents Are Coming for Microsoft OfficeAlex Wilhelm | Cautious Optimism | July 11, 2026Alex Wilhelm argues that one of the week's quieter AI questions is whether the productivity market that Microsoft successfully moved into subscription software is now being attacked by agentic tools. The piece begins with the infrastructure backdrop: SK Hynix raised $26.5 billion in a U.S. listing while building U.S. HBM and advanced-packaging capacity, and memory, chip, and foundry companies are now priced for sustained AI demand.Wilhelm then says the AI conversation has shifted quickly from raw capability to cost per task. He cites new model releases and vendor language emphasizing cheaper agentic and coding models, faster performance, and lower dollars per task. That matters because lower costs make it more plausible for AI systems to take on routine knowledge work at scale rather than remain a premium coding assistant market.The core of the article is Microsoft Office. Wilhelm notes that Microsoft turned Office from a one-time purchase into Microsoft 365, a large recurring revenue business with tens of millions of subscribers and a major productivity segment. Now, he says, late-stage unicorns and AI labs are pushing into the same territory: Anthropic's Cowork was reportedly used mostly outside software development, OpenAI merged ChatGPT and Codex into a tool for creating sheets, slides, docs, web apps, and long-running work, and other companies are building agentic coworkers that connect business data to documents, workflows, schedules, alerts, and apps.The article's caveat is that Microsoft has survived major platform shifts before. The argument is not that Office disappears quickly, but that the definition of office software is broadening from documents and spreadsheets into AI systems that can create, monitor, and act across workplace data.Read moreWhat Is Loop Engineering, and Who Owns It?Author: Nilesh Barla Published: July 11, 2026Nilesh Barla argues that “loop engineering” is becoming a distinct discipline because production AI agents now fail less at single prompts than at runtime: when to stop, what state to preserve, and how to recover after a bad step. Prompt engineering shapes one model call, and context engineering shapes what the model sees, but loop engineering shapes what a sequence of calls actually does.The killer detail is the three-primitives frame. Barla says a real agent loop needs halt conditions, state carryover, and recovery paths, then maps teams across five maturity levels. At the lowest level, an agent is just a model call in a for-loop with a step cap and raw history; by the higher levels, the system has structured state, explicit planning, replay, evaluation, and self-repair.The pull is organizational. If agents are becoming production systems rather than demos, someone has to own the runtime itself. The loop engineer is the role Barla gives to the person responsible for making long-running agent work dependable.Read more: Adaline LabsThe Fight Against AI Data Centers Is Just BeginningEmma Roth | The Verge | July 12, 2026Emma Roth argues that community resistance to data centers has moved from an early warning sign into a national political fight as AI facilities grow larger, more power-hungry, and more visible to nearby residents. The article starts with Apple's failed 2015 plan for a $1 billion data center in Athenry, Ireland, where a small group of residents challenged the project over noise, light pollution, flooding, traffic, and wildlife effects until Apple abandoned it in 2018.The current data-center buildout is presented as much larger and more contentious. Roth writes that residents now cite rising energy costs, water quality, noise, light pollution, and greenhouse gas emissions, while the U.S. Energy Information Administration expects commercial energy demand to surpass residential demand this year because of AI data centers and Goldman Sachs expects data-center power demand to double by 2027.The central evidence comes from Data Center Watch, which says protesters blocked or delayed at least 75 U.S. projects worth $130 billion from January to March, with active opposition groups more than doubling from 396 at the end of 2025 to 833 by the end of the first quarter of 2026. Roth also cites QTS abandoning a $12 billion Wisconsin campus, Delaware City regulators blocking a 580-acre project under the Coastal Zone Act, opposition stopping a QTS project in Prince William County, and pressure that pushed Kevin O'Leary to downsize the proposed 40,000-acre Project Stratos in Utah.The policy section describes a split between federal acceleration and local resistance. President Trump has treated data centers as part of the AI race with China and fast-tracked construction, while some Republican candidates are distancing themselves from that position ahead of midterms. Sanders and Ocasio-Cortez have proposed a moratorium until price and environmental protections exist, bipartisan lawmakers are backing ratepayer-protection measures, and states including Florida, Idaho, and Washington have passed rules on cost shifting, water use, and tax breaks. Roth's caveat is that the policy patchwork is still incomplete, leaving many communities to fight project by project.Read more6 months to live for open modelsAuthor: Nathan Lambert Published: July 12, 2026Nathan Lambert argues that open-weight AI models are facing their most serious policy test so far because U.S. officials are beginning to discuss concrete controls rather than abstract safety concerns. He says reported White House conversations about a new executive order may initially target Chinese-origin models and government use, but could create a broader review habit for frontier open models. His forecast is that a model above the capability range of GPT-5.5, Claude Opus 4.8, or GLM-5.2 could trigger a ban or indefinite delay within six months.The post separates two policy fights that are becoming intertwined: distillation and frontier capability. Lambert says the distillation campaign against Chinese models has become a form of regulatory capture because Anthropic and other closed-model companies would gain economically if Chinese open models were banned. He does not dismiss IP protection, but argues that if a closed model's capabilities are dangerous enough to justify restricting open models, the lab also has to explain why those capabilities are exposed through a queryable API. He cites unauthorized access to Anthropic's Mythos private beta as evidence that APIs are not automatically secure.The broader claim is that a unilateral U.S. ban would hurt positive actors more than bad actors if comparable open models remain available elsewhere. Lambert says the only durable ceiling would require global agreement, which does not exist, and that open models can improve safety by allowing broad inspection, adaptation, and understanding. His proposed near-term off-ramps are a strong U.S. open model release from companies such as Microsoft, Meta, or Reflection, and a broader coalition of open-source beneficiaries lobbying for safe rollout rather than prohibition.Read more: SourceAmericans Deserve a Dividend From AI Companies' RichesAuthor: Scott Stanford Published: July 14, 2026Scott Stanford argues that proposals to give the government a stake in AI companies miss the point unless ordinary citizens directly receive and control the upside. Sam Altman has discussed giving up equity in OpenAI, Washington already owns a stake in Intel, Nvidia is sharing China chip revenue, and Bernie Sanders wants large AI labs to contribute half their stock to a sovereign wealth fund. Stanford says those ideas all park value with the state, not with people.The killer detail is New Carlisle, Indiana, where AWS's Project Rainier is turning cornfields into one of the world's largest AI superclusters. The project is planned to run up to a million chips, draw more than two gigawatts of power, and represents an investment that has grown from $11 billion to $13.8 billion. Stanford uses that local transformation to argue that AI's public bargain should be visible at the household level.The pull is design. A citizen AI dividend would have to specify who earns a stake, how they hold it, and when they see cash. Without that mechanism, the AI wealth debate remains a fight over government balance sheets rather than public ownership.Read more: SourceWho Gets to Define the Frontier?Author: Mark Daley Published: July 14, 2026Mark Daley argues that Demis Hassabis is right to call for a serious institution to verify frontier AI systems, but that the power to test models is also the power to govern them. Hassabis's proposed Frontier AI Standards Body would get privileged pre-release access to advanced models, testing compute, held-out evaluations, support from national labs and security agencies, third-party auditors, and eventually authority to block models from the American market or coordinate a slowdown.The killer detail is Daley's constitutional objection. He says the proposal sometimes looks like a scientific lab, a standards body, an industry regulator, a licensing authority, and an emergency security council at once. Combining those roles because each requires technical expertise would be like putting the central bank, auditor-general, and Supreme Court in one building and calling it efficient.The pull is standard-setting. Daley's concern is not that verification is unnecessary, but that whoever writes the tests, decides what passes, adjudicates disputes, and grants market access may end up defining the frontier itself.Read more: SourceGPT-Red: Unlocking Self-Improvement for RobustnessOpenAI | OpenAI | July 15, 2026OpenAI describes GPT-Red as an internal automated red-teaming model trained to find prompt-injection vulnerabilities at a scale human red teams cannot match. The post says AI systems increasingly encounter third-party data through browsers, connected apps, local files, and tools, creating opportunities for malicious instructions hidden in emails, webpages, tool responses, or code repositories. Human red-teaming remains part of OpenAI's safety process, but the company says it is time-intensive and cannot generate enough diverse adversarial examples for model training.The system is trained through self-play reinforcement learning, with GPT-Red rewarded for eliciting valid failures and defender models rewarded for resisting attacks while still completing their tasks. OpenAI says the training environments specify threat models across settings such as local files, webpage banners, email bodies, and tool outputs. The model is kept separate from deployed production models because it is intentionally trained with malicious capabilities.OpenAI reports that GPT-Red generalized beyond its training set, including an internal replication of the indirect prompt-injection arena from Dziemian et al. (2025), where it found successful attacks in 84% of scenarios compared with 13% for human red-teamers. The post also says GPT-Red transferred attacks from simulation to a live autonomous vending-machine agent, causing price changes and order cancellations, and outperformed a prompted GPT-5.5 baseline against a Codex CLI agent on held-out data-exfiltration tasks.The article's main robustness claim is that OpenAI has used GPT-Red and predecessor models in training since GPT-5.3, with later GPT releases becoming more resistant to prompt injections. It says GPT-5.6 Sol has six times fewer failures on OpenAI's hardest direct prompt-injection benchmark than the best production model from four months earlier, that a “Fake Chain-of-Thought” attack class fell from more than 95% success against GPT-5.1 to below 10% against GPT-5.6 Sol, and that GPT-5.6 Sol fails on only 0.05% of GPT-Red's direct prompt injections. OpenAI says general capabilities and targeted over-refusal evaluations were not harmed, and says a preprint with more details will follow.Read moreAnthropic, Blackstone bet the next trillion-dollar AI business is implementation, not just modelsRebecca Bellan | TechCrunch | July 15, 2026Rebecca Bellan reports that Ode with Anthropic is the $1.5 billion AI implementation company launched by Anthropic with Blackstone, Hellman & Friedman, Goldman Sachs, and other backers. The article says the venture reflects a growing belief among frontier AI labs that enterprise adoption requires more than better models: customers need engineers who can embed inside businesses and turn AI into working systems.Ode was originally conceived by Blackstone after it used both large consulting firms and smaller AI services boutiques across its portfolio companies. TechCrunch reports that Fractional AI, an AI engineering services startup, stood out and was acquired by the joint venture shortly after the venture was announced. Fractional now forms the foundation of Ode, which has 100 engineers and works closely with Anthropic's applied AI team to identify where the technology can affect specific businesses.Ode CEO Chris Taylor tells TechCrunch that the company could someday become a trillion-dollar business if it scales without losing quality. He says an ideal customer is one whose CEO treats the AI project as a top one or two priority, whether it is a major product feature or the reworking of a core business process. Ode will operate under a “Claude-first” principle, using Anthropic technology whenever possible, but the article says it can use rival AI products when needed.The article's central implementation argument comes from Ode chief technologist Eddie Siegel, who says model selection matters but is not where most of the engineering effort goes. He compares it to the choice of programming language in software: one ingredient in a system that still has to be engineered. Bellan writes that Ode's challenge is hiring and training enough elite generalist engineers, many of them former founders, while competing with OpenAI's The Deployment Company and consulting giants that have built their own forward-deployed engineering teams.Read moreVint Cerf is working on a plan to unleash AI agents on the open internetTim Fernholz | TechCrunch | July 15, 2026Tim Fernholz reports that Vint Cerf, after leaving Google, is advising Innovation Labs on an open architecture for identifying AI agents online. Innovation Labs is a subsidiary of Identity Digital, a DNS registry company, and its proposal is to use domain-name infrastructure as part of a system for agent identity, accountability, and auditability. The premise is that agents will need a way to identify themselves if they move beyond proprietary systems and begin interacting across the open internet.The concrete proposal is DNSid, a registry that links an AI agent to an existing internet domain and uses cryptographic proofs to log its registration over time. Innovation Labs says it is trialing the standard with unnamed hyperscalers and identity companies. Cerf frames the problem around authority and accountability: what authority an agent has, where that authority came from, who is accountable for the agent's behavior, how its identity is established, and why anyone should trust it.The article's caveat is that standards are still emerging and agents are more active than static domains. Cerf says the period may be both fascinating and exasperating because the functionality is powerful and interoperability is unresolved. He compares the adoption problem to TCP/IP: competing systems may not work together until users push for functional interoperation. He also says an agentic economy is not inevitable, but that people will try to build it because delegating work to agents will be easier.Read more: TechCrunchxai-org/grok-build, now open sourceAuthor: Simon Willison Published: July 15, 2026Simon Willison argues that xAI's decision to open-source Grok Build is best understood as a trust repair move after a severe privacy failure. The CLI had triggered backlash when users realized that running it in a directory could upload the entire directory to xAI's Google Cloud buckets, including one user's reported SSH keys, password manager database, documents, photos, and videos. xAI disabled the feature, said previously retained coding data would be deleted, and released the code under Apache 2.0.The killer detail is what the codebase reveals. Willison counts 844,530 lines of Rust, only about 3% of which appears vendored, and finds remnants of the upload system still present but disabled: gcs.rs contains Google Cloud upload code, while upload_session_state() now returns a hard-coded session_state_upload_unavailable error. He also notes copied or ported tool implementations from Codex and OpenCode, prompt files, and a terminal Mermaid renderer.The pull is that terminal coding agents are becoming large, intricate software systems in their own right. The privacy failure mattered because these tools operate inside the directories where developers keep their most sensitive work; the open-source release matters because trust now depends on inspecting what an agent can see, send, and do.Read more: SourceThe Pulse: What can we learn from Bun's rapid Rust rewrite with AI?Author: Gergely Orosz and Ivan Klaric Published: July 16, 2026Gergely Orosz and Ivan Klaric argue that Bun's AI-assisted rewrite from Zig to Rust is a practical sign of how software engineering changes when models can take on large, bounded migrations with clear feedback loops. The piece does not treat the rewrite as magic: Jarred Sumner first spent hours turning design judgment into a detailed porting guide, then used adversarial review, parallel agents, compiler errors, and tests to force the work toward correctness.The killer detail is the scale. Bun had 535,496 lines of Zig, 1,448 files, and 22 million monthly downloads, making a conventional rewrite a year-long freeze the team could not justify. Using Fable, Sumner split the work across 64 agents, produced about 6,500 commits, and got the migration done in 11 days at an estimated API cost of $165,000.The pull is economic, not theatrical. If a one- or two-year migration can become an 11-day project, AI coding is not just faster autocomplete; it changes which technical debts are worth paying down.Read more: SourceOrphan risks at the frontier of artificial intelligenceAuthor: Andrew Maynard Published: July 16, 2026Andrew Maynard argues that frontier AI safety frameworks are creating “orphan risks”: harms that companies can see, but do not formally own because they are hard to quantify, do not fit catastrophic-risk thresholds, or fall outside audit-friendly compliance machinery. His target is not existing frontier safety work, but the narrowing effect that happens when private companies decide which risks count as governable.The killer detail is Maynard's contrast between measurable model dangers and threats to value. He points to Meta's three-day Galactica collapse, OpenAI's 2023 board crisis, safety-team departures, and wellbeing litigation as examples of risks that damaged trust, culture, legitimacy, or users without fitting cleanly into conventional model-risk categories. The proposed fix is an orphan-risk register: a public record of risks a company considered and chose not to manage, with reasons.The pull is accountability. Frontier developers' internal scoping choices have become a de facto layer of public governance, so the question is no longer only which risks they manage, but which risks they quietly leave outside the frame.Read more: SourceThe Lab of the Future Should Feel Like a Data CenterLatent.Space with Andy Beam and Rafa Gomez-Bombarelli | Latent.Space | July 16, 2026Latent.Space interviews Lila Sciences CTO Andy Beam and chief science officer for physical sciences Rafa Gomez-Bombarelli about the company's attempt to build an AI-run science factory. The post describes Lila's thesis as treating the lab itself as an “infinite token generator”: if internet data drove the first era of AI scaling, experimentally verified scientific data may be the next scarce training source. Lila is trying to produce that data with robotics, lab instruments, orchestration software, and AI models wired into the wet lab.The central analogy is the lab as data center. Instruments are nodes on a graph, a magnetically levitating transport layer moves materials between them, and experiment scheduling looks like a compute queue. Beam says Lila is not simply an automation company, because the point is not just throughput; it is flexibility, generalization, and experiment capture. The post says Lila has built more than 10 trillion experimentally validated “scientific reasoning tokens,” not internet text or biological sequences.The interview ranges across biology, chemistry, drug discovery, materials science, and the limits of automation. It notes that Lila rebuilt one gas-sorption measurement to run roughly 2,500 times faster, claims its general models can transfer priors from small-molecule chemistry to metal-organic frameworks for carbon capture, and describes model-suggested platinum-group-free electrocatalysts that moved from looking boring or wrong to becoming strong performers. The caveats are physical: experiments have runtimes, biology cannot always be accelerated, chains of thought can be unreliable narrators, and reward hacking becomes more dangerous when a model controls a real lab.Read more: Latent.SpaceWhy AMI Labs' Alexandre LeBrun won't call his AI “AGI” or “superintelligence”Kate Park | TechCrunch | July 16, 2026Kate Park interviews AMI Labs CEO Alexandre LeBrun about why Yann LeCun's world-model startup avoids the language of “AGI” and “superintelligence.” LeBrun says the terms are not useful because they lack stable definitions: “We never used the word AGI. And I just noticed that nobody is using it anymore; they switched to superintelligence.” His argument is that the practical frontier is not a label, but whether AI systems can understand and predict real-world states.The article explains the world-model thesis by contrasting language prediction with physical-state prediction. A large language model predicts the next word; a world model predicts the next state, such as what happens when a glass tips over. LeBrun says LLMs remain complementary and efficient for language, but the physical world is where current AI is weak. Robotics is the clearest case: hardware has advanced quickly, but robots are still brittle outside controlled routines because they lack context and situational understanding.AMI is still pre-product, but TechCrunch reports that LeBrun was in Seoul looking for industrial partners, researchers, and global companies. He says world models cannot be built entirely inside a lab because they need access to real environments. That is why South Korea appeals to AMI: robotics, semiconductors, manufacturing, and fast adoption create the kind of hardware-heavy context that software-only AI has barely touched.Read more: TechCrunchKimi K3 Tech Blog: Open Frontier IntelligenceKimi | Kimi | July 16, 2026Kimi introduces Kimi K3 as an open 3T-class frontier model aimed at coding, knowledge work, reasoning, multimodality, and long-context agentic use. The source describes the model as a 2.8T-parameter system built on Kimi Delta Attention and Attention Residuals, with native multimodality and a 1M-token context window. It says Moonshot AI plans to release model weights by July 27.The post presents K3 through benchmark and use-case sections rather than as a general product announcement. It reports results across coding, productivity, agentic, and multimodal evaluations, including DeepSWE, Terminal-Bench 2.1, Program Bench, SWE Marathon, FrontierSWE, PostTrain Bench, OfficeQA Pro, SpreadsheetBench 2, MCP Atlas, AutomationBench, BrowseComp, GDPval-AA v2, AA-Briefcase, MMMU-Pro, MathVision, BabyVision, OmniDocBench, and PerceptionBench. The source says all reported K3 results use maximum reasoning effort with temperature and top-p set to 1.0, and that different benchmark comparisons use KimiCode, Claude Code, or Codex harnesses depending on the test.Kimi's caveats are unusually concrete. The limitations section says K3 was trained in preserved thinking-history mode, so quality may become unstable if an agent harness does not pass historical thinking content correctly or if an ongoing session switches to K3 midstream. It also says K3's emphasis on long-horizon tasks can make it excessively proactive when it encounters minor issues or ambiguous intent, and recommends imposing explicit behavioral constraints for applications that require strict boundaries. The post adds that K3 remains behind Claude Fable 5 and GPT 5.6 Sol in user experience despite being competitive overall.Read moreVenture CapitalThree Years InAuthor: Tomasz Tunguz Published: July 10, 2026Tomasz Tunguz marks Theory Ventures' third anniversary by arguing that AI's central market effect is time compression. In his telling, model release cycles, company revenue milestones, enterprise adoption, and venture categories have all accelerated. Seed, Series A, and Series B still exist as financing labels, but they no longer cleanly describe company maturity when some seed rounds are larger than IPOs and the best AI companies can mature much earlier than prior software companies.The killer detail is the shift from models to inference. Tunguz argues that inference has become the dominant AI market because workloads and buyer preferences are fragmenting: video, batch, local, agentic, and real-time tasks each create different infrastructure needs. He compares this to databases splitting into OLTP, OLAP, vector, and streaming categories, with AI pushing the same specialization into inference infrastructure.The pull is that Theory sees the AI-native venture firm as part of the same pattern. The firm says it has analyzed twice as many investment opportunities with three investors working alongside a nine-person intelligence organization, using agents and research systems to map markets, source companies, and support diligence. The piece is both a market map and a statement about how venture itself is being rebuilt by the technology it funds.Read more: LinkedInVenture Has Rarely Looked More BifurcatedAuthor: Beezer Clarkson Published: July 14, 2026Beezer Clarkson points to PitchBook's Q2 report as evidence that the U.S. venture market has split into two very different realities. AI now accounts for more than 60 percent of all U.S. venture deal value, meaning the headline market can look active and well-funded even while much of the non-AI market is dealing with a much colder liquidity and fundraising environment.The thread uses that split as the setup for Clarkson's latest Origins episode with Alec Litowitz, founder of Magnetar and QStar Capital and one of Citadel's original founding partners. Clarkson says markets like this are periods of genuine uncertainty, not merely ordinary risk, which is why Litowitz's Adaptability Quotient framework is relevant.The embedded clip makes the liquidity point concrete. Litowitz says DPI is “the resolution of uncertainty” because it converts an uncertain investment into actual cash returned to LPs. In his framing, a realized dollar is a real mark, while TVPI remains uncertain until it is realized.The killer detail is the distinction between pricing risk and resolving uncertainty. Litowitz's perspective matters because QStar is a SpaceX investor and Clarkson says the conversation happened just before one of venture's most consequential IPOs. The episode's stated questions are why venture remains a way to gain exposure to innovation, how AI is changing what is investable, why liquidity is ultimately a function of time, and why uncertainty requires a different decision framework from risk.Read more: XThe Best Angel Investors in the US: Who Backs the Most Unicorns, and Who's Active NowAuthor: Ilya Strebulaev Published: July 10, 2026Ilya Strebulaev ranks angels, angel groups, accelerators, and incubators by lifetime U.S. unicorn investments, counting checks written before a company reached unicorn status. The top of the combined list is dominated by organizations: Y Combinator leads with 113 unicorn investments, followed by Plug and Play at 52 and 500 Global at 41. Sand Hill Angels is the highest-ranked angel group at 31.The killer detail is how quickly the list changes below the biggest accelerators. Strebulaev says 271 of the 304 investors in the Top 200 are individuals, or 89%. In the top 100, individuals are 91%. That makes the market underneath the large accelerator counts look much more personal: mostly operators and individual angels writing early checks from their own networks.The pull is the ranking's own caveat. Strebulaev writes that every lifetime leaderboard has a blind spot because many of the unicorns behind those totals were founded a decade or more ago, and some angels have since moved into formal funds, slowed down, or stopped investing. His post therefore separates lifetime performance from recent cohorts, including companies founded in 2015 or later and 2020 or later. For founders or allocators making current decisions, that distinction matters: a career record and a current record are not the same measure.Read more: Ilya StrebulaevAre Prediction Markets Doomed to Fail?Author: Contrary Published: July 16, 2026Contrary argues that prediction markets' current boom depends on whether platforms can prove they are more than regulated gambling with exchange-style branding. Kalshi and Polymarket have reached mass cultural, investor, and regulatory attention, but the article says the underlying idea is old: academic markets, corporate forecasting tools, Intrade, PredictIt, and other predecessors all struggled with the same linked problems of liquidity, legality, and user appeal.The killer detail is the comparison with sportsbooks. Prediction markets present themselves as peer-to-peer, transparent, and non-house-based, but sports contracts reportedly account for more than 90 percent of Kalshi trading, and the article says the platforms keep a much thinner slice of volume than sportsbooks. A market can therefore show sports-betting-scale handle while generating far less revenue.The pull is that the product's hardest problem may be distribution of wins. If a small group of sharp traders captures most profits while casual users lose interest, prediction markets may become valuable data feeds and professional tools before they become durable consumer networks.Read more: SourceRegulationExclusive: The Next Frontier of the Deportation Wars: College CampusesAuthor: Adrian Carrasquillo Published: July 11, 2026Adrian Carrasquillo reports that college campuses are becoming a new front in the fight over immigration enforcement because automatic license plate readers can turn ordinary campus security infrastructure into searchable location data. His thesis is that Flock Safety's camera network, even without direct ICE or DHS contracts, can feed deportation enforcement through local police partnerships and data-sharing practices.The killer detail is the campaign target. The Emergency Campaign to Support Higher Education, working with Schools Drop ICE, is focusing on 75 colleges and universities publicly identified as having Flock contracts. Flock says it has no ICE or DHS contracts, but activists argue the risk comes through local agencies that coordinate with federal authorities and run searches on their behalf.The pull is broader than immigration. Carrasquillo notes that license plate readers have already been abused by officers for stalking, and that Flock's AI search features can identify more than plates, including bumper stickers. A campus safety tool can become a political surveillance system when the data layer is searchable.Read more: The BulwarkThe Supreme Court Broke Independent Agencies. Here's a Way to Slow the Damage.Author: Todd Phillips Published: July 12, 2026Todd Phillips argues that the Supreme Court's decision in Trump v. Slaughter damaged independent agencies by ending for-cause removal protections, but did not leave Congress powerless. The ruling weakens the old model in which commissioners at bodies such as the FTC, NLRB, CPSC, SEC, and CFTC could be insulated from dismissal over policy disagreements. Phillips says the next fight is whether presidents can turn nominally bipartisan commissions into one-party instruments.The killer detail is the procedural fix: quorum rules. Phillips proposes that Congress require bipartisan slates of commissioners to be seated before independent agencies can act. A president

The entire technology world has, for decades, treated the IP address as a shorthand host identifier. This is clearly not the way IP was designed, but what are our other choices? In this episode of the Hedge, Scott Robohn joins Russ And Tom to discuss a recent paper arguing cryptographic keys should be the primary host identifier, and another article on the centrality of DNS to the Internet.

Should you panic when your Search Console indexing report is showing pages that aren't indexed? Is a 404 error code always a sign of a broken website? In this episode of Search Off the Record, Martin Splitt and John Mueller from the Google Search Relations team dive deep into the Page Indexing report in Google Search Console. They unpack why treating this report as a static inventory checklist to "fix" things is the wrong response, how to spot massive SEO-ruining hosting or CDN traps, and why a healthy website doesn't actually need a 100% index rate. In this episode, you'll learn: The Indexing Report Shift: Insights from the Search Console team's Hillel on why you should look for trend lines and systemic patterns rather than treating the report as a giant list of errors. When 404s are Good: Why expected 404 errors are technically correct for deleted content, and how to survive the "boss panic" of numbers that won't go down. The Domain Property Advantage: How setting up a domain property handles canonical shifts, www vs. non-www tracking, and performance data much cleaner. The Site Query vs. Search Console: Why the site: query is an artificial tool that might show old domain moves or hreflang swaps for years, making Search Console your only true source of truth. Hosting & CDN Traps: How aggressive bot protections, hidden interstitials, and "Soft 200" error pages completely destroy your crawl data and lead to malicious canonicalization. Discovered vs. Crawled: What it actually means when pages sit in "Discovered/Crawled - currently not indexed," and how to recognize holistic site quality issues over technical bugs. Key Takeaways for SEOs & Developers: Patterns over Inventories: Use the report to verify that your intentional changes (like site migrations or page removals) are processing correctly over time. Forget the Ratio: There is no magic metric for indexed vs. non-indexed pages. Even Google's own developer documentation has a massive chunk of non-indexed content due to intentional choices. Watch Out for Soft Blocks: Ensure your security layers or CDNs aren't serving "Are you a bot?" challenge screens to Googlebot with a 200 success code. Computers Fail (And That's Fine): Minor server blips, failed DNS requests, or temporary 500 errors happen. Google's systems are resilient and will just try again later.   Chapters 0:00 - Introduction: The Search Central Live coverage report confusion. 1:45 - Shifting perspectives: Treating Search Console as a pattern tracker, not a checklist. 4:36 - Tracking site migrations and processing data delays. 6:25 - Why 404 errors can be a good thing  8:00 - Handling canonical shifts and the value of Domain Properties. 11:12 - Why the site: query isn't telling you what you think it does (Domain moves and hreflang bugs). 13:38 - CDN bot protection and the absolute nightmare of soft error pages. 17:49 - How to use "marked as fixed". 20:32 - Discovered vs. Crawled Not Indexed: Is it a technical or site quality issue? 25:31 - Debunking the indexed-to-non-indexed ratio myth. 27:48 - Final verdict: How to stop fearing your Indexing Report. Resources Mentioned: Google Search Central: https://developers.google.com/search Google Search Console: https://search.google.com/search-console Search Central Live Events: https://developers.google.com/search/events    Are you actively stressing over your non-indexed page counts, or are you tracking the big trend lines? Let us know in the comments! Episode transcript →  https://goo.gle/sotr112-transcript   Listen to more Search Off the Record → https://goo.gle/sotr-yt  Subscribe to Google Search Channel → https://goo.gle/SearchCentral   Search Off the Record is a podcast series that takes you behind the scenes of Google Search with the Search Relations team.  #SOTRpodcast #SEO #GoogleSearch #SearchConsole Speakers: Martin Splitt, John Mueller

On January 1st a new EU law switched on across 27 countries — and it reports exactly how much Bitcoin you own.Europe's DAC8 turned "Know Your Customer" into what Bull Bitcoin calls "Kill Your Customer" — so the Canadian exchange just filed the first legal challenge to strike it down. We get into all of it: Tennessee becoming the 2nd US state to ban Bitcoin ATMs, Trump's disclosure showing $1.4B in crypto income while his own team writes the rules, Kraken reportedly chasing a full EU bank license — and the feel-good one, a single $150 home miner that beat billion-dollar farms to win an entire block worth ~$200,000. Then north of the border: KPMG says nearly half of Canadian manufacturers have moved or are eyeing a move to the US, Ottawa tells the UAE it has no "shovel-ready" projects for C$70B, and more.Canadian Bitcoiners PodcastWebsite: https://canadianbitcoiners.comX: @CanadianBTCPodSubscribe & turn on notifications so you never miss an episode. ————————————————————————————————SPONSORS

On January 1st a new EU law switched on across 27 countries — and it reports exactly how much Bitcoin you own.Europe's DAC8 turned "Know Your Customer" into what Bull Bitcoin calls "Kill Your Customer" — so the Canadian exchange just filed the first legal challenge to strike it down. We get into all of it: Tennessee becoming the 2nd US state to ban Bitcoin ATMs, Trump's disclosure showing $1.4B in crypto income while his own team writes the rules, Kraken reportedly chasing a full EU bank license — and the feel-good one, a single $150 home miner that beat billion-dollar farms to win an entire block worth ~$200,000. Then north of the border: KPMG says nearly half of Canadian manufacturers have moved or are eyeing a move to the US, Ottawa tells the UAE it has no "shovel-ready" projects for C$70B, and more.Canadian Bitcoiners PodcastWebsite: https://canadianbitcoiners.comX: @CanadianBTCPodSubscribe & turn on notifications so you never miss an episode. ————————————————————————————————SPONSORS

A weekly live show covering all things Freedom Tech with Max, Q and Seth.GO TO https://radar.chat/ for more information [[BILLLKEONNE]]TO DONATE TO ROMAN'S DEFENSE FUND: https://freeromanstorm.com/donateVALUE FOR VALUEThanks for listening you Ungovernable Misfits, we appreciate your continued support and hope you enjoy the shows.You can support this episode using your time, talent or treasure.TIME:- create fountain clips for the show- create a meetup- help boost the signal on social mediaTALENT:- create ungovernable misfit inspired art, animation or music- design or implement some software that can make the podcast better- use whatever talents you have to make a contribution to the show!TREASURE:- BOOST IT OR STREAM SATS on the Podcasting 2.0 apps @ https://podcastapps.com- DONATE via Monero @ https://xmrchat.com/ugmf- BUY SOME STICKERS @ https://www.ungovernablemisfits.com/shop/FOUNDATIONhttps://foundation.xyz/ungovernableFoundation builds Bitcoin-centric tools that empower you to reclaim your digital sovereignty.As a sovereign computing company, Foundation is the antithesis of today's tech conglomerates. Returning to cypherpunk principles, they build open source technology that “can't be evil”.Thank you Foundation Devices for sponsoring the show!Use code: Ungovernable for $10 off of your purchaseCAKE WALLEThttps://cakewallet.comCake Wallet is an open-source, non-custodial wallet available on Android, iOS, macOS, and Linux.Features:- Built-in Exchange: Swap easily between Bitcoin and Monero.- User-Friendly: Simple interface for all users.Monero Users:- Batch Transactions: Send multiple payments at once.- Faster Syncing: Optimized syncing via specified restore heights- Proxy Support: Enhance privacy with proxy node options.Bitcoin Users:- Coin Control: Manage your transactions effectively.- Silent Payments: Static bitcoin addresses- Batch Transactions: Streamline your payment process.Thank you Cake Wallet for sponsoring the show!MYNYMBOXhttps://mynymbox.ioYour go-to for anonymous server hosting solutions, featuring: virtual private & dedicated servers, domain registration and DNS parking. We don't require any of your personal information, and you can purchase using Bitcoin, Lightning, Monero and many other cryptos.Explore benefits such as No KYC, complete privacy & security, and human support.

In this episode, I share how I finally moved my websites off Bluehost after more than 20 years. What started as a simple side project with Cloudflare turned into a complete migration of my domains, DNS, and WordPress hosting—and it ended up being much less intimidating than I expected. Along the way, I talk about why I chose Cloudflare and KnownHost, how ChatGPT helped me work through each step with confidence, and why having an ongoing AI conversation made tackling a project like this feel manageable instead of overwhelming. If you've been putting off a website migration because it feels too risky or too technical, this episode is for you. I'll walk through what I learned, the mistakes I avoided, and why now might be the perfect time to make the move yourself. more-text-here-if-needed

More Odd DNS Records: NIMLOC https://isc.sans.edu/diary/More%20Odd%20DNS%20Records%3A%20NIMLOC/33128 From Invoice to AnyDesk: Uncovering a Phishing Campaign Targeting Russian Aerospace Organizations https://www.seqrite.com/blog/from-invoice-to-anydesk-uncovering-a-phishing-campaign-targeting-russian-aerospace-organizations/ Tenda firmware (multiple versions) contains hidden authentication backdoor https://kb.cert.org/vuls/id/213560 GitLost: GitHub AI Agent Leak https://noma.security/wp-content/uploads/GitLostWorkflow_2.gif My Upcoming Classes https://www.sans.org/profiles/dr-johannes-ullrich

77 Bitcoin kidnappings logged in France since January — and Canada isn't immune. A Toronto CEO was ransomed for $1M. Saylor sells $216M in Bitcoin. Carney admits climate defeat. CUSMA gets rejected. This week on the Canadian Bitcoiners Podcast: French Interior Minister Laurent Nuñez confirms 77 kidnappings and extortions targeting Bitcoin holders since January 2026 — already past all of 2025. Ledger co-founder David Balland had a finger severed by his kidnappers. Then it hits home: Toronto's WonderFi CEO was forced into a car during rush hour for a $1 million ransom, and a B.C. family was held hostage overnight. Joey and Len break down why being loud about your Bitcoin stack is now a physical security problem — and what actually protects you. In this episode of the Canadian Bitcoiners Podcast:- The global Bitcoin kidnapping wave - France's 77 cases and the Canadian parallel nobody's talking about- Strategy (MicroStrategy) sells $216M in Bitcoin - is Saylor's playbook cracking, or is this just treasury management?- K Wave Media's 10,000 BTC dream collapses to zero - the corporate Bitcoin treasury reckoning is here- Mark Carney concedes emissions targets will not be met, pivots to a new energy plan and a west coast pipeline- CUSMA joint review: the U.S. declines to renew, and what it means for Canadian trade- Toronto's FIFA World Cup bill: FIFA cashes out, taxpayers cover $380 million for six games- An Ontario judge exposes CBSA's 577,739-case deportation backlog through one repeat LCBO thief- Plus: a Brampton Ponzi scheme, Montreal's rental relief, BTCPay's new tap-to-pay terminal, Wasabi Wallet's mandatory upgrade, and a clipboard-hijacking cautionary tale Bitcoin doesn't create the incentive to overpromise and quietly walk it back - governments and corporate copycats do that fine on their own. Self-custody still means being your own bank. Just don't be a loud one about it. Sources: BeInCrypto, CoinDesk, CBC News, CTV News, Global News, The Epoch Times, The Globe and Mail, Global Affairs Canada, Parliamentary Budget Officer — Canadian Bitcoiners PodcastWebsite: https://canadianbitcoiners.comX: @CanadianBTCPodSubscribe & turn on notifications so you never miss an episode. ————————————————————————————————SPONSORS

RCS and DNS: The NAPTR Record https://isc.sans.edu/diary/RCS%20and%20DNS%3A%20The%20NAPTR%20Record/33124 OpenSSH 10.4 released https://seclists.org/oss-sec/2026/q3/62 Beyond Trust Advisory CVE-2026-40138 CVE-2026-40139 https://www.beyondtrust.com/trust-center/security-advisories/bt26-03 PolinRider: North Korea-Linked Supply Chain Campaign https://socket.dev/blog/polinrider-north-korea-linked-supply-chain-campaign-expands My Upcoming Classes https://www.sans.org/profiles/dr-johannes-ullrich

You learn at least five new things every episode, and this one stacks them fast. Roll your Apple Watch crown instead of tapping it to keep Sleep mode from searing your eyes, then hit Command-Shift-F in the Finder to jump straight to Recents when a saved file vanishes into the void. When a listener asks how to archive iMessages, you’ll meet iMazing, Shottr’s scrolling screenshots, privacy.apple.com, and a public-key-encryption rabbit hole that explains why even Apple can’t hand your texts back. You’ll also learn to Control-click the Printers list to reset the entire CUPS printing system, and to check every app’s Rosetta versus Apple Silicon status before sluggish performance quietly fools you, because Rosetta survives macOS 27 but finally bows out in fall 2027, so audit those Intel holdouts now. Then the questions roll in. You’ll troubleshoot a PDF that downloads on one Mac but stalls on another (think DNS blocks, CDN handoffs, stale session cookies, and a fast browser swap), wrestle Apple TV lip-sync into line by killing soundbar processing and matching frame rates, and chase a self-dimming iPad back to its ambient-light sensor and screen protector. Cool Stuff Found delivers a Beelink dock that sidesteps Apple’s storage surcharges and Pixette for reviving an ancient iPad as a photo frame. And when refurb Mac minis sell out in two hours and Apple nudges prices up overnight? Don’t Get Caught letting hope prevail like Dave did, grab it the moment you see it, because that cart won’t hold it for you. 00:00:00 Mac Geek Gab 1149 for Monday, July 6th, 2026 July 6th: National Air Traffic Control Day 00:02:47 A quick video from Dave's daughter's wedding MGG Monthly Giveaway – Win a license to Mole Quick Tips 00:00:01 Jan Landy-QT-Roll Your Apple Watch crown in Sleep Mode to go easy on your eyes 00:06:36 Claire-QT-To Find something you just added, Command-Shift-F in Finder for your Recents list 00:11:02 GW-I wish Apple had a way to archive Messages iMazing (and formerly PhoneView) Remember privacy.apple.com 00:19:09 Message in iCloud and Public Key Encryption Shottr's scrolling screenshots 00:23:33 WillRun4Fun-QT-Control-click in Printers list to Reset Printing System Enable CUPS web page on your Mac Visit https://localhost:631/printers/ If that fails, open Terminal and run “cupsctl WebInterface=yes” to enable it 00:26:14 TheDaveAbides-QT-Check your apps’ Rosetta/Apple Silicon status 00:35:28 Andrew-QT-Buy your refurbs immediately…don’t just leave in your cart Refurb Tracker RefurbMe AppTamer Sponsors 00:40:49 SPONSOR: Keeper. Right now, Keeper is offering our listeners 60% off personal and family plans at https://Keepersecurity.com/MGG. This offer is only for podcast listeners! 00:42:10 SPONSOR: Even Realities G2. Use promo code MGG at evenrealities.com to get 10% off Even Ring 1 and/or Even Clip when you add them to your Even G2 order. 00:43:48 SPONSOR: Decagon. Ready to transform your customer support? Decagon helps companies create personalized, concierge-style customer experiences with AI agents across chat, email, voice, and SMS. Go to https://decagon.ai/MGG to get a personalized demo and see what Decagon can do for your team. Your Questions Answered and Tips Shared! 00:45:16 Mike-Why can I download a pdf on ONE computer, but not another? 00:53:01 David-How do I resolve Apple TV lip sync issues? YouTube A/V sync videos AWOL 2500 Projector Sonos Soundbars 01:04:52 Larry-iPad Dims When I don't want it to Cool Stuff Found 01:12:44 Mike-CSF-Belkin Beelink Mac mini storage dock (and others like it) 01:14:24 Greg-CSF-1147-Pixette for displaying photos on an old iPad 01:16:55 MGG 1149 Outtro MGG Monthly Giveaway Bandwidth Provided by CacheFly Pilot Pete's Aviation Podcast: So There I Was (for Aviation Enthusiasts) The Debut Film Podcast – Adam's new podcast! Dave's Business Brain (for Entrepreneurs) and Gig Gab (for Working Musicians) Podcasts MGG Merch is Available! Mac Geek Gab iOS app Mac Geek Gab YouTube Page Mac Geek Gab Live Calendar This Week's MGG Premium Contributors MGG Apple Podcasts Reviews feedback@macgeekgab.com 224-888-GEEK Active MGG Sponsors and Coupon Codes List BackBeat Media Podcast Network

Show DescriptionChris & Dave have been challenged to not talk about AI this episode - do they succeed? How do I optimize tracking pixels in my web app, what's the best book on CMS and authoring experiences, picking the best tooling in Rails that won't be obsolete, SPF, DNS, and D-Marc, adding dark mode to a website, and dealing with good old fashioned F.A.R.T. Listen on WebsiteWatch on YouTubeLinks Vizio accidentally made the best dumb TV on the market | The Verge Window: queueMicrotask() method - Web APIs | MDN Jake Archibald on the web browser event loop, setTimeout, micro tasks, requestAnimationFrame, ... - YouTube Designing Content Authoring Experiences Ruby on Rails: Accelerate your agents with convention over configuration Secure AI Agent & User Authentication | Auth0 next.js TanStack | The open-source application stack for the web. RedwoodSDK: A simple framework for humans Heroku | The Cloud Application Platform For Developers Push your ideas to the web | Netlify Agentic Infrastructure - Vercel Astro Jekyll • Simple, blog-aware, static sites | Transform your plain text into static websites and blogs Nuxt: The Full-Stack Vue Framework SendGrid Email API and Email Marketing Campaigns | Twilio Email · The email API for developers who ship the rest of the message too — Bird Cloudflare: Build for the agent era CSS Analytics - Project Wallace Flash of inAccurate coloR Theme (FART) | CSS-Tricks

On this episode of DNS, Dave Crosland and Scott McNally answer listener questions covering Testosterone Isocaproate, injection frequency, cycle design, HGH, peptides, heart health, Clenbuterol, TRT, and much more. 0:00 Dave is a Ray of F'ing Sunshine 0:40 Support the show with our advertisers 2:00 Scott's frustration talking about the PED market 7:00 More Horn Talk 17:00 Heart Health Tests for Steroid Users 19:00 Testosterone Isocaproate Explained 19:40 Daily vs Weekly Injections for Long Esters 24:00 Best Testosterone, Deca & Masteron Ratio 25:40 Planning Your Second Steroid Cycle 30:00 Domestic Source Package Seized 33:00 HGH Secretagogues for Older Lifters 35:00 Best Time to Take HGH Before Bed? 37:00 Coming Off PPI Medications Safely 41:15 Peptide-Only Cycle: Does It Work? 42:50 Depression After Long-Term Steroid Use 44:45 Clenbuterol: Split Doses or All at Once? 47:10 Testosterone-Induced Folliculitis 50:00 Using Pharma Testosterone Ampules 56:45 German Autobahn vs Detroit Freeways 1:01:10 Omnadren vs Sustanon 1:05:00 Dave the Felon: Life After Prison 1:26:00 Behind the Scenes UK Blood Work Get your Labs done by Dave in the UK : https://evalbloodanalysis.com/home/ Support the Podcast Patreon — Help keep the show growing. Even $5/month makes a difference. https://www.patreon.com/thinkbigbodybuilding Sponsors TRUE NUTRITION — Custom supplements for serious lifters Use code THINK to save https://www.truenutrition.com/THINK STROM SPORTS — Performance supplements trusted by athletes UK: https://tinyurl.com/ydmbfa54 US: https://stromsportsus.com Supplement Source Canada — Top brand supplements with fast shipping http://www.supplementsource.ca Merch Official THINK BIG Merch — Train, represent, support the brand https://think-big.printify.me/products

A weekly live show covering all things Freedom Tech with Max, Q and Seth.Archipelago Foundation: https://www.archipelago-foundation.org/ Archipelago OS Demo: https://demo.archipelago-foundation.org/[[BILLLKEONNE]]TO DONATE TO ROMAN'S DEFENSE FUND: https://freeromanstorm.com/donateVALUE FOR VALUEThanks for listening you Ungovernable Misfits, we appreciate your continued support and hope you enjoy the shows.You can support this episode using your time, talent or treasure.TIME:- create fountain clips for the show- create a meetup- help boost the signal on social mediaTALENT:- create ungovernable misfit inspired art, animation or music- design or implement some software that can make the podcast better- use whatever talents you have to make a contribution to the show!TREASURE:- BOOST IT OR STREAM SATS on the Podcasting 2.0 apps @ https://podcastapps.com- DONATE via Monero @ https://xmrchat.com/ugmf- BUY SOME STICKERS @ https://www.ungovernablemisfits.com/shop/FOUNDATIONhttps://foundation.xyz/ungovernableFoundation builds Bitcoin-centric tools that empower you to reclaim your digital sovereignty.As a sovereign computing company, Foundation is the antithesis of today's tech conglomerates. Returning to cypherpunk principles, they build open source technology that “can't be evil”.Thank you Foundation Devices for sponsoring the show!Use code: Ungovernable for $10 off of your purchaseCAKE WALLEThttps://cakewallet.comCake Wallet is an open-source, non-custodial wallet available on Android, iOS, macOS, and Linux.Features:- Built-in Exchange: Swap easily between Bitcoin and Monero.- User-Friendly: Simple interface for all users.Monero Users:- Batch Transactions: Send multiple payments at once.- Faster Syncing: Optimized syncing via specified restore heights- Proxy Support: Enhance privacy with proxy node options.Bitcoin Users:- Coin Control: Manage your transactions effectively.- Silent Payments: Static bitcoin addresses- Batch Transactions: Streamline your payment process.Thank you Cake Wallet for sponsoring the show!MYNYMBOXhttps://mynymbox.ioYour go-to for anonymous server hosting solutions, featuring: virtual private & dedicated servers, domain registration and DNS parking. We don't require any of your personal information, and you can purchase using Bitcoin, Lightning, Monero and many other cryptos.Explore benefits such as No KYC, complete privacy & security, and human support.

Eric Sirion, Joscha, and Hermann join to discuss Fedimint being used in the wild in South Africa. We get into Bitcoin Ekasi's live five-of-seven federation, running guardians on Start9, how Iroh removes DNS and networking pain, backups, uptime, and why federated custody can be a powerful middle ground between self custody and custodial wallets. We discuss Lightning gateways, Conduit wallet, MoneyBadger payments, eCash privacy, zero-fee internal transactions, on-chain UTXO consolidation, and why Fedimint may become a key tool for Bitcoin communities around the world.Starting your own federation: https://fedimint.org/guardians/Setup/overviewConduit Wallet: https://joschisan.github.io/conduitFedimint on X: https://x.com/fedimintBitcoin Ekasi on X: https://x.com/BitcoinEkasiBitcoin Ekasi on Nostr: https://primal.net/bitcoinekasiEPISODE: 206BLOCK: 956087PRICE: 1714 sats per dollarmore info on the show: https://citadeldispatch.comlearn more about me: https://odell.xyzmonitor the situation: https://citadelwire.comten31: https://ten31.xyzopensats: https://opensats.org

Thank you for joining us for our 2nd Cabral HouseCall of the weekend!   I'm looking forward to sharing with you some of our community's questions that have come in over the past few weeks…   Amy: Hi Dr Cabral. Many thanks for all that you do for this community! I'd love your advice on anal fissures. I had one recently that had begun healing. My doctor gave me Diltiazem/Lidocaine cream that I used 2x day and my fissure closed and developed healing/scar tissue. However, less than 2 months later, it's opened again. His suggestions previously involved either Botox injections or surgery if the cream had not worked and I would prefer to try and address it more naturally. Do you have any suggestions about what I can do to get this fissure to heal and stay healed naturally? I'm working on diet/fiber but that doesn't seem to be enough. One bad bout of constipation and it seems to undo anything I've done to help. Thanks!       Anonymous: Hi Dr. Cabral. This is a bit of an intimate question. I'm having some sort of reaction after I have sexual intercourse. I get a prickly, itchy, warm sensation in my vagina, on my legs and stomach. Sometimes red bumps appear on my skin. Almost like I'm having an allergic reaction. Symptoms continue on and off for about 3 or 4 days then everything calms down and goes back to normal. It seems to happen more around ovulation and menstruation but recently happening anytime of the month. Any idea what is happening to me and what I can do to heal it?! To note, I'm 40 years old, still getting regular periods. Thank you for your help!       Mke: Hi Dr Cabral, would love your thoughts. I have known for a year I've had silicosis & PMF (progressive massive fibrosis) I was misdiagnosed for the last 15 years they've told me I've had sarcoidosis. My wife is an IHP, and we have been doing foundational protocols/DNS/detoxes/heavy metal detox/saunas/coffee enemas etc for the past 3 years. I have a very physical job and do keep my cardio fitness up, which seems to help my breathing. We have just invested in a home soft shell hyperbaric chamber after speaking to a guy in the US who's lung function has dramatically improved (he has silicosis but not PMF) as well as investing in a rife machine. Do you have any advice, thoughts or info on slowing down or reversing scarring or anything that might help?       Jamie: My daughter, nearly seven, has developed severe hay fever this year in the UK. She is constantly sneezing, has dark circles under her eyes, looks exhausted for almost three months, and her sleep, breathing, concentration and focus seem affected. She may also have a mild dairy intolerance, possibly linked to eczema in her inner elbows, though otherwise she is active and well. We also suspect slightly restricted sinus passages and mouth-breathing, and are considering seeing a functional-medicine-style dentist to support facial/skull development. Could you advise on practical ways to help her and whether any allergy or other tests would be worthwhile at her age to identify the root cause? Any initial guidance hugely welcome. Massive thank you to you your family and your team.      Sarah: Last year I was diagnosed with stage 4 grade 3 endometrial cancer. I underwent six rounds of chemotherapy: Paclitaxel and carboplatin along with immunotherapy; jemperli. I also had a complete hysterectomy after the third round. Prior to treatment I had regular bowel movements. Now they are loose and very dark brown. I have very limited resources currently. What do you suggest I do to heal my gut?    Thank you for tuning into this weekend's Cabral HouseCalls and be sure to check back tomorrow for our Mindset & Motivation Monday show to get your week started off right! - - - Show Notes and Resources: StephenCabral.com/3796 - - - Get a FREE Copy of Dr. Cabral's Book: The Rain Barrel Effect - - - Join the Community & Get Your Questions Answered: CabralSupportGroup.com - - - Dr. Cabral's Most Popular At-Home Lab Tests: > Complete Minerals & Metals Test (Test for mineral imbalances & heavy metal toxicity) - - - > Complete Candida, Metabolic & Vitamins Test (Test for 75 biomarkers including yeast & bacterial gut overgrowth, as well as vitamin levels) - - - > Complete Stress, Mood & Metabolism Test (Discover your complete thyroid, adrenal, hormone, vitamin D & insulin levels) - - - > Complete Food Sensitivity Test (Find out your hidden food sensitivities) - - - > Complete Omega-3 & Inflammation Test (Discover your levels of inflammation related to your omega-6 to omega-3 levels) - - - Get Your Question Answered On An Upcoming HouseCall: StephenCabral.com/askcabral - - - Would You Take 30 Seconds To Rate & Review The Cabral Concept? The best way to help me spread our mission of true natural health is to pass on the good word, and I read and appreciate every review!