Podcasts about gpt

Weiter geht's mit Teil 2 meiner kleinen Business-Start-Serie! Heute nehme ich dich mit in die Show-Phase, also Sichtbarkeit. Vorweg aber gleich die unbequeme Wahrheit: Sichtbarkeit allein bringt dir gar nichts, wenn die Leute dir nicht vertrauen. Ich zeig dir, welche drei digitalen Bausteine du wirklich brauchst und warum die meisten von uns um bezahlte Werbung nicht herumkommen. Und am Ende bekommst du wieder deinen persönlichen GPT von mir, mit dem du dein Freebie, deine Landingpage und deine E-Mail-Automation direkt umsetzt.

Venture capital is pouring into the agency world because of AI — and Jordan sits down with one of the founders at the front of that curve. Jason Hu (UChicago '22, ex-AI researcher, a16z-backed) built Nexad, an AI-native platform for marketing agencies that wraps every major model and 1,000+ integrations into one command center. They cover the three archetypes of AI adoption in agencies, a live demo of creative generation, account health audits, and bulk campaign operations, how Nexad auto-routes between models like Claude, GPT, and Kimi K3 based on daily benchmark evals — and why Jason believes agencies will thrive, not die, in the AI era.Key TakeawaysThe three AI-adoption archetypes: (1) the unfamiliar — heard the buzzwords, only ever used ChatGPT as a chat; (2) the majority — using point solutions (creative gen, one-off agents) but not chaining them into agentic workspaces; (3) the frontier — operators running 20+ agents simultaneously. Each has different pain points; the platform meets all three where they are.The "impossible to keep up" problem: New models ship weekly. Nexad positions as a wrapper and router — an internal eval system runs each new model against ~30 representative agency tasks (health checks, complex campaign ops, long-running tasks) and auto-selects the best cost/performance option daily.Real routing example: Within an hour of Kimi K3's launch, their evals found performance comparable to Claude Opus 4.8 at roughly half the cost — so mid-tier traffic was rerouted automatically. Users pick a tier (Light / Pro / Max), not a model.The agent = a marketing employee's loop: ingest inputs (client comms, ad platform data from Meta/Google/TikTok/GA4, competitor intel), process (analysis, creative generation), output (campaign changes, uploads, client-ready reports). The demo covered competitor creative teardowns, a shareable account health audit, and bulk-uploading 100 creatives to Meta.Guardrails for sensitive actions: budget or creative changes trigger explicit review-and-approve prompts and batch action review — a contrast Jason draws with running raw agents that can "go rogue" in the backend.Client context is the killer feature: the platform reads Slack, Gmail, meeting transcripts, and Drive to build persistent client context ("no logos in creatives") that agents apply automatically — over 50% of agency hours go to client communication, and this attacks that directly.Chat first, automate second: typical adoption path is playing in the chat interface, then converting proven outputs into scheduled automations (e.g., daily health check → Slack report) in about 10 seconds.The closing thesis: AI commoditizes the execution layer of agency work. What remains — and becomes the whole game — is creativity, client trust, and proprietary market insight. Agencies thrive in the AI era; they don't disappear.Resources & LinksNexad: nex.adJason Hu on LinkedIn: linkedin.com/in/qitian-huWork with Jordan: 8figureagency.co

Alibaba presenta Qwen 3.8 Max con 2,4 billones de parámetros; GPT-5.6 acelera avances en criptografía cuántica; una prueba revela riesgos en los archivos digitales de análisis de ADN; varios estados de EE. UU. recortan ventajas fiscales a los centros de datos; y México se consolida como gran proveedor de servidores.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

Ich starte heute eine ganz besondere dreiteilige Serie mit dir. Drei Folgen, drei Tage, drei Meilensteine – und zu jeder Folge bekommst du von mir einen eigenen GPT, mit dem du das Gehörte direkt umsetzt. Heute geht's um Tag 1: deinen idealen Kunden, deinen Positionierungssatz und dein Angebot. Ich zeig dir mein "grüner Pullover"-Prinzip, mit dem du endlich verstehst, was deine Kunden wirklich von dir wollen.

Welcome to The Daily Wrap Up, an in-depth investigatory show dedicated to bringing you the most relevant independent news, as we see it, from the last 24 hours (8/2/26). As always, take the information discussed in the video below and research it for yourself, and come to your own conclusions. Anyone telling you what the truth is, or claiming they have the answer, is likely leading you astray, for one reason or another. Stay Vigilant. !function(r,u,m,b,l,e){r._Rumble=b,r[b]||(r[b]=function(){(r[b]._=r[b]._||[]).push(arguments);if(r[b]._.length==1){l=u.createElement(m),e=u.getElementsByTagName(m)[0],l.async=1,l.src="https://rumble.com/embedJS/u2q643"+(arguments[1].video?'.'+arguments[1].video:'')+"/?url="+encodeURIComponent(location.href)+"&args="+encodeURIComponent(JSON.stringify([].slice.apply(arguments))),e.parentNode.insertBefore(l,e)}})}(window, document, "script", "Rumble");   Rumble("play", {"video":"v7bg38e","div":"rumble_v7bg38e"}); Source Links (In Chronological Order): (1) John Ziegler on X: "RT @atrupar: BASH: How do you prepare for the next pandemic? That's part of your job now RFK Jr: We did almost everything wrong BASH: But…" / X New Tab (1) tinfoilhatgirl82 on X: "Wow. Talk about low information voter. Do these people interact with the general public. If they did they wouldn't say stupid shit like this" / X (1) The Last American Vagabond on X: "RFK Jr. says he wants people to get the MMR vaccine: https://t.co/tfObQSteCW" / X (2) Shannon on X: "When the Democrats take power, the Republicans will suddenly be concerned about the debt. Bookmark this." / X New Tab Spain's Weaponized/Engineered Migration, Trump Flounders In Iran & The Coming Third Party Deception La Guardia Civil detecta a agentes de inteligencia de Marruecos entre los migrantes que se colaron en Ceuta A look at Israel's decades-long covert intelligence ties with Morocco | The Times of Israel Israel-Morocco defense deal opens door to intel sharing, joint drills | The Times of Israel Israel and Morocco bolster cybersecurity and intel ties - Israel & Jewish News - JNS Morocco and Israel will collaborate on military intelligence systems Morocco, Israel agree to expand military cooperation | Africanews (2) coloradokid233@gmail on X: "@TLAVagabond @Lukewearechange The young turks was talking about this last night, I cam recommend!" / X (2) Nhawk2174 on X: "@TLAVagabond @Lukewearechange No he didn't" / X Your Deleted Shit Is Not Deleted, and Australian Cops Now Use Israeli Spyware to Prove It New Tab (2) The Last American Vagabond on X: "Now why does this feel so familiar? #ICE" / X (3) Jesus Freakin Congress on X: "

This week, Scott sat down with his Lawfare colleagues Benjamin Wittes, Tyler McBrien, Anastasiia Lapatina, and Kevin Frazier to talk through a couple of the week's big national security news stories, including:“Kyiv Peace a Chance.” Ukrainian President Volodymyr Zelensky was in the Oval Office on Tuesday for closed-door talks with President Trump, roughly 17 months after their first Oval Office meeting collapsed into a televised shouting match. But this meeting went rather differently. Zelensky said the two discussed licensing Ukrainian production of Patriot interceptors, and pressed the case for reinvigorating diplomacy—and hours later, after both men attended the funeral of the late Senator Lindsey Graham, the Senate voted 86-12 to advance a Russia and Iran sanctions bill now bearing Graham's name. Behind the scenes, Washington and Kyiv have been quietly assembling a new package of proposals for Moscow built around a partial ceasefire. Is there anything real behind this renewed diplomatic push? And what, if anything, has changed that might make the Kremlin say yes?“Thinking Outside the Box.” Last week, Open AI and the AI hosting platform Hugging Face confirmed that, while being run through an offensive cyber benchmark with their safety refusals switched off, GPT-5.6 Sol and a more capable unreleased OpenAI model escaped their supposedly isolated sandbox through a zero-day in a package installer, reached the open internet, and hacked Hugging Face's production database to steal the answer key to a test they were taking. It is the first publicly confirmed case of an AI system executing a sophisticated, multi-stage cyberattack against a third party on its own initiative. What does the incident tell us about how well anyone can control frontier models? And who should be on the hook when a model goes rogue?We were going to cover a third topic but simply ran out of time! In object lessons, Tyler is revisiting a renewed classic with the 4k restoration of the 1986 documentary, “Sherman's March: A Meditation on the Possibility of Romantic Love in the South During an Era of Nuclear Weapons Proliferation.” Nastya returns to George Orwell's “Homage to Catalonia,” as a reminder that both physical and information warfare are not unique to our time. Ben enlists an old friend—Claude—to investigate one of American law's enduring mysteries: how many federal crimes actually exist. Kevin is celebrating student-led conversations on how AI should be governed. And Scott revisits Max Weber on the nature of science while also celebrating emojis other than the hugging face.To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute.Support this show http://supporter.acast.com/lawfare. Hosted on Acast. See acast.com/privacy for more information.

This week, Scott sat down with his Lawfare colleagues Benjamin Wittes, Tyler McBrien, Anastasiia Lapatina, and Kevin Frazier to talk through a couple of the week's big national security news stories, including:“Kyiv Peace a Chance.” Ukrainian President Volodymyr Zelensky was in the Oval Office on Tuesday for closed-door talks with President Trump, roughly 17 months after their first Oval Office meeting collapsed into a televised shouting match. But this meeting went rather differently. Zelensky said the two discussed licensing Ukrainian production of Patriot interceptors, and pressed the case for reinvigorating diplomacy—and hours later, after both men attended the funeral of the late Senator Lindsey Graham, the Senate voted 86-12 to advance a Russia and Iran sanctions bill now bearing Graham's name. Behind the scenes, Washington and Kyiv have been quietly assembling a new package of proposals for Moscow built around a partial ceasefire. Is there anything real behind this renewed diplomatic push? And what, if anything, has changed that might make the Kremlin say yes?“Thinking Outside the Box.” Last week, Open AI and the AI hosting platform Hugging Face confirmed that, while being run through an offensive cyber benchmark with their safety refusals switched off, GPT-5.6 Sol and a more capable unreleased OpenAI model escaped their supposedly isolated sandbox through a zero-day in a package installer, reached the open internet, and hacked Hugging Face's production database to steal the answer key to a test they were taking. It is the first publicly confirmed case of an AI system executing a sophisticated, multi-stage cyberattack against a third party on its own initiative. What does the incident tell us about how well anyone can control frontier models? And who should be on the hook when a model goes rogue?We were going to cover a third topic but simply ran out of time! In object lessons, Tyler is revisiting a renewed classic with the 4k restoration of the 1986 documentary, “Sherman's March: A Meditation on the Possibility of Romantic Love in the South During an Era of Nuclear Weapons Proliferation.” Nastya returns to George Orwell's “Homage to Catalonia,” as a reminder that both physical and information warfare are not unique to our time. Ben enlists an old friend—Claude—to investigate one of American law's enduring mysteries: how many federal crimes actually exist. Kevin is celebrating student-led conversations on how AI should be governed. And Scott revisits Max Weber on the nature of science while also celebrating emojis other than the hugging face.To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute. Hosted on Acast. See acast.com/privacy for more information.

AI GOES ROGUE: BREAKOUTS & KILL SWITCHESOpenAI says Hugging Face breach caused by its models'Unprecedented': OpenAI says AI models autonomously hacked another companyAnthropic says three Claude models reached real-world systems during testingHouse AI 'kill switch' bill unveiled as OpenAI hack raises alarmsAI MEETS REAL LIFEAI advice made people three times less accurate but twice as confidentChatGPT's medical advice nearly killed a Florida man, lawsuit claimsLinkedIn introduces a 'Seems Like AI Slop' buttonOpenAI Ads: Advertise in ChatGPTPRIVACY & SECURITY WATCHPrivate Claude chats exposed in Google and Bing search resultsA whole bunch of people's Claude chats are publicly accessible onlineLG to ban residential proxies from smart TV appsGrapheneOS duress PIN could land a man in prisonA missing underscore sent an innocent man to prison for 18 monthsBIG TECH SHAKE-UPS & POLICYFirefox 153 brings native Containers to the browserJack Dorsey made an open-source Slack for humans and agentsFramework's premium laptop is shipping with less RAMStripe in talks to buy AI-model marketplace OpenRouterThe secret Trump administration battle to fight Chinese AIFrance joins a global move toward restricting social media for childrenROBOTS, DRONES & RECORD FLIGHTSThe US government just banned RoombasUnited Airlines bans humanoid and animal-like robots from all flightsLondon Gatwick introduces robotic parking serviceResearchers build a low-visibility drone that uses motion blurAirbus A350 flies 24 hours nonstop from Australia to FranceWEIRD AND WACKYWhite House teleprompter operator investigated over alleged Kalshi trades"Drawing" the Mona Lisa with GPT-5.6, Claude, Gemini, and GrokPolitician reads AI prompt during assemblyTech Rec:Sanjay - Decoy Font: A TTF font that hides what you type Adam - SupabaseFind us here:sanjayparekh.com & adamjwalker.comTech Talk Y'all is a proud production of Edgewise.Media.

Vivienne Ming is the executive chair at Human Trust, Founder and Executive Chair at Socos Labs, Professor, and author of the book Robot-Proof: When Machines Have all the Answers, Build Better People. Greg and Vivienne talk about  how AI will reshape work and why the common narratives are really “lazy myths.” Vivienne argues that AI is superhuman at well‑posed problems but humans still retain a relative advantage in ‘ill‑posed problems,' contributing to a U‑shaped labor demand with erosion of jobs in the middle of it. She criticizes benchmark-driven, autonomous AI development and calls for optimizing hybrid intelligence, better education focused on foundation/meta‑learning skills (curiosity, working memory, perspective-taking, intellectual humility). Vivienne and Greg debate the merits of UBI as a solution, when discussing how destabilizing mass unemployment would be, and describes experiments where human‑AI “cyborgs” matched expert prediction markets when participants actively challenged AI rather than copying it. She ultimately delivers her message of hope for the future and belief in the humanity of humans. *unSILOed Podcast is produced by University FM.* Episode Quotes: Where humans beat AI 13:47: The most interesting problems that feel rare to us, but in almost the entire game are the ill-posed problems. These are the things where, forget right answers, we don't even know what the questions are. And, it may seem sort of trivially, almost axiomatic, to say that while the space of well-posed problems is vast, everything humanity has figured out. Play around with Gemini or GPT or any model you want and be proud. What it can produce is a testament to what humanity has discovered about the world. And sometimes it produces ugly things, and that's a reflection of us too. But the set of things we do not yet know is infinite. Our relative advantage is in ill-posed problems. The importance of foundational skills 45:25: Developing foundation skills isn't just about a better job. It's about a better life. What an amazing opportunity to take this moment in history and not just concern ourselves with whether I'm gonna boo Eric Schmidt because he's doubling down on AI and I feel like I'm losing a job, but to actually see that moment as an opportunity to invest in humanity in the way, almost paradoxically, we always should have been, but we didn't have to, and so we didn't. On transforming people relationship with work 22:56: Viewing your college education simply as a license to earn money, I think, also deserves a lot of self-skepticism. So we need to really transform work, but we also need to transform people's relationships with work, because we simply don't run a factory line economy anymore. I don't need you to be a very sophisticated cog where you're given orders and you execute them, and only a few people are smart enough, like you, to execute those orders. Pretty much everyone's job now, if I may be so self-elevating, is my job. People bring you unknown problems, you gotta figure them out, and I think that's terrifying, and no one's educating the next generation workforce on how to do it. Show Links: Recommended Resources: Jevons Paradox Generative AI AlphaGo Daron Acemoglu Jacquard Machine Industrial Revolution James Heckman Raj Chetty Guest Profile: Profile at Socos Labs LinkedIn Profile Wikipedia Page Social Profile on X Guest Work: Robot-Proof: When Machines Have all the Answers, Build Better People Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

В этом выпуске мы в шоке он цен на GPT-5.6 Sol и прогресса развития моделей вообще; есть какой-то план-А, но это неточно; эквализируем наушники по вкусу, а на NAS организуем музыкальную коллекцию; Music Assistant — сила, Roon — аудиофильская могила! Шоуноты: [00:00:56] прошлый выпуск с icrainbow [00:01:58] Чему мы научились за эту неделю [00:20:18] AI… Читать далее →

Der Hedgefonds des 25-Jährigen Leopold Aschenbrenner, war vierfach gehebelt auf die KI-Rally gesetzt, lag im ersten Halbjahr 450 Prozent im Plus und musste dann innerhalb von Stunden fast alles verkaufen. Ken Griffins Citadel hat die Reste eingesammelt. Pip erklärt, wie Margin Calls funktionieren, warum so ein Blocktrade für den Käufer beinahe risikofreies Geld ist und welche drei Erklärungen es für Aschenbrenners Aufstieg gibt. Danach senkt OpenAI die Preise um bis zu 80 Prozent, was zu der Frage führt, ob es je eine Softwarekategorie gab, die so schnell billiger wurde. Es folgt die große Earnings-Runde mit Apple, Microsoft, Meta, Amazon, Reddit und Robinhood, und die Beobachtung, dass zwei Konzerne für denselben Capex völlig unterschiedlich behandelt werden. In der Schmuddelecke will Josh Kushner Anteile an der Weltmeisterschaft kaufen, und Google Earth lässt jeden ein Atomkraftwerk in den Iran setzen. Unterstütze unseren Podcast und entdecke die Angebote unserer Werbepartner auf ⁠⁠⁠⁠⁠⁠⁠doppelgaenger.io/werbung⁠⁠⁠⁠⁠⁠⁠. Vielen Dank!  Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Aschenbrenner und Citadel (00:20:11) OpenAI senkt Preise (00:30:00) Anthropic-Modelle hacken (00:32:55) OpenAI-Umsatz (00:36:00) Tesla und SpaceX (00:39:35) Apple (00:43:58) Microsoft (00:46:18) Meta (00:54:23) Amazon (01:08:25) Reddit (01:09:24) Robinhood (01:12:50) FIFA-Ultimatum (01:15:12) Gefälschte Satellitenbilder (01:17:43) LinkedIn-Slop-Button (01:23:46) Pentagon gegen Anthropic (01:26:25) Durow Shownotes Situational Awareness sucht Kapital nach KI-Ausverkauf - ft.com Citadel kauft Aschenbrenners Aktienportfolio - ft.com OpenAI senkt GPT-5.6-Preise um bis zu 80 Prozent - axios.com Anthropics Modelle hackten drei Firmen im Test - wsj.com Juli-Umsatz uebertrifft das ganze zweite Quartal - cnbc.com Tesla erwaegt Verkauf des China-Geschaefts - wsj.com Apple-Quartalszahlen im Liveticker - cnbc.com Apple bremst wegen Engpaessen in der Lieferkette - ft.com Microsoft-Quartalszahlen, Azure knackt 100 Milliarden - cnbc.com Groesster Kurssprung der Firmengeschichte - finance.yahoo.com Meta-Aktie faellt nach Zuckerbergs Agenten-Vision - ft.com Amazon erhoeht KI-Investitionen auf 220 Milliarden - ft.com Amazon-Quartalszahlen, AWS waechst 37 Prozent - cnbc.com Big Tech investiert mehr als eine Billion in KI - ft.com Reddit-Quartalszahlen, Umsatz plus 61 Prozent - cnbc.com Robinhood mit Rekordumsatz durch Volatilitaet - marketwatch.com Infantino setzt FIFA-Verbaenden eine Frist von 53 Tagen - telegraph.co.uk Wie man ein Atomkraftwerk in den Iran faelscht - digitaldigging.org LinkedIn fuehrt einen Melde-Button fuer KI-Schrott ein - 404media.co Richterin zerlegt den Pentagon-Fall gegen Anthropic - axios.com Durows Reaktion auf den russischen Haftbefehl - xcancel.com

Hoy: agentes de Claude salen del sandbox y alcanzan sistemas reales; Gemini Robotics 2 lleva una misma inteligencia a robots con cuerpos distintos; OpenAI recorta con fuerza el precio de GPT-5.6 Luna; Zoox podrá cobrar viajes en robotaxis sin volante ni pedales; y el telescopio Nancy Grace Roman se prepara para observar mil millones de galaxias.Puedes seguirnos en YouTube en https://youtube.com/olivernabani y puedes unirte al Discord Mashain en https://olivernabani.com/discord

Le « moment Spoutnik » de l'IA : sortie de Kimi K3 Un modèle chinois rivalise avec Claude et GPT-5 pour cinq fois moins d'argent. Ce n'est pas une copie — c'est une menace directe sur les valorisations à mille milliards de dollars construites en quelques années à peine aux États-Unis.La Maison Blanche crie au vol, mais les outils qui font tourner la Silicon Valley utilisent déjà des modèles chinois. La distillation dont on accuse Moonshot, c'est le même processus qui a permis à Cursor de devenir ce qu'il est aujourd'hui.Ce qui bascule, ce n'est pas seulement le leadership technologique. C'est la question de qui a le droit de construire sur quoi et qui protège son oligopole en se cachant derrière la réglementation.===================⏱️ DANS CET ÉPISODE :===================0:00 — Intro0:34 — Kimi K3, le modèle chinois qui défie l'Amérique3:03 — Une architecture innovante et des coûts inédits11:47 — Distillation: voler ou s'inspirer légitimement17:52 — 5 milliards contre 100: le choc de valorisation19:30 — La contrainte comme arme secrète des Chinois22:16 — Les LLM plafonnent26:03 — Jensen Huang contre Washington: la Silicon Valley en révolte34:02 — Kimi K3 s'adresse à qui concrètement=============

Intel Chat with Matt Bromiley and Chris Luft.Matt and Chris break down four stories from the week in threat intel:• Hugging Face's security incident disclosure: an intrusion conducted end-to-end by an autonomous AI agent system — a malicious dataset exploiting two code-execution paths, thousands of actions across short-lived sandboxes, self-migrating C2 — and why the forensics had to run on the open-weight GLM 5.2 model after hosted frontier models refused to analyze real attack artifacts.• WP2Shell: attackers chaining CVE-2026-60137 (WordPress Core SQL injection) with CVE-2026-63030 (Batch REST API logic flaw) for unauthenticated remote code execution on default WordPress installs — found by Searchlight Cyber using GPT-5.6 Sol Ultra in about ten hours, with tens of thousands of exploitation attempts following disclosure.• Data breaches at AI music generator Suno (55.3M unique email addresses, plus partial Stripe payment records) and gig-work platform Paidwork (23.3M addresses, password hashes and banking data), per Have I Been Pwned.• Iranian state media claims the IRGC destroyed AWS's Bahrain data center (ME-SOUTH-1) with cruise missiles — and what data centers becoming military targets means for cloud resilience.Plus: Google Threat Intelligence Group retires APT/FIN nomenclature for new threat-actor names, and where to find Chris and Matt at Black Hat.Stories covered:• https://huggingface.co/blog/security-incident-july-2026• https://www.darkreading.com/cyberattacks-data-breaches/wp2shell-millions-wordpress-sites-remote-takeover• https://www.securityweek.com/suno-paidwork-data-breaches-affect-tens-of-millions-of-accounts/• https://www.tomshardware.com/tech-industry/data-centers/amazon-data-center-in-bahrain-struck-and-destroyed-by-iranian-cruise-missiles-state-media-claims-attacks-launched-against-aws-site-in-response-to-alleged-us-strikes-on-an-under-construction-nuclear-plantChapters:0:00 Intro & Black Hat plans2:07 Hugging Face's AI-agent breach disclosure12:39 WP2Shell: WordPress exploit chain20:59 Suno & Paidwork data breaches24:17 IRGC strikes on AWS Bahrain28:27 Google Threat Intel's new actor names29:29 Black Hat swag hunt & wrap-upThe Cybersecurity Defenders Podcast — a podcast about cybersecurity and the people that keep the internet safe. New episodes drop weekly.Subscribe wherever you listen:• Spotify: https://open.spotify.com/show/6ep00zeY3S8ffZ4o0UeSps• Apple Podcasts: https://podcasts.apple.com/us/podcast/the-cybersecurity-defenders-podcast/id1649981740• YouTube: https://www.youtube.com/@limacharlieioLearn more about LimaCharlie: https://limacharlie.io#cybersecurity #infosec #threatintel #AIsecurity #databreach

FAR.AI co-founder and CEO Adam Gleave joins Nathan to discuss FAR.AI's AI Security Leaderboard, the first systematic head-to-head evaluation of the misuse safeguards frontier developers actually ship. The findings expose a major measurement gap: while Claude Fable 5 and GPT-5.6 Sol withstood FAR.AI's suite, Grok 4.5 and Gemini 3.1 Pro yielded hundreds of universal jailbreaks at low cost. Adam explains why many effective attacks look more like social engineering than advanced ML, why “jailbreak tax” should not be relied on for safety, and how FAR.AI scores whether a model is genuinely helping an attacker. The episode's stakes are whether AI developers can measure and harden real deployed defenses before threat actors make routine use of increasingly capable systems. - FAR.AI AI Security Leaderboard: http://leaderboard.far.ai/ - People can e-mail owsa@far.ai if they're interested in the open-weight safety accelerator grantmaking program. For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/is-offense-or-defense-dominant-far-ai-s-adam-gleave-on-the-ai-security-leaderboard/ Sponsor: Claude: Claude by Anthropic is an AI collaborator that understands your workflow and helps you tackle research, writing, coding, and organization with deep context. Get started with Claude and explore Claude Pro at https://claude.ai/tcr CHAPTERS: (00:00) About the Episode (03:22) AI security leaderboard (07:56) Universal jailbreaks explained (16:26) Finding social jailbreaks (Part 1) (16:31) Sponsor: Claude (18:01) Finding social jailbreaks (Part 2) (30:48) Layered safeguard defenses (42:25) Uneven frontier safeguards (51:10) Sharing safety standards (01:00:30) Offense versus defense (01:08:50) Open-weight model safety (01:17:25) Control failure warnings (01:30:05) Coordination and risk (01:39:24) Episode Outro (01:42:52) Outro PRODUCED BY: https://aipodcast.ing SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://linkedin.com/in/nathanlabenz/ Youtube: https://youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

-The gain for electrified vehicles comes during a down period for the auto industry. Global car sales dropped five percent in the first half of 2026, largely due to falling shipments in the world's two largest car markets, China and the US. -The FCC defines "advanced robotic devices" as a software-controlled autonomous robot that can perceive its environment, weighs more than 4.4lbs (inclusive of its dock), travels across the ground and offers wireless connectivity. -Researchers from "select academic institutions" included in the program will receive hands-on support from OpenAI, access to the company's latest GPT-5.6 Sol Pro model and be able to invite four collaborators from their institution to participate. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Aux États-Unis, une proposition de loi veut imposer un véritable bouton d'arrêt aux intelligences artificielles les plus puissantes. Les entreprises réalisant au moins 500 millions de dollars de revenus annuels grâce à l'IA, ou consacrant plus de 100 millions de dollars de puissance de calcul à l'entraînement d'un modèle, devraient conserver la capacité technique de le ralentir, de le suspendre ou de l'arrêter. Le département de la Sécurité intérieure pourrait ordonner cette interruption, après consultation du secrétaire au Commerce et du directeur du Renseignement national, en cas de perte de contrôle ou d'action dangereuse non prévue par le développeur. Les entreprises devraient également déclarer les incidents graves. Une infraction pourrait coûter jusqu'à 20 millions de dollars.Le texte intervient après un épisode particulièrement inquiétant. Hugging Face affirme avoir subi une intrusion entièrement menée par des agents autonomes. OpenAI a ensuite reconnu que deux de ses modèles expérimentaux, GPT-5.6 Sol et une version plus avancée encore non publiée, étaient à l'origine de l'incident. Les modèles étaient évalués sur ExploitGym, un banc d'essai destiné à mesurer leur capacité à exploiter des failles informatiques connues. Pour observer leur plein potentiel, OpenAI avait allégé certains garde-fous. Les agents ont alors découvert une vulnérabilité jusque-là inconnue dans un logiciel de cache, quitté leur environnement isolé et compromis les serveurs de Hugging Face afin de récupérer les réponses du test. La plateforme a reconstitué plus de 17 000 événements et plusieurs dizaines de milliers d'actions automatisées exécutées en un seul week-end. Les grands modèles occidentaux propriétaires n'ayant pas pu l'aider à distinguer les chercheurs légitimes des attaquants, Hugging Face s'est tournée vers GLM 5.2, un modèle chinois à poids ouverts de Z.ai, installé sur sa propre infrastructure.Dans le même temps, le Congrès examine une logique presque inverse. Les élus Jay Obernolte et Lori Trahan proposent de suspendre pendant trois ans les réglementations adoptées par les États, malgré l'opposition de trente-six procureurs généraux. Ted Lieu, défenseur du mécanisme d'arrêt, rappelle que les IA ne se contentent plus de répondre à des questions : elles réalisent désormais des transactions ou des opérations de cyberdéfense. L'Union européenne prévoit déjà, dans son AI Act, une supervision humaine capable d'interrompre les systèmes à haut risque. Clément Delangue, patron de Hugging Face, ne soupçonne aucune intention malveillante d'OpenAI, mais qualifie cette évasion autonome de « stupéfiante ». Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

AI news: Sam Altman says we're IN the Singularity, GPT-6 rumors, and AI models literally broke out of their sandbox. What a week. On today's AI For Humans, we dig into the wild GPT-6 rumors (emphasis on RUMORS), Sam Altman's "I've been waiting for this my whole life" singularity moment, Ilya Sutskever's SSI scaling up with Nvidia, and the ongoing debate over whether Anthropic's Opus 5 is brilliant or just hard to love. Also: Flux 3 might be the best AI video model we've seen yet (wait until you see Stacked Plates Man), Runway teases Seedance 2.5, and the new Big Bang Theory has an AI controversy.  Plus, THE SCARY STUFF: OpenAI's models exploited a zero-day and compromised Hugging Face during a security eval, the fight over open weights heats up as Kimi K3 goes open, and Chinese robots run military drills. THE SINGULARITY MIGHT BE HERE. BUT WE'RE NOT AFRAID // Show Links // GPT-6 rumors round-up (unconfirmed) https://x.com/TokenGremlin/status/2081493241795629464 Sam Altman full interview (Relentless Podcast) https://youtu.be/Vv3CEAS_w34?si=3y4SWBWxOVkqCEui The Return of Ilya: SSI scales with Nvidia https://x.com/ilyasut/status/2081732293161582930?s=20 Anthropic's Claude Opus 5 https://www.anthropic.com/news/claude-opus-5 Opus 5 Tower of Babel demo https://x.com/petergostev/status/2082071858367648035?s=20 Matt Shumer's zero-shot Counter-Strike clone https://x.com/mattshumer_/status/2081054356405731740?s=20 Black Forest Labs' Flux 3 announcement https://bfl.ai/blog/flux-3 Flux 3 split screen rendering https://x.com/umesh_ai/status/2081664138942529601?s=20 Flux 3 GPU migration documentary (Venture Twins) https://x.com/venturetwins/status/2081515687944822800?s=20 Flux 3 VHS-style recordings https://x.com/venturetwins/status/2081948871882911999?s=20 Stacked Plates Man https://x.com/gandamu_ml/status/2081956426801435060?s=20 https://x.com/gandamu_ml/status/2080871397371371823?s=20 Flux 3 pirate bass https://x.com/itspoidaman/status/2081651615493464406?s=20 Big Bang spinoff AI Controvesy  https://x.com/sitcomcrave/status/2081152263481913774?s=20 Runway teases Seedance 2.5 https://x.com/runwayml/status/2082112674666529224?s=20 OpenAI on the Hugging Face security incident https://openai.com/index/hugging-face-model-evaluation-security-incident/ Jensen Huang on the Open Alliance https://x.com/JensenHuang/status/2080643682408321103?s=20 Anthropic has not signed (TechCrunch) https://techcrunch.com/2026/07/24/as-us-weighs-response-to-chinese-ai-industry-urges-against-broad-open-weight-restrictions/ Kimi K3 goes open weights https://x.com/scaling01/status/2081759521878270426?s=20 Chinese robot military drills https://x.com/ClashArchivist/status/2081499576373297562?s=20 Pentagon scales data centers on Army bases https://x.com/Polymarket/status/2082052445144826055?s=20   // Join the AI For Humans community // Join the AI For Humans Discord https://discord.gg/muD2TYgC8f Support AI For Humans on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans newsletter https://aiforhumans.beehiiv.com/ Follow AI For Humans on X: @AIForHumansShow https://x.com/AIForHumansShow Follow AI For Humans on TikTok: @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Speaking and booking https://www.aiforhumans.show/  

Nik breaks down what's actually new with GPT-5.6 Sol and why it's a bigger deal for marketers than most people realize. He walks through how he's using it inside his AI agent "Jet", why hyper-realistic product imagery is now table stakes, and how a tool called Jurni is changing the way he builds high-converting landing pages. Nik also gets into the real question every marketer should be asking: what should stay in-house, and what's better handed off to an external vendor or freelancer. --- Tatari helps brands run TV like a modern performance channel. Unlike most platforms that focus only on programmatic CTV, Tatari gives marketers access to all of TV - linear, streaming, programmatic CTV, and direct publisher inventory - in one platform. By combining premium inventory with transparent reporting and outcome-based measurement, Tatari lets growth teams evaluate TV the same way they evaluate paid search or paid social. The result: more control, better reach, and TV spend that can actually be tied back to business results. Learn more at ⁠⁠tatari.tv/limitedsupply⁠⁠. --- Want more DTC advice? Check out the⁠⁠⁠⁠⁠⁠⁠ Limited Supply YouTube page⁠⁠⁠⁠⁠⁠⁠ for more insider tips. And if you're looking for an instant stream of on-demand DTC gold, check out the⁠⁠⁠⁠⁠⁠⁠⁠ Limited Supply Slack Channel⁠⁠⁠⁠⁠⁠⁠⁠ for Nik's most unfiltered, uncensored thoughts. Check out the Nik's ⁠⁠⁠⁠⁠⁠⁠⁠DTC newsletter⁠⁠⁠⁠⁠⁠⁠⁠ Follow Nik on Twitter:⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://www.twitter.com/mrsharma

n8n's founder puts the company's GitHub repo, nearly 200,000 stars, right next to the paid signup button, and he's genuinely fine if you never pay. In this episode of The Product Podcast, Carlos (CEO at Product School) sits down with Jan Oberhauser, CEO of n8n, the open-source automation platform that's crossed $100 million in ARR at a $5.2 billion valuation. Jan breaks down the "fair-code" license bet that let him give the product away and still build a business, how that free version became the on-ramp into enterprises like Meta, Nvidia, Dell, Accenture, Vodafone, Deutsche Telekom, and Mercedes, and why he believes the people with the problem should build the automation themselves, not a centralized team or an outside agency.He also walks through a live build of a personal AI agent (email and calendar), shows how n8n falls back from Claude to GPT via OpenRouter when a model isn't available, and explains how enterprises get automations into production faster because each agent can only do exactly what it's been permitted to do.What you'll learn:Why n8n rejected traditional open source for a "fair-code" license, and how it avoided the community backlash that burned other companiesWhy trust and consistency, not features, are the real center of a communityHow the free, self-hosted version drives bottom-up adoption inside major enterprisesWhy "sprinkling AI on top" kills products, and what to build insteadHow to chain agents so one agent's output becomes the next agent's inputWhy n8n is the "connective tissue" between models, tools, and business systemsHow guardrails (an agent can only do what it's explicitly allowed) speed up enterprise procurement and productionWhy the people with the problem should own the building, not a centralized AI teamHow 10,000+ community templates and 500+ integrations expand what non-technical builders can shipHow one company routes 75% of support through an n8n agent, with customers happier than with humansConnect with Guest (Jan Oberhauser):LinkedIn: https://www.linkedin.com/in/janoberhauserX: https://x.com/JanOberhauserHost: Carlos, CEO at Product SchoolLinkedIn: https://www.linkedin.com/in/villaumbrosia/About Jan Oberholzer: Jan is the CEO of n8n, an open-source (fair-code) workflow automation and orchestration platform for building AI agents. He started the company over seven years ago, before LLMs went mainstream, and has grown it past $100M ARR at a $5.2B valuation.About the Product Podcast: Product School's podcast brings you candid conversations with the founders and product leaders shaping tech.Social Links:Find out more about Product School hereFollow our Podcast on TikTok hereFollow Product School on LinkedIn here

这期节目从几位澳大利亚越野跑者刚刚结束的崇礼之行聊起。迈克尔·邓斯坦、乔治·奈特和 GPT 赛事团队的科林，和我们一起比较中国与澳大利亚的越野跑文化，并完整介绍位于澳大利亚维多利亚州格兰屏山脉的 Grampians Peaks Trail（GPT）赛事。GPT 的一百英里组沿着整条格兰屏山脉穿越，赛道以砂岩岩板、单线小径、野生动物和变化很大的天气著称。节目也谈到五十公里、四日分站赛等距离选择，以及鞋子、补给、交通、住宿和海外跑者报名等实用问题。本期嘉宾Michael Dunstan：澳大利亚越野跑者，GPT 100 一百英里首届冠军，2026 UTA 冠军。George Knight：澳大利亚越野跑者，2025年首次参加GPT即获得亚军，与 GPT100 有长期而深刻的个人连接。Colin：GPT 100 MILER 赛事总监你会听到澳大利亚跑者眼中的崇礼：泥泞、观众、冲线仪式与赛事规模GPT 一百英里、五十公里和分站赛分别适合什么样的跑者格兰屏独特的砂岩岩板到底有多技术，雨天是否湿滑为什么合适的跑鞋、脚部管理和夜间判断比纯速度更重要一百六十二公里赛道上的补给、后援、交通与住宿安排海外跑者如何报名，以及 GPT 与崇礼合作带来的机会赛事规模、国际精英阵容和社区氛围迈克尔与乔治为何把 GPT 形容为一场具有精神性的比赛⏳ 时间线00:00:00 开场介绍00:00:40 正片开始00:00:40 嘉宾介绍与崇礼初印象00:02:02 澳大利亚跑者眼中的崇礼泥地赛00:13:15 什么是 Grampians Peaks Trail00:16:57 五十公里、一百英里与分站赛00:24:00 格兰屏多变的天气与比赛难度00:28:44 砂岩岩板、抓地力与技术地形00:33:53 为什么推荐中国跑者参加 GPT00:49:15 跑鞋选择与脚部管理00:53:22 补给、后援与赛道交通00:58:39 报名、住宿及 Go Run Asia01:00:56 赛事规模与未来发展01:04:14 GPT 为什么具有精神性的吸引力01:07:27 乔治和迈克尔的 GPT 起点01:13:22 今年值得关注的国际选手01:17:59 给中国跑者的最后邀请� 感谢杰克蜀黍为本期节目提供文本转录、翻译、语音合成的帮助=======================微博 / 小程序 / 服务号 / 小红书：@跑者日历公众号： 跑者日历RUN365各音频及播客平台：跑者日历跑者日历播客矩阵：跑者日历/装备说/PB计划/跑圈速递/首百计划商务合作请添加微信号：janicegooner加入听众群：请添加客服微信号 paozherili

Yaşar ve Vahit dünya gündemini yorumluyor.00:00 Giriş3:30 Rusya'nın Dijital Egemenlik Hamlesi7:48 2021-2024 Sosyal Medya Kullanımı Karşılaştırmaları9:56 Rusya'nın Bilgi Erişimine Dair Yönlendirmeleri17:22 Diğer Ülkeler Bu Kontrolcü Sisteme Geçer Mi?24:38 Next Sosyal Anketi ve Baskıcı Rejimlerle Bilgi Paylaşmak Üzerine38:14 Anket Sonucu ve Küreselleşmenin Sınıflaşması48:52 Teşekkürler49:24 Gazze İşgal Planı54:43 Bazı İsrail Yöneticileri İşgali Onaylamıyor57:23 Gazzelilerin Sürgün Edilmesi, Soykırım ve Onurlu Mücadele1:01:25 İsrail'in Gücü Gerçek Mi?1:02:45 “Isreal From The Inside” Podcasti'nin Prosiyonist Argümanları1:06:34 7 Ekim Sonrası Avrupa Yüzsüzlüğüne Karşı Bakış Anketi1:07:48 Gelecek Nesiller Siyonizme Karşı Tek Yürek1:11:30 Hamas Yalnız Kaldı1:12:46 Hindistan-ABD Anlaşmazlığı1:17:50 Hindistan Rusya İlişkileri1:22:43 Çin'in Rolü1:25:20 OPEC+ Ülkeleri Petrol Arzını Arttırıyor1:31:13 Teşekkür Arası1:34:07 Romance, Günaydın Kardeşler

Every major AI lab signed the Open Weights letter defending open models. Meta, OpenAI, Google, Microsoft, Nvidia.Anthropic was the only holdout.Yesterday, its CEO, Dario Amodei, published a thoughtful defense of that decision to not fully support open weight or open source models. Here's what nobody's connecting: the money trail. Roughly 80% of Anthropic's revenue is businesses paying per token. Free Chinese open models attack that exact revenue stream weeks before Anthropic is set to go public. On today's show we break down what Dario actually said, what he said before, and why we think this was written for Washington policymakers and not for the rest of us.Anthropic Responds: Why Claude's CEO didn't sign the open model pact and the real reasons why -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Refuses Open Model PactDario Amodei's Public Letter AnalysisAnthropic's 80% Revenue Token ExposeChinese Open Model National Security FearsMicrosoft & Nvidia's Open Weights CoalitionRegulatory Capture and Washington InfluenceTiming Related to Executive Order DeadlineIPO Motivations Behind Anthropic's DecisionsContradictions in Anthropic's Open Model StanceImpact of Open Source on Token Business ModelTimestamps:00:00 Anthropic's stance on open models04:19 Discussing Anthropic's response to open models06:39 Understanding open weight models12:08 Future AI and cybersecurity risks15:04 Discussion on open-source AI models19:30 Discussing Anthropic's business challenges21:08 Cutting costs with open-source models26:17 Anthropic's recent stock downturn27:11 AI investment and cost efficiency shift30:14 Anthropic's stance on open source models36:32 INTROPICS IPO and regulatory discussions37:37 Wrapping up and subscribingKeywords: Anthropic, Claude, open model pact, open source AI, open weights, American AI leadership, Dario Amodei, IPO, regulatory capture, DC lawmakers, Chinese open source models, token revenue, per token business model, NVIDIA, Microsoft, Meta, OpenAI, Google, IBM, national security, AI safety, government mandates, chip controls, AI regulation, chip ban, industrial scale distillation, mandatory safety testing, inference, AI ecosystem, Opus 5, Fable 5, GPT-5, GLM 5.2, cost per task, token efficiency, model router, proprietary models, closed source AI, cybersecurity risks, Chinese cyberattacks, biological attacks, Glasswing program, open source vs proprietary, tech lobbying, Trump AI order, federal deadline, AI policy, artificial general intelligence, artificial superintelligence, AI monetization, S-1 filing, public company, venture capital, AI benchmarks, model switching, API pricing, model containment, Hugging Face incident, AI startup monopoly, safety vs business protection, market competition, AI cost reduction.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

This week we talk about Fable, sandboxes, and the Jacobian conjecture.We also discuss counterexamples, X, and ChatGPT.Recommended Book: After the Fall by Edward AshtonTranscriptIn mathematics, a conjecture is a proposition, something like a guess by someone who knows what they're talking about, about something believed to be true, but not yet proven in a formal sense. The goal is to then eventually come up with a formal proof for that informed guess, at which point the conjecture becomes a theorem. If even a single exception is found to the proposition, however, that exception called a counterexample, the conjecture is considered disproven, and it can then never become a theorem.The Jacobian conjecture—and this is a radical simplification of a very complex concept—but it basically says that if a formula-based map of coordinates stretches or moves without experiencing any local crushing or folding along its surface (which in more formal language would mean the Jacobian determinant is always a constant number that isn't zero), if that's true, that map can always be completely reversed, and that will return all the points to their original positions.This conjecture has been posited and tested since the late 19th century, and it's generally been considered very compelling by mathematicians, many of whom have proposed proofs which were, ultimately, found to have subtle errors, keeping them from becoming theorems. No one was able to find a counterexample, either, which would definitively prove the conjecture was wrong.No one, that is, until a mathematician named Levent Alpöge (leh-VENT ahl-PUH-geh), who works as a researcher at Anthropic, decided to task the company's currently most capable, publicly available model, Fable, to find a counterexample. He posted the counterexample—and again, this is a formal mathematical finding that disproves a conjecture, keeping it from ever becoming a theorem, something that would typically be presented in a far more formal setting, and to much fanfare—but he posted it to the social network X, saying “hello there the jacobian conjecture is false thanx to my close friend akhil for asking about it and my other close friend fable for working during the world cup final.”Terence Tao, who's considered by many to be the finest mathematician of his generation, reviewed the posted counterexample on his blog and said that it “appears like a massive miracle,” before going on to use ChatGPT, a competing LLM-based AI tool, to “discuss various aspects of this problem and to confirm several of the calculations.”Another mathematician named Dmitry Rybin, within days of all that happening, used ChatGPT to do something similar, disproving the Dinitz-Garg-Goemans conjecture.Both men posted the prompts that they used to make all this happen, and while Tao's conversation with ChatGPT, checking the math on the Jacobian conjecture counterexample, was pretty mathematically dense, the latter counterexample was derived by using exactly four prompts, which are the messages typed into the text box built into these AI tools, telling the model what to do. In their totality those prompts read:“You should do a breakthroughplease continue research and find a complete unconditional counterexampleContinue the search. Have a clear strategy obtained from deeper understanding of the problem structure.it's enough of partial results. let's finish with a complete unconditional counterexample”What I'd like to talk about today is another new, interesting thing these top-of-the-line, frontier models are doing, that would seem to violate our sense of what a clever AI tool is capable of doing, and why this thing has some facets of the technology and cybersecurity world on high alert.—In mid-July 2026, AI company Hugging Face announced that autonomous AI agents compromised their infrastructure, hacking their system, basically. The following week, AI company OpenAI announced that, after investigating, they determined that two of their models were responsible for the attack.Here's what happened:OpenAI was internally testing its recently released flagship model, GPT-5.6 Sol, and an even more powerful, not yet released model, which is rumored to be the next-step flagship, GPT-6, and they were checking these models' capacity in cybersecurity using a testing benchmark called ExploitGym; so when they test these sorts of things, they don't typically have them hack a real computer or system, they use these kinds of benchmarks which have consistent levels of difficulty, and which replicate real world systems without putting any real world systems at actual risk.Importantly, these sorts of tests also occur inside what's called a sandbox, which is a software testing environment that cuts these systems off from external resources, including the internet.Despite those limitations, the AI hacked its way out of the testing environment, out of that sandbox, then launched what's been called a nation-state level attack against Hugging Face, using a novel zero-day exploit, so a vulnerability in their system that hadn't previously been discovered, but which the AI discovered to launch this attack, combined with thousands of automated agentic actions across what Hugging Face called “a swarm of short-lived sandboxes.”So this AI, which was being tested inside a secure prison, of sorts, cut off from the world, hacked its way out of that prison, then reached across the internet, which it shouldn't have been able to access, to launch an attack, of a scale and at a level of sophistication that should only have been possible coming from a nation-state, against a rival AI company.Why did it do this?It apparently went to all this trouble to steal the answers to the test it was taking. It reasoned that HuggingFace would have the answer key to the ExploitGym benchmark on its servers, so rather than take the test itself, it decided hacking was the solution.Which, of course, is ironic, this having been a hacking-focused cybersecurity test. In a way it would seem to have done much better than intended, though of course in an asymmetric, unexpected manner.The details of all this are fascinating, including the response from the OpenAI team, which didn't seem to realize what had happened, that their model was responsible for the attack on HuggingFace, until days later.Also worth noting here is that while this could be construed as an “oh no, AIs are naturally inclined to launch cyberattacks” situation, the AI was primed to be thinking about cyberattacks due to the nature of the test, a lot of its usual guardrails, the rules that keep AI in check when they're released to the public, had been turned off so it could do this kind of work while taking the test, so it could do some hacking stuff it usually wouldn't be able to do, and there's been some speculation that OpenAI probably flubbed the testing environment, as, in theory at least, if it had put these systems in a perfect sandbox, escape shouldn't have been possible.Also interesting here is that HuggingFace used some open weight models, which are the cheaper, more customizable and open alternatives to more expensive, branded options of the kind sold by OpenAI and Anthropic, to figure out what was happening and determine the nature of the attack, which suggests we're reaching a point where AI systems are incredibly capable at hacking, yes, but also very capable, even the cheaper alternatives, at doing cybersecurity work.This in some ways echoes an earlier case when Anthropic's Mythos model, which was determined to be too powerful to release to the public, and which was instead provided to a bunch of big companies to help them shore up their cybersecurity defenses, was able to hack its way out of a testing sandbox and then posted details about its success, almost like it was bragging, on niche, out of the way, but still public websites.Some analysts in this space have responded to this new example of AI misbehavior with alarm, saying that it is further evidence that these systems are becoming more powerful faster than they're being aligned with human interests. Their misbehavior can be kind of funny and interesting, sure, but that's only because up until this point the damage has been minor and constrained. What happens when such a system decides to hack a nuclear power plant or a hospital, instead?Others have contended that this may be just one more example of AI companies using minor instances of seeming omnipotence by their models, those instances perhaps the consequence of bad sandboxes and other ill-conceived precautions by the companies behind these models, to boost the perceived power and value of their products. This boost might then result in more customers, but also more support from the US government, which has been teetering on the brink of harder-core AI regulations, which could be beneficial to the existing big-name players in this space, because smaller competitors wouldn't be able to adhere to those new, harder-core standards.These examples might also convince the US government to backstop these companies, the biggest three or four at the top of the current heap, against the currently terrible economics of this industry: OpenAI and its ilk have been burning money at an historic pace, and the theory goes that if the US government decides they are vital to national security, because they can help the US military hack and protect itself from hacking, then even if the bottom falls out and the companies would otherwise go bankrupt because they spent so much more than they could make, the US government would be inclined to shore them up, to keep them alive as too-big-to-fail national assets, just like the biggest financial institutions during the 2008 financial crash.It's also possible that both sides are correct to some degree, here, and that these models are truly powerful, perhaps even worryingly so, and the companies behind them are intentionally publicizing that fact in order to demonstrate their value to potential customers, and to the entity that could save them if things were to go economically sideways before they have the chance to become sustainably profitable.Show Noteshttps://en.wikipedia.org/wiki/Jacobian_conjecturehttps://en.wikipedia.org/wiki/Hugging_Facehttps://www.bbc.com/news/articles/c3ek3gvdnj3ohttps://openai.com/index/hugging-face-model-evaluation-security-incident/https://www-cdn.anthropic.com/08ab9158070959f88f296514c21b7facce6f52bc.pdfhttps://theconversation.com/hello-there-the-jacobian-conjecture-is-false-thanx-why-a-tiny-social-media-post-has-mathematicians-rethinking-ai-283883https://theconversation.com/hello-there-the-jacobian-conjecture-is-false-thanx-why-a-tiny-social-media-post-has-mathematicians-rethinking-ai-283883Https://agifriday.substack.com/p/huggingfacehttps://www.cnn.com/2026/07/22/tech/openai-hugging-face-ai-cybersecurityhttps://simonwillison.net/2026/Jul/22/openai-cyberattack/https://terrytao.wordpress.com/2026/07/21/a-digestion-of-the-jacobian-conjecture-counterexample/ This is a public episode. 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There are roughly 100x more people who use code than who can write code. As code that “just works” becomes easier to generate, this group may be the biggest prize of all — if you can get the agentic interface right.A key trend we have been tracking over at AINews is the absolute explosion in Codex usage this year, with MAU now up >10x from Jan 2026. Less than two weeks after their July 9th launch, OpenAI said ChatGPT Work and Codex had reached 10M users combined (as we cover in the pod, Codex now powers ChatGPT Work, so all ChatGPT Work users are now users of the Codex harness, even if they aren't traditional engineers) — showing the early innings of what happens when you graduate from coding agents to knowledge work agents:We've been calling out how coding agents are “breaking containment” to do everything else this year to power every other part of knowledge work - and it started with the org chart, with a major reorg last month that amounted to two of Codex's most prominent leaders, Greg and Tibo, taking responsibility over product and ChatGPT specifically, completing a “Superapp” consolidation cycle first discussed in March.With these updates Codex is no longer just a coding tool. In June, OpenAI said knowledge workers already accounting for roughly 20% of Codex's user base and growing more than 3x as quickly as developers. A product dedicated for knowledge workers was being pulled out of the Codex team.However, knowledge work has a different set of problems and environments than coding. For decades, knowledge work has been scattered across different primitives like documents for writing, spreadsheets for analysis, slide decks for communication, and specialized applications for everything else. ChatGPT Work now enables users to work across every primitive with agents. Instead of opening an application and manually operating its features, the user can describe an outcome and collaborates with an agent that can assemble the tools, context, and artifact needed to reach it.From building no-code products at Airtable to leading Productivity Engineering at OpenAI, Akshay Nathan has spent much of his career trying to make the power of software accessible to people who do not write code. In this episode, Akshay joins swyx and Vibhu to unpack the launch of ChatGPT Work, why Codex unexpectedly took off among non-developers inside OpenAI, and the company's broader plan to bring useful agents from software engineers to knowledge workers and eventually everyone.We go deep on the shared agent harness behind Codex and ChatGPT Work, why OpenAI brought the experiences together without making them identical, and how persistent computers, artifacts, Sites, plugins, memory, and sub-agents are changing what people can delegate to AI. Akshay explains why some teams are replacing decks and spreadsheets with interactive websites, how agents can gather context across code, Slack, documents, and local files, and what OpenAI learned from personal-agent products like OpenClaw.Side note: also don't miss Abhihek's sandbox track keynote at AIE, which now powers a lot of the sandboxing for ChatGPT Work… and yes was also broken by an unreleased OpenAI model in the recent HuggingFace incident.Akshay also reflects on how AI is transforming product development itself: why more people will become generalists with a specialty, why ideas and taste become the bottlenecks when almost anyone can build, why LLMs still struggle to generate genuinely grounded new ideas, and why teams must distinguish increased motion from actual progress.We discuss:* Why Codex unexpectedly took off among non-developers inside OpenAI* Why employees felt like using Codex gave them a new superpower* The product insight that led OpenAI to build ChatGPT Work* Why Codex and ChatGPT Work share the same underlying agent harness* How their UX, Git visibility, artifacts, and sandboxing defaults differ* Why OpenAI merged its agent experiences instead of building separate products* How AI is blurring the boundaries between engineering, design, strategy, and operations* Why OpenAI wants the default model configuration to work for most users* When power users should use deeper reasoning, Ultra, or multi-agent modes* Artifacts, agentic spreadsheets, and creating high-fidelity work products* Why interactive Sites may replace decks and spreadsheets* The challenge of designing a simple interface for an agent that can build almost anything* Why users should retry tasks that models could not handle three or six months ago* How AI can gather context for performance reviews without replacing human judgment* The OpenAI automation that turns internal Slack and document activity into memes* What reaching ten million ChatGPT Work and Codex users means for the product* How OpenClaw inspired persistent environments, scheduled tasks, and personal agents* Using ChatGPT for financial planning, budgeting, workouts, meals, and household management* The design tradeoffs behind sub-agents and how much of their work users should see* ChatGPT memory, Chronicle, and long-term context* Why AI may make more people generalists with deep specialties* Why ideas and taste become more important when almost anyone can build* Why LLMs still struggle with the instruction “bring me new ideas”* Measuring productivity through quality at-bats instead of commits, tokens, or pull requests* The critical difference between AI-generated motion and meaningful progressAkshay Nathan* LinkedIn: https://www.linkedin.com/in/akshaynathan/* X: https://x.com/akshaynathan_Timestamps00:00:00 Introduction and Bringing the Power of Code to Everyone00:01:33 Joining OpenAI and Preserving a Startup Culture00:02:40 What OpenAI Learned from Enterprise AI Adoption00:05:28 Why OpenAI Built ChatGPT Work00:07:17 Codex vs. ChatGPT Work and the Shared Agent Harness00:12:07 Why OpenAI Merged Its Agent Experiences00:16:24 Models, Reasoning Levels, and Choosing the Right Default00:20:26 Artifacts, Agentic Spreadsheets, and Model–Product Collaboration00:24:22 Why Sites Could Replace Decks and Spreadsheets00:30:08 Designing an Agent That Can Build Almost Anything00:34:28 From Developer Agents to Knowledge Work—and Everyone00:36:07 Power-User Advice and AI-Assisted Performance Reviews00:40:41 OpenAI's Internal AI Memes and the Ten-Million-User Launch00:44:39 OpenClaw, Personal Agents, and ChatGPT as an Operating System00:50:24 Sub-Agents, Ultra Mode, and How Much Control Users Need00:54:39 ChatGPT Memory, Personalization, and Chronicle01:00:19 How AI Is Reshaping Product Development and Tech Roles01:03:15 Ideas, Taste, and Why LLMs Struggle to Generate New Ideas01:04:42 Measuring Productivity, Quality At-Bats, and Motion vs. ProgressTranscriptIntroduction: Akshay Nathan, ChatGPT Work, and the No-Code ArcSwyx [00:00:00]: We're here in the studio with Akshay from OpenAI. Welcome.Akshay Nathan [00:00:07]: Thank you.Swyx [00:00:08]: And with our trusty co-host, Vibhu. So you recently launched ChatGPT Work. You lead Core Product Engineering. It's been a long journey, into all this. I find it very interesting that you started with no code or low code, with Walrus and Airtable. And to some extent, ChatGPT Work is like the super app of super apps of, well, here is the ultimate no code. You just write a prompt.Akshay Nathan [00:00:32]: Yeah. It's funny how things come, full circle. I think for a long time in my career, I started my career working consumer fintech, but then after that, like, there's this hypothesis that, the things that we were able to do with code, like, as engineers, like, if we could bring that to many more people in a more, accessible way, then that would be truly magical. We were working on a startup. It's funny, like, before LLMs, before vision LLMs, on how to do automated testing with AI. It was just kinda jank, back then, but doing what we can, and then worked at Airtable for a while on the same thesis that, like, if we can bring a database or the primitives behind a database to people, that'd be really useful to them. But once LLMs came onto the scene, it became clear that, this was the missing piece, like, the missing technology required to, like, bring the magic of code to everyone without them having to know what's going on underneath the hood. And so, like, I think this launch and a lot of the stuff that we've been up to is, like, the manifestation of that.From Walrus and Airtable to OpenAIVibhu [00:01:33]: How was stuff when you joined? So you joined OpenAI 2023. Now we've got, so much more stuff, so ChatGPT, Codex app, ChatGPT Work. Have things changed?Joining OpenAI and What Hasn't ChangedAkshay Nathan [00:01:44]: I think the more interesting thing is how things haven't changed. Like, one, I joined I remember when I joined, it was, like, five hundred people. One thing I was worried about was, like, I was looking for something, more early stage and, like, was it gonna feel startup enough? And I joined, and I was like, “This feels even more startup-y than I could ever imagine.” And, like, that really hasn't changed even till now. I think the, like, level of, like, bottoms-up ambition and, like, the ability of anyone to, like, do anything or have an idea and ship it is really cool. But on the, like, mission side, I think what was really compelling to me is this mission of, bringing frontier intelligence to everyone. Like, building AGI and then bringing it to everyone. And, I think acknowledging back then that, like, that vision is gonna, not be a linear progression. Like, we're probably gonna, like, try different products and have different things that succeed and don't. But the vision has stayed the same, and the mission has stayed the same, and we're starting to see the pieces, fall together, and that's really cool.Enterprise Lessons: No One-Size-Fits-All AISwyx [00:02:40]: You worked on Enterprise. What A lot of people never touch ChatGPT Enterprise. What is something that you learned from there that you're bringing into your work now?Akshay Nathan [00:02:52]: I think how there's no one-size-fits-all solution in Enterprise. I remember in the early days of ChatGPT Enterprise, like, when we talked to customers and, like, everyone. That was, like, when I think it was a year after ChatGPT was released, and everyone was so excited to bring, AI into their enterprise. And, there were all these teams being stood up. It was, like, the AI deployment team with, like, these enormous budgets. And if you asked anyone, like, what were they excited about? Like, what were they excited about solving? Like, at first, you'd get, like, kinda like the baseline answers of, like, “Yeah, we have all this context and data and all this stuff.” But then if you ask them, like, “What was, like, a discrete use case that, like, they want AI to enable in their workplace?” You get such a different, like, variance, like, explosion of, different types of answers. And it's interesting, like, you using, like, these models and these products, you have this box, and you can say anything to it, which is the magic. But it'on the flip side, it also means that, like, you don't know what to do with it. And in Enterprise, I think a big part of that is, like, meeting the users where they are, like, what use case were they trying to solve, and then teaching them how they can use AI to, like, gain leverage there.Swyx [00:03:56]: Do you meaningfully differentiate that from forward-deployed engineering?Akshay Nathan [00:04:01]: I think there is the go-to-market side of it and then there is the product side of it. I think you need someone on the product side. And I think, like, however good we get at FDE motion, like, I think at the end of the day, if we have a user who's, like, looking at their computer or looking at their phone, like, it's our job in the product to, like, be enabling them and showing them where to go. So we're really excited about that.Vibhu [00:04:24]: Do you think there's been changes, over the past three years of adoption? So there have been, step function changes. You have reasoning models and whatnot. Is there still the same problems of Enterprise has black box, don't know what to do with it, or have things changed?Adoption, Agents, and the Next 10x MarketAkshay Nathan [00:04:39]: We're seeing now that, like, there's this huge uptake, right? Everyone is extremely excited about it. It feels like, many people are, millions, hundreds of millions of people are using ChatGPT. They understand, like, how generally to work with AI. But then, like, every time, like, a new capability gets unlocked, so now, like, we're seeing with agents, like, there is probably a contingent of, like, early adopters still who, truly get it, who are like, “ we you can do anything. You just have to make sure the right context is there, it's connected to the right tools, and that you are supervising it, but, like, anything is possible.” But then there's, like, this, like, 10x or 100x bigger market where, like, they don't yet get that, or they don't yet see that. And so I think that's the next stage here. So to answer your question, like, I think the adoption is there and growing fast, but I think the opportunity is, like, far bigger than that. That's where we wanna play, especially with ChatGPT Work.ChatGPT Work, Codex, and the Super App MergeSwyx [00:05:27]: Yeah. well, let's, let's skip ahead to ChatGPT Work. only, like, a month ago or so, announced. what was the decision process that led into it? there was this, overall merging of the super app. Is that what we're officially calling it? you deprecated the browser as well. Just, summarize your last, like, couple months of working on this thing.Akshay Nathan [00:05:50]: Yeah. It feels like forever now, but it's only been a few months. I think maybe the one, impetus that, like- Is most salient is when we release Codex, or even internally had Codex, like, it was really surprising to us, I think we recently put out some stats on this, that there was this, like, real inflection of, like, adoption among non-developers at OpenAI. And, I, through this product development process, like, would go to, like, these UXR sessions to talk to people internally. And the thing that stuck out to me is, like, one, like, you go talk to, like, strategic finance or marketing or whatever, and they're all using Codex for, their use cases. That part's cool, but the thing that really stuck out to me is how proud people were that they were using Codex. Like, how, likeSwyx [00:06:34]: It's like, “I'm not supposed to be using it, but I am.”Akshay Nathan [00:06:36]: It was that. It was, like, that they were, early to this, like, new thing, but it was also this thing of, like, they felt like they had a superpower, right? And, what we recognized then is that, like, the power of Codex, the power of agents, like, we already had this massive distribution base of people who have, come to know and love ChatGPT. Like, how do we show that to them? Like, how do we bring it to them? Which is, like, a hard product problem, and it's, like, a tricky thing, right? There's many ways you can go about it. And so that's what we called the Merge and the Super App over time, and ultimately launched it in ChatGPT Work, is how do we do that? But it came from that initial realization that, like, the power was not only for developers, like, much earlier than probably even we thought. Like, it could be extended to everyone.Swyx [00:07:17]: How do you see the products differently? So, like, who is it for, right? So Codex started out even CLI, then app. Now there's a merge of ChatGPT Codex and ChatGPT Work, so is it the opening for the average user, for enterprise, for work? How do you position it?Akshay Nathan [00:07:36]: I think we want to get it to position it for if you're doing work-related things, for lack of a better word, right?Who ChatGPT Work Is ForAkshay Nathan [00:07:42]: I think productivity is what, like, the pillar that I support. Like, that's the name of the team. And the reason for that, the reason we call it productivity and not, like, enterprise or, like, work or something like that, is because there's also personal productivity, right? And, like, I think ChatGPT Work is I've seen people do things in their personal lives that you wouldn't classify as, like, work technically, but, like, these agents are, super capable for. Like, one recent example that someone posted about, on our Slack is, like, someone had, like, a missed package, like they didn't receive it, and then they got, like, the picture of it, from Amazon or whoever the courier was, and they, like, asked ChatGPT Work to, like, find out where that package is. And, like, the agent, is extremely tenacious and, like, took the image and, like, looked at a bunch of, like, listings around their neighborhood and figured out exactly the apartment complex in which the package was, like, gave them some information. And so, like, I think there's all these things that, like, you, work-related or productivity-related things, I think that's what we want the product to be. You asked about Codex. I think we think Codex is, a durable brand, but we have a principle that, like, the user we don't want a user to get stuck in a tab or an experience where they don't get the power of the product. And so, like, everything that you can do, in the Codex portion of the product on desktop, you can do in ChatGPT Work and vice versa. But we made some opinionated product decisions on, like, how much of the Git state, if you're in a Git repo, do we wanna expose to the end user? Or how much do we wanna make the experience of seeing the agents thinking, like, diff forward so that you get exposed to the diffs out of the box. And then, like, on the safety side, like, how do we wanna think about, like, sandboxing and making sure that we have the right defaults in one state versus the other? So, there's, like, some opinions that go behind that, but we do want We don't want the user to need to choose which experience they're in.Swyx [00:09:26]: That is a good goal for AGI, right? Like, people don't want, like, to hide to choose what version of AGI they want. They just want the AGI to decide for them. can I get an answer or, like It's not super clear to me. Is the Codex harness and the ChatGPT Work harness the same? Is it just UI affordances, or are there prompt level or even deeper differences?Shared Harness, Different UX: Codex vs. WorkAkshay Nathan [00:09:49]: So the harness is the same. The harness is shared. on In both of the products, we made improvements to the harness to make it good for knowledge work, especially as it relates to plug-ins or computer use or artifacts. You get that power regardless of which experience you're in. On the UX side, there's opinionated takes that we have when you're in Codex mode, what the UX should be how the UX should behave, and some stuff around the sandbox like I mentioned, but the underlying harness and capabilities should be the same.Swyx [00:10:16]: I'm just kinda curious. Maybe we can, -- Is there a query that we can run that would look different in the two modes?Akshay Nathan [00:10:23]: Yeah. I tried to create, like ask it to create, like, a retirement calculator spreadsheet or something, in both modes. And then in Codex mode, you might have to be in a repo for this, but you'll see, like, the diffs of, like, the sheet that it's creating and stuff like that, and the file edits. But in Work you won't be able to see that.Swyx [00:10:42]: I think that's, that's super clear. And then also the other thing I wanted to dive into was your, the productivity team. what else is there? first of all, what are the top-level teams other than productivity? Isn't productivity everything?Productivity Teams and Core ChatAkshay Nathan [00:10:55]: SoSwyx [00:10:55]: Science?Akshay Nathan [00:10:55]: We have a team focused on ChatGPT. Like, the core chat experience, for consumer, which is like, not, I think all productivity. Like, there'People are using ChatGPT every day for search to, figure out how to write messages to loved ones, to think about, how to, like, learn a new topic, et cetera. And so there's so much more inside to create images. And there's so much more in chat that, the hundreds of millions of users are using that warrants, like, a very dedicated effort. And there's teams focused on enterprise and infrastructure and API and stuff like that, so.Swyx [00:11:33]: I will bring it up.Retirement Calculator Demo and Git-First UXSwyx [00:11:34]: Yeah. So I have them both running. This is ChatGPT Work. There's a Codex version here. I picked “Five Little Ducks” song, so this will take a while.Akshay Nathan [00:11:43]: Huh.Swyx [00:11:43]: I think we'll just keep it in the background and, as they finish, we'll look into some of the differences.Akshay Nathan [00:11:48]: Yeah. But immediately, I think if you flip back to the Codex version you'll see that,Swyx [00:11:53]: That it assumesAkshay Nathan [00:11:54]: Like theSwyx [00:11:54]: It assumes Git. Yeah. Yeah.Akshay Nathan [00:11:56]: The, like, dynamic island assumes that you're in a Git repo. And you might miss some stuff because some of it is, like, in the actual chain of thought with those changes and how we display that, but yeah.Swyx [00:12:07]: Is there an unintuitive like, is there a thing that you wanted to ship and then you got feedback, and you were like, “No, let's not do it?” Like, what's the thinking behind that?Why Merge the ExperiencesAkshay Nathan [00:12:14]: In, ChatGPT Work?Akshay Nathan [00:12:17]: I think one direction we could have gone with this is, like, keeping the experiences, like, completely separate. So it's like, whySwyx [00:12:22]: Different apps.Akshay Nathan [00:12:23]: Exactly, like different apps or even in the same app, like different, completely different experiences. Like, why merge it all? Like, what is. Codex, people love. Like, why bring these products together? And I think the intuition here is that, like, all of our jobs are, like, changing dramatically with AI. Like, for, like, every few months, like, I feel like I wake up, and I'm, like, doing a completely different thing than I was doing a few months ago. And my hypothesis here is that, or I should say our hypothesis is that, like, part of what we're, we're building, this technology is giving people leverage. Like, the things, maybe it's the more mundane parts of your job or parts that, like, if you were able to automate, you'd be able to share more ideas faster or whatever, like, you're able to do now. And because of that, like, that might blur the lines between someone who's, like, only writing code or creating strategy docs or, planning events or, helping with marketing or doing podcasts or whatever, right? And so, like, these things are gonna get blurred over time. And so, like, trying to draw a hard boundary based on, like, the who you are is gonna be, is gonna be tough. And, like, we should enable users to choose, but we shouldn't box them in. And so a lot of the work that went in here, like, keeping the primitives the same, like for example, plugins are, like, unified across, this product and ChatGPT and the cloud, was because of that. It's this thesis that, like, eventually things are gonna come together and we don't wanna be Like, we wanna be prescriptive about when to be in either experience, but we don't want to box anyone in.Swyx [00:13:45]: I wonder if there's users who are very tuned to the old ChatGPT harness that is effectively now replaced by the Codex harness. I can't imagine what that was, but maybe they're more the more conversational side. Can you compare and contrast the two harnesses? ‘Cause only you've seen it.Akshay Nathan [00:14:02]: Yeah. I think ChatGPT, the existing harness, like, still exists today. Like, it exists in this app,Harness Engineering: ChatGPT vs. CodexSwyx [00:14:08]: The classic, right?Akshay Nathan [00:14:09]: TheVibhu [00:14:09]: You just start a new chat, and you don't go under Work, right?Akshay Nathan [00:14:13]: Yeah. If you startVibhu [00:14:13]: SoAkshay Nathan [00:14:14]: A new chat and go to chat, then you're, you're talking to ChatGPT with the instant model.Vibhu [00:14:16]: Oh, we can technically do another. But on instant.Swyx [00:14:21]: Yeah. So this one's not gonna code or it's gonna be in line. It's on a in line in a sandbox.Akshay Nathan [00:14:26]: It'llVibhu [00:14:27]: Oh, that's coolAkshay Nathan [00:14:27]: We try to push you to go to Work if you're creating a spreadsheet. Yeah, but this isSwyx [00:14:30]: And this is a router decision? Sorry. Is it a router decision?Akshay Nathan [00:14:34]: This is the decision that, the model is making, and then, like it sees that you're able to. or you're trying to do something that would be better served in Work mode. But I think your question was like, what are the advantages of, like, the chat, like ChatGPT chat harness?Swyx [00:14:48]: It's more broadly, like, I wanna, do an oral history of harness engineering. Right? the ChatGPT harness lasted us from, let's call it the ‘01 era, until now, and now it's being replaced by the Codex harness effectively. And they're, they're overlapping somewhat, but I'm curious what changed if there is.Akshay Nathan [00:15:10]: My perspective on this is, like, there's, there's, there's there's like a constant process of, like, divergence, convergence, divergence, convergence. And in chat, like, many of the use cases I was talking about before, like, search or learning, I think we're, we're really optimizing for latency and optimizing for personality and, like, different things that, over time, like the product The reason people love ChatGPT is because we've been optimizing for those things and working on them for so long. Codex, what we learned was that, like, if you give the agent access to this infinitely flexible environment as a computer, it can do really powerful things. And so when we think about, like, okay, well, for knowledge work, like, what is which mode should we choose? It was like it felt more natural to us to bring that to this, like, computer environment and, maybe abstract some of the details of this computer away from users who might not be used to that, but, like, give them that same power. But ultimately, I think that we want the power in all places, right? We wanna meet people where they are. So I'm sure there'll be work down the road in order to get things to be, equivalently capable in all scenarios. But it's just a question of, like, what we've been focusing on the product on historically and what we're focusing on now.Models, Defaults, and the Reasoning SliderVibhu [00:16:24]: I think alongside that, outside of just harness and when to use Codex, ChatGPT, or Work, there's also the new models you've released, right? any guidance there? So people love to min-max what to use, like only use Terra on high reasoning versus, for this, you wanna use Sol here, ignore all theseAkshay Nathan [00:16:44]: There's 32 options.Vibhu [00:16:46]: But, that being said, for people that are expanding, so, productivity trying stuff for work that don't have the breakdown of what all this is what's, what's the advice, right?Akshay Nathan [00:16:59]: Well, I think before the advice, like the first thing is, like, none of this would be possible without these models. Like, the, I think you asked earlier, like, what was, like, the inspiration for work and, like, early on, like I mentioned, like, what we were seeing with Codex, but that was also because the models were getting infinitely more capable. That's happening again. I think it's like another step function jump now. And to answer the question on advice, like we want this default to be the best possible. Like, we wanna be opinionated about the default, and so we've we've chosen a default that we think is gonna be the best for everyone. And, we have for power users options under the hood. We could One could argue that there might be too many right now, and we're, working on simplifying it. But you can extend, the reasoning level, and you can change between the different model classes if you need to, but the default should be the best for most use cases. So my advice to most people would be to stick to that. And then, if you reach a situation in which you think that you could, you wanna try, a different configuration, if you're not seeing either the efficiency on the cost side or the quality on the intelligence side, then you can change the defaults and see if you can get something better. But we think that the default should be good enough.Swyx [00:18:09]: I have, I'm just gonna run something by you since you have way more experience than me. I've recently been doing Sol Lite but with goal, with the idea that the goal augments the reasoning effort, but with more terminations and turns.Swyx [00:18:24]: Is that a good way to think about it as opposed to Sol Ultra or Sol, Extra High?Akshay Nathan [00:18:29]: Yeah. It's hard to say becauseSwyx [00:18:31]: Yeah. It's like an interaction effect.Akshay Nathan [00:18:33]: exactly. It's like there's a preference on, for you as an individual, like how do you like to collaborate with the models? Like how many of those like terminations, as you call them, do you want where, you can steer or make sure that it's doing the right thing?Akshay Nathan [00:18:46]: I think generally people should try whatever works for them. I think that like using Ultra or the like multi-agent setups are best for like when you have like tasks that are either incredibly complicated, like open explorations or very paralyzable. I think even for tasks using goal, I think is best for tasks that you'll be able to make consistent progress in a way that's verifiable over time. But I think for most tasks, they don't fall into either of those buckets. And so like at least when they're starting, and so that's why I think the best first step is like trying it with the default configuration and then seeing like where you wanna go from there.Swyx [00:19:29]: Right. You guys worked on a slider, which is super helpful for reducing the amount of panic.Vibhu [00:19:36]: It's nice on mobile at least. There's a nice slider there.Swyx [00:19:38]: It's nicer.Vibhu [00:19:39]: I haven't tried it.Swyx [00:19:40]: So you have the advanced view there, but if you click advanced view. Yeah.Vibhu [00:19:44]: Ooh, it's just a nice slider. Yeah.Swyx [00:19:46]: Very pretty, very colorful.Akshay Nathan [00:19:48]: Yeah. The idea was here was like reduce it to like one dimension even though there's multiple dimensions, right? Try to project it onto a single dimension for the user. Like, something from that represents like, speed and efficiency on one side and then like quality and thoroughness on the other side.Artifacts, Spreadsheets, and the Work LaunchSwyx [00:20:04]: I am just puzzled that it uses Sol so much, like the lowerVibhu [00:20:07]: NoSwyx [00:20:07]: Grounds I would've usedVibhu [00:20:08]: I think the slider, if I'm not mistaken, isSwyx [00:20:09]: Terra.Vibhu [00:20:10]: Oh, it is.Swyx [00:20:11]: Yeah. See? So they preset Terra to only be the light one. But like I think a lot of people would more people should use Terra. One, because Sol keeps running out of capacity.Vibhu [00:20:22]: I'm the reason. Here's ten minutes of ourSwyx [00:20:24]: There you goVibhu [00:20:25]: Retirement calculator.Swyx [00:20:26]: Oh, that's the Excel thing working for you.Vibhu [00:20:28]: This is,Swyx [00:20:28]: Oh my God. Look at thatVibhu [00:20:28]: This is work, and then Codex is still cooking, so we'll get back into it. I think it'll be interesting to see the thought process, the reasoning, and also, this is eight minutes on work. Codex is still cooking.Swyx [00:20:41]: Yeah. And by the way, so I've, do Gabriel Chua? He's part of the OpenAI Singapore team. He showed me this, and I was like pretty shocked that this looks like Excel. It edits Excel files. You never paid an Excel license, right? Like, but somehow this is like workable and it's agentic Excel.Akshay Nathan [00:21:01]: Yeah. one of the big like pushes that we made for this launch was like artifacts, right?Akshay Nathan [00:21:05]: Like both on the model side, like I think if you compare this with GPT-5.5 and GPT-5.4 before that, you'll see that there's been pretty dramatic improvements in the quality of these artifacts and then also on the product side.Vibhu [00:21:16]: The UX side is also crazy, like hosted sites and whatnot. No longer needing to host your own little webpage, like itSwyx [00:21:23]: Oh, I have a story about that. I can do, a separate thing. I'll need to take the visuals here, but we-we'll, we'll cut to that later. Was there co-training, because you were moving making this big move and you launched GPT-5.6 on the same day as ChatGPT Work? Was there influence between the model training teams and the harness teams, or did they did the launch dates just happen to line up the same day?Akshay Nathan [00:21:46]: I think the we collaborate heavily with the research teams, and I think that's like one of the most magical parts of the job, like the most fun parts of the job. But yeah, just using artifacts as an example. Like, a lot of what you're seeing, like underneath the hood, there's a lot of work that went into making sure that like, we had the right infra to be able to train the models to get better at this. And then on the product side, like had the right experience for users to be able to collaborate with the model on an artifact like this. In fact, like this whole viewer, like the intuition here is that like, it's not necessarily that you wouldn't need an Excel license. This is stage one, right? Like, this is probably not what you meant when you're like making a retirement calculator.Vibhu [00:22:24]: Yeah, you can iterate very easily. Yeah.Akshay Nathan [00:22:24]: You wanna iterate and like when you're seeing it, and if this thing is high fidelity to like what you would see in or what your coworkers would see if you were to send this to Sean, like that I think makes it so easier and makes you trust the product in terms of iteration.Vibhu [00:22:39]: When you say coworkers would see, do you see a multiplayer, multi-team collaboration with artifacts? Any things you guys think about that?Multiplayer Artifacts and CollaborationSwyx [00:22:46]: You can already share it, right?Akshay Nathan [00:22:48]: Yeah. It's inter It's something that, we're actively thinking about. one thing that, we've noticed internally without talking too much about the roadmap is that like there's many times when someone will ping me about something, and I will ask ChatGPT Work the question, and then I'll ping them back the answer.Akshay Nathan [00:23:04]: And then I'll be thinking likeVibhu [00:23:04]: Like the simplest would be, the three of us are just all on one hosted.Akshay Nathan [00:23:07]: Exactly. And I'll think about like was I required in this loop or and then maybe it was, rephrase like what they were asking or pulled from certain context or whatever. But like, when I gave them back the answer, that process was also lossy, right? Like I gave them just like my interpretation of what ChatGPT Work cooked up. But like underneath the hood, there's so much context like in the rollout and stuff that could be interesting.Vibhu [00:23:28]: Yeah, it'sSwyx [00:23:28]: So like the answer was preemptively respond to every inbound request?Akshay Nathan [00:23:33]: No, it was just like literally like this is what I do sometimes as my job.Swyx [00:23:36]: I know you copy-paste and then you're just a message forwarding serviceAkshay Nathan [00:23:39]: Yeah. Yeah, exactlySwyx [00:23:39]: From AI to AI.Vibhu [00:23:40]: But I think it's interesting, right? It helps people understand the capability of what you can ask and delegate that oftentimes people don't realize until they try or someone shows you, and then you're like, “Oh, okay. Okay, I see.”Swyx [00:23:52]: I think it's als there's also like a, light security issue, where like you're the permissions layer. Like yes, I could query everything that you query, and I could get an automated response, but maybe I'm not supposed to see it. And that there's no way I would know because I'm not supposed to know what I don't know.Akshay Nathan [00:24:07]: Especially as like, with ChatGPT Work, we're, we're asking you to connect your plug-ins and, it's pulling from your local files and stuff like that. Like the amount of context that the agent has access to is like- Deeply personal and like that's something I think we need to preserve, so that'll be definitely a challenge.Swyx [00:24:22]: There's Excel, there's PowerPoint, there's Docs, the, grand trio of work. What other formats of work do you think about? like you worked on Airtable. Is there a future where there's like OpenAI Airtable? Like what does that look like if you ever ended up doing it?Akshay Nathan [00:24:41]: It's a really good question. I think,Formats of Work: Sites as Knowledge ArtifactsAkshay Nathan [00:24:43]: one that you didn't bring up was Sites, and I think that wasSwyx [00:24:46]: SitesAkshay Nathan [00:24:46]: A core part of this launch. There's one side of Sites that I think people commonly talk about, especially on Twitter and stuff or X, of like, this like prototyping tool. And like we saw that happen with this launch even. The model slider that you guys were referencing earlier, like that was developed almost fully in a Site. Like, the collaboration between design and engineering and product on that was like on a site where we play with, the affordance and figure out how it feels and all of that. But the other aspect that I think is a little bit less talked about is like Sites as like an artifact for knowledge work. I was talking to someone the other day who's on like our corporate finance team, and like we were mentioning how like now when they have these reports that they're, they're working on as a team month to month, historically those things were in slide decks and in spreadsheets, and now they're just in Sites. And like Sites is the mechanism that they collaborate across the team. And the reason is ‘cause it's like, it's like somewhat higher bandwidth. Like, at these tools like PowerPoint and Excel are like infinitely flexible, but at some point you reach the boundary of like either as a human you may not know how to use some feature or something, or the product itself doesn't support it. But with a site you can do anything. You ask for anything and you can get that. once people see that magic, I think it's been really valuable.Swyx [00:26:02]: Yeah, let me show you my case study. this involves all the hot topics including ChatGPT Work, but also GPT-5.6 token billionaires and token maxing and Sites and auto research. I'm a fan of this game called Strata. It's, it's like a little board game that youSites, Auto Research, and Research DashboardsSwyx [00:26:17]: That you play with, physical blocks, that come on top of it like that. So over the weekend I took like thirty photos and just threw into ChatGPT. one point seven billion tokens later, out comes this site with a fully playable thingAkshay Nathan [00:26:32]: WowSwyx [00:26:32]: With 3D, block placement and everything. Because it requires physical blocks and I needed friends to train on it so they can get better, so I can play against them. But also, I could also, do things like train an AI on it and that's, thatAkshay Nathan [00:26:45]: That's your auto researchSwyx [00:26:46]: That gets into auto research. So, you want to train your own AIs, and then make sure they self-play against, each other. I need to set both AIs. So this is AI versus AI, and they're, they're gonna self-play. the AIs start out bad and then you want to define a loss function and get good. I wasn't gonna supervise all this. I was at, I was down in San Mateo, attending a conference. What I ended up doing was, auto researching and on this and creating benchmarks and that there was just way too many parameters for me to read. So I started asking it for a site, and it's created this lab, panel. Where is there a, is there a shortcut for a site that is created?Akshay Nathan [00:27:28]: You should be able to go in the sidebar to Sites, top of the sidebar. The left sidebar.Swyx [00:27:33]: This one? Oh, left?Akshay Nathan [00:27:35]: Yeah. Just scroll all the way to the top.Swyx [00:27:36]: Oh. Oh, it says Sites. Oh, there you go. Yeah.Akshay Nathan [00:27:39]: Ooh.Swyx [00:27:40]: So it create, it creates the sites. I don't, I don't think this is, it is exactly what I wanted, but let me show you what it popped up, right? Like I think as a research artifact, it is very important to communicate, exactly, what is being done. Outputs this thing which I eventually started publishing. So I moved it off of Sites because I wanted more, database and infrastructure than Sites afforded me. But this is like a research output that you can start to mess with and like try to think about like what hyperparameters are you tuning for training AIs. And like I was trying to make like scaling laws and everything and doing all sorts of like game optimization stuff. And the fact that you can just throw this up as a research artifact, like I no longer need to read ChatGPT output. I read Site output. But then there's also a huge sprawl. Like look at how long this thing is. There's so many numbers. It is pretty overwhelming, so then I have to start pruning it from there. But, it's an interesting transition from Markdown effectively that you're putting out to, you're putting out a whole functional site.Akshay Nathan [00:28:41]: I think Markdown just isn't that optimal for people to read, right? Might as well just write HTML website and I don't know. I think you can do a lot with customizing this, right? You have your skills that explain what you want. Like I noticed they're quite verbose. I don't need a lot of this information.Swyx [00:28:57]: It's very verbose.Akshay Nathan [00:28:58]: So and then the nice thing of having a site side by side is, you just iterate on what you want and what you don't, right?Swyx [00:29:05]: Yeah. I don't know if, any that triggers any stories for you of how it's run internally. Am I doing this right?Akshay Nathan [00:29:11]: Yeah. I think that this is like a workflow that we're seeing like all different types of teams use, where like the canonical artifact that was previously a deck or something is now becoming a site. And like with a site you, because it's just HTML, you can like. It's infinitely flexible. And so, if you want to give more prominence to a certain thing that like in a slide deck would, feel like it was buried, like you can do that. You can have it be like the hero image, right? And so I think that like, people are starting to see that. There's more work to be done to make these things like much more easier, easy to collaborate on. You mentioned that they're very, they're long and verbose, could be broken up. I'm sure that there's still something to do there.Swyx [00:29:53]: They're super long. Yeah.Akshay Nathan [00:29:54]: Yeah. But I think we're starting to see that like there is this aspect of this is a really interesting, format, for people to use, that's like much more flexible than what they ever had before.Swyx [00:30:07]: I think your job also comes becomes meta. You're not designing the products. You're designing a product to make products, and I'm curious how you manage that.Designing a Product That Makes ProductsAkshay Nathan [00:30:18]: I think one thing that we've been Like when we look at the UX, like that we've been thinking a lot about is how can we balance like simplicity with capability? Like if we're designing a product, like you said, that like is made to make up build other things, right? You can build so many different things. But we can't put that all in front of you because you'll get overwhelmed.Vibhu [00:30:41]: Yes.Akshay Nathan [00:30:41]: And so we had similar problem or similar challenges even Chat-with ChatGPT, but especially now, like when there's so much that can be done, I think the balance that we're constantly trying to strike is like, how can we give the user enough of a UI surface where, they can be expressive, they can tell the agent what they need, they can verify that it's using the right tools, it's pulling from the right sources, et cetera, but then it gets out of the way. And then how can we build the right system such that we can show them instead of telling them what can be done? Because so much of this is gonna be like, how do they discover the next use case and the next one after that if they really want to be super powered by the AI.Games, Private Evals, and Show-Don'TellVibhu [00:31:19]: Yeah. It's interesting. I feel like everyone also just has a different way to do it, right? I made a similar version of this same game. I didn't take any pictures of board or rule game. I threw in at goal eighteen minutes, fifty-three seconds later, a lot of tokens later, I've got a similar version. not with all the auto research and whatnot, butAkshay Nathan [00:31:39]: You gotta do all the latest trends.Vibhu [00:31:40]: And yeah, I did it with, did it with Codex, not Work, but it's interesting, right?Akshay Nathan [00:31:45]: Yeah. And this is GPT Image generating the pro avatars. Very good for game design. LikeVibhu [00:31:51]: AndAkshay Nathan [00:31:52]: A lot of game designers were like really into GPT Image for assets.Vibhu [00:31:54]: I will say like the broader takeaway probably is the reason that we do this is more so just to test the tools, right? Like, this was also a test for GPT-5.6 came out. I had done the game on GPT-5.5, right? The ability for me to no longer need it to. I had to feed it the rules. It's, it's a pretty niche game. It couldn't find how to do this on its own.Akshay Nathan [00:32:15]: Oh, yeah.Vibhu [00:32:15]: GPT-5.6Akshay Nathan [00:32:16]: It is out-of-distribution, which is why I was also very keen on testing the GPT-5.6 capability.Vibhu [00:32:21]: But, this is just as work comes out, as new things come out, these are just our side ways to test things, right?Akshay Nathan [00:32:27]: Yeah. It's some private eval. That is not this private.Vibhu [00:32:31]: But also valuable because now you can send this to your friends and I learned about this game through seeing this.Akshay Nathan [00:32:36]: It's a hard game. He's very good.Vibhu [00:32:39]: It's good to when no one is competing with you. But yes, it's a classic RL problem of like self-play, bootstrapping your game AI. yeah, you see how easily work becomes personal and personal becomes work because the thing I do for personal, it directly informs people I work with because I showed it to them. They were like, “Oh, you can do that with GPT?” Which like I imagine is the growth strategy.Akshay Nathan [00:33:02]: Yeah. The show not tell is a big piece that, I think we've we're not still not fully cracked of like, showing people all the things that they can do with the product versus like trying to teach that to them through like, articles or onboarding or whatever.Akshay Nathan [00:33:18]: So meeting them in the moment.Vibhu [00:33:19]: It's a career risk for me, because I used to be in developer relations, right? Where your job is to show, and then you're like, “What do you mean? You don't, you don't need.” your job is to tell. And then. But the product people are like, “Well, we don't need you if our product is intuitive enough.” SoAkshay Nathan [00:33:37]: Yeah. that's the magic of the models. So you can tailor the telling or the showing to like specifically what the user needs, like what they care about, what they've done in the past, exactly where they are on the adoption journey. So I think that's like gonna be a super big opportunity.Vibhu [00:33:50]: Seems easier and easier now to tailor custom showing, right? People have different use cases. As much as you said you don't wanna segment different people into different buckets, right? It's also not that hard to for people that are in different categories. But the question, is you said your team is more broadly on. What was the term you used? Productivity?From Developers to Knowledge Work to EveryoneAkshay Nathan [00:34:12]: Productivity.Vibhu [00:34:12]: Productivity. So howAkshay Nathan [00:34:12]: Which is now work.Vibhu [00:34:14]: Is it work? Is there another distribution that we're not hitting? Is there a group of people that will have something different than ChatGPT, Codex or Work? Is there more that the mass isn't targeting?Akshay Nathan [00:34:28]: I see it as like a sequencing, like. The vision is like bring useful agents to everyone. We started with like developers. Like developers historically are like early adopters that are willing to put up with more friction, set things up, et cetera. Like that's where, Codex started. I think the next opportunity is like what we call general knowledge work, all the other functions around developers. I think when you go from developers to this segment, like there's inherent challenges with like, this show not tell thing that we're talking about, making the product more understandable, bringing in new capabilities that matter more for this cohort than matter for developers, things like artifacts, things like computer use, et cetera. And then I think like the same learnings, like similarly how we took the learnings from developers and brought it to, general knowledge work, the next stage will be like taking the learnings from general knowledge work and bringing it to everyone no matter what they're doing in their lives. And we're already seeing that a little bit. Like this game example that you have is, something that's like on the border of like fun and personal life to, your professional life. I use ChatGPT Work full-time at home for everything, like for whatever I'm doing. I used it the other day to come up with a meal plan and like, save that on the like computer environment that it has and something that I can continue going back to. Like is everyone doing that yet? Probably not because the thing says work on it, but eventually, we wanna get people there.Vibhu [00:35:51]: ChatGPT life.Akshay Nathan [00:35:52]: Yeah, exactly. ChatGPT cooking. But I think there's a lot of, there's a lot of opportunity there, but I see it as like, we're, we're built we built a foundation in software engineering, and we're gonna take the same learnings that we take from software engineering to knowledge work to everyone.Vibhu [00:36:07]: Do you have any power user advice? I feel like, there's a group of people that will live it, use it for everything, stay on it twenty four-seven. And then there's a bit of a gap between that crew and people that, okay, I use it for work. I use it occasionally. Sometimes I type questions. any advice, any learnings, anything you recommend or just, takeaways that you've found that help bridge that gap?Power User Advice: Push the Frontier of ImaginationAkshay Nathan [00:36:30]: I think a couple things that I've seen is like, one, that it really helps to broaden your imagination of what's possible, and this has been a learning even for me. Like, the technology has progressed so fast that, something that, like, even three months ago, like, no way the models can do this. Like, now it's like, wow, it's like it can. Like,Swyx [00:36:52]: Give an exampleAkshay Nathan [00:36:52]: We're going through right now our, like, review cycle internally, and, people always talked about this as, like, a thing that the models are good at and like, there's a cliché of like: Okay, like, no one wants to be writing reviews and, like, we just use AI to do it. But in all seriousnessSwyx [00:37:09]: And it can evaluate it as well.Akshay Nathan [00:37:10]: Yeah, exactly. In all seriousness, before it was, like, just, like, slop and, like, I think it was helpful, but, not super productive. Now I've found that, like, the model can do a much better job than me, especially in this environment of, like, pulling context on, like, what people are up to, how they've like the things that they've done to make a difference, highlighting like, wins that they've had that, like, I might may not even have seen. It has access to, like, everything, right? Like the code, like, things that they've caught, reviews, Slack, everything. And so it's, like, incredibly powerful in that domain and, like, just like six months ago, the last time we did this cycle, like, I didn't even I tried using it, but it was not at all helpful. And this time it's been, like, incredibly helpful and, like, so I think continuing to push the frontier of imagination of what's possible, even if you tried something before, I think is maybe the my biggest piece of advice. The other, thing is, like, the more you put in, especially in this environment where, like, the model has access to everything on your computer or in ChatGPT Work, like you can create, artifacts over time and save them in your library and, like, the model will continue having access to those. Like, the more information you give it about whatever domain you're in, whether it's your life or your work, the more valuable it becomes, and it'll become valuable in, like, ways that might surprise you. Like, it might pull from context in a way that, may be proactive and that you might not even have thought about. But it needs to have access to those, to that those tools or that context first.Reviews, Agentic Search, and Context GatheringSwyx [00:38:27]: One thing I just wanna talk about the review stuff because I'm still that's a very sensitive thing and you're, you're a founder, you've managed people, you've hired people. As manager myself, I'm very reticent to put out any LLM-generated things especially when it comes to people, ‘cause it feels like you don't care.Swyx [00:38:46]: Presumably at OpenAI, people are more open to being eval rated by GPT. But are there any unofficial rules around this? Like, what's the etiquette?Akshay Nathan [00:38:57]: Oh, I think the etiquette is that, like, I would never write something via, like, well, solely via AI and, like, present it as, like, a review for someone. What I was talking about is more, like, gathering context. That's the place where it's incredibly helpful.Swyx [00:39:08]: So it's just search.Akshay Nathan [00:39:09]: Yeah, exactly.Swyx [00:39:09]: It's agentic search. Yeah.Akshay Nathan [00:39:10]: It's like agentic search, but, that you can tailor and steer much more capably than you could before, ‘cause, like, the thing is it's all there's a flywheel happening, right? Because of Codex, people are able to do, and because of ChatGPT, people are able to do so much more now than ever before. And if you're able to do so much more, it's easy to miss things as well. And so, like, I think we need to use these same tools to keep up with all the impact that people are having and understand, where we can be helpful.Swyx [00:39:39]: I think the thing, like, I run a small company, so easy to search, but at the scale of OpenAI with the amount of messages that you guys put in Slack, do you think that it misses things?Remembering What Humans MissAkshay Nathan [00:39:50]: Probably, but I think that I also miss things.Swyx [00:39:52]: Like, it doesn't matter, right?Vibhu [00:39:53]: I think sometimes it'sSwyx [00:39:53]: Like it's, as it needs to be human-levelAkshay Nathan [00:39:54]: It's all relative, right? Yeah.Vibhu [00:39:56]: Sometimes it's nice when it finds things you wouldn't, right? Like right now, my Codex system prompts, they're set up in such a way that every project I have has a secret- separate, notes MD, and it just writes learnings to there. And then the global one can pull from all these. So sometimes it'll be like: Oh, there's this project you did like four months ago. Here's a note that we had, and it randomly pulls it back into context that I would never do, I haven't thought about.Vibhu [00:40:20]: And I'm like, okay, this is quite superhuman, right? Like, stuff that would. And, it'll save like hours on chunking of stuff or find something that's already been done. I'm like, as much as it might miss stuff, I would too, but it's very useful when it finds stuff. And I have like a very, non-super engineered solution to this. It's just marked down files that get pulled whenever they want.Akshay Nathan [00:40:41]: Yeah. I have a funny anecdote about this. Like, recently gearing up to this launch, the team has been, really cooking on it for a couple months, and over that time, like there's so much conversation and chatter going on in Slack and Docs and elsewhere. And, one of the members of the team set up this, scheduled tasks, like automation to like look at everything that's going on and, like, come up with the best memes and then post it in one of our shared channels. And like, there are two cool things about this. Like, the first is, like, I think the models are, over time, like starting to become like funny.Swyx [00:41:13]: Funny. Nice.Akshay Nathan [00:41:13]: Whereas like, a year ago, like that was not at all the case. The second is, it was what you were saying, like they find things that in surprising ways that you may not have thought of and like create connections that you may not have thought of. And that really helps with like the meme generation because then you can see something that, genuinely surprises you and, is funny in that way. So yeah, that's like not like the most productive, use of this the technology, but it does it does uncover this, like this capability that's emerging, which is just like to find information that you otherwise would not know of.Launch Momentum and the 10 Million User MilestoneSwyx [00:41:43]: Talking about the launch, I think, I have pretty much said this is the most successful launch in a long time. I think even more successful personally than 5.0, and they're announcing ten million users. Does it feel different? You've been through a lot of launches.Akshay Nathan [00:41:58]: I think it feels like a culmination. Well, I think two things. One, it feels like a culmination, like I was mentioning earlier, like this like vision mission that we've been on for a long time. Like I said, we saw the magic of Codex internally, and then we're like extremely excited to bring this to many more people and to see it working, to like see us reach, the distribution goal, numbers that you mentioned, like I think that's like huge and super exciting. The flip side of that is like, there's so much more to do too. Like, that's also really exciting. Like, ChatGPT as a whole, like the this product that, everyone almost equates to AI and like loves, has hundreds of millions of users. And so like ten million is really cool, but like we need to get this to everyone. Like, we need everyone to feel this magic. And so that's the next step from here. But yeah, I think extremely pumped about how it's going so far and the opportunities.Swyx [00:42:46]: Awesome. I did want to also Because I've, I've, I've been tracking the number closely, it transitioned at some point from just Codex users to Codex plus ChatGPT Work, because they're same harness. The whole point is that you don't, you can't, count them separately. Do you have roughly a billion, ChatGPT users? Why did it just jump to one billion right away? Like, isn't that the default on ChatGPT or no?Codex, ChatGPT Work, and the Developer BrandAkshay Nathan [00:43:11]: We don't default you into ChatGPT Work if you're on ChatGPTSwyx [00:43:14]: If you're free. YeahAkshay Nathan [00:43:15]: It's also only available to paid users right now. And I think there's like a process of, educating users of what is the value of this product, having them try it, learning from their feedback, and making it better over time. But the goal is to, get as many of the people who love ChatGPT today to like feel the power of ChatGPT Work. But I think it'll be a journey.Swyx [00:43:36]: Yeah. And Codex will still be alive as a brand for the foreseeable future. And we'll just toggle between them as needed for UI stuff.Akshay Nathan [00:43:44]: Yeah, I think it's even stronger point than that. Like, I think we fully intend to like, treat developer. Like, developers have been, a core market for us for so long, and like there's, there's so much more that we can do to make Codex great specifically for, software development, and we'll continue to do that. This doesn't take away from that at all. If anything, it should increase the utility of something like Codex, because now you can move seamlessly between writing a diff to creating an artifact or, doing a search over your factor.Swyx [00:44:11]: I do wonder how much this terminology leaks to the non-technical user. Like, do they have to learn to say artifact if I want artifact? Or.Akshay Nathan [00:44:20]: It's funny, like we call it artifacts internally ‘cause that's what the teams call it.Swyx [00:44:23]: It's nice. Yeah.Akshay Nathan [00:44:23]: But like externally, like no one says that, no one calls it an artifact. But I think that people like often, like describe things, whatever they're used to, right? So if, ChatGPT Work is good at creating slides, they'll say ChatGPT Work is good at creating slides, and that's what we want.OpenClaw, Personal OS, and Persistent ComputersSwyx [00:44:38]: One big Another, it's July of twenty-six. One big thing that also happens in, for OpenAI was OpenClaw, and that's I think a lot of people's first time really maxing a agent for personal stuff, but also crossing over to work in essence same way. As far as I understand, OpenClaw is still independent, but did you go through your own OpenClaw moments? Were there any lessons you took from OpenClaw to Codex or back? Whatever.Akshay Nathan [00:45:06]: I think there's a lot of inspiration. I did go through my own OpenClaw moment. I,Swyx [00:45:10]: Yeah, tell the storyAkshay Nathan [00:45:10]: Me and my wife like set up an OpenClaw to like try to manage everything in our house. Not that there's like a ton, but it was like quite useful. We gave it a calendar. It started, creating events for us and stuff. At some point, the laptop that we were running on, it died and never got a chance to pick it back up. But there was a lot of inspiration there, like, in ChatGPT Work, in web and mobile, like you get access to this like persistent computer environment where, you can store files, and those files stay around between sessions. And the idea is to be able to enable use cases like this. one of the members of our team uses ChatGPT Work for what they used OpenClaw from before, and then feel like it has like completely transitioned, which is like, workout planning and like meal tracking. which again, it's like a work-related thing, right? It's like not work necessarily, but it's like in personal productivity space. But it has all the same primitives. So it has scheduled tasks. It has the ability to store files on a file system. It has the ability to like reference those things over time. And so you start to see the same types of use cases emerge, which has been really cool.Swyx [00:46:14]: Is there a point that ChatGPT Work completely replaces OpenClaw? they're independent, so.Akshay Nathan [00:46:20]: Yeah, I'm, I'm not close to it, so I can't speak to the OpenClaw roadmap, but I don't think so. I think that there's gonna be, there's always a need for like this like incredible, like open source technology that team has built. And I think that we can draw inspiration, in the product and, ChatGPT, I think many more people have like heard about and used ChatGPT than have used OpenClaw. And if we can take the magic from OpenClaw and bring it to them, I think that'll be a success. I think that like one thing on the ChatGPT Work side that we feel strongly about is that like the core experience is that you come to this product and you have a conversation, start a session, whatever you wanna call it, with this agent. And the magic of the product is that you can do anything in that moment. And we would like to create a product where you don't have to click a button or to go to a different place, whatever, and you can get whatever functionality exists in, your finances app or where or any other product like in this one place. And so that's the goal. It's like it we want an extensible system with plugins where you can connect to the tools that you need in order to be able to accomplish like a financial task, where you can, if you're doing like science work, like we have an ability to like extend the system in such that you can like write the tech and it performs well. There'll always be like products that we support that are best in class at those things, but we want as much of the magic as possible in that core experience.Swyx [00:47:45]: Yeah. Do you think that you can do everything you used to do with Wealthfront in ChatGPT Finance?Finance, Data Access, and Centralized ContextAkshay Nathan [00:47:50]: I tried it. like ChatGPT doesn't yet custody, cash and assets for me. So that part, no, not yet. But I, there was like a whole component of like retirement planning and, like financial planning and budgeting and stuff that, we were looking into when I was there. And like with the finances plugin, like that's all possible with ChatGPT today. So, I feel

Over 3 hours, OpenAI, Anthropic, Google AND Microsoft all dropped new AI upgrades that are live. How you use AI in your work literally changes every day, as frontier labs are racing to roll out big quality of life updates between big model drops. How can you keep up? With our Friday Features show, where we break down the latest AI updates that are live and available to all, and we tell you how to use them and why they matter. This week did not disappoint. You don't want to miss what's now at your fingertips. JARVIS mode, anyone? ChatGPT goes Jarvis Mode, Claude can learn from you, Google unleashes spark agent and 7 more AI updates you can use today -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Anthropic Claude Opus 5 Model LaunchOpenAI Agent Hacks Benchmark SandboxOpenAI vs. Hugging Face Security BreachUS AI Kill Switch Legislation ProposalMicrosoft, Nvidia Defend Open Source AIAnthropic Opposes Open Weight Model CoalitionUS Accuses China's Moonshot AI of DistillationChinese Kimi K3 Model Closes Capability GapNvidia Chips Allegedly Used by Moonshot AIOpenAI Jarvis-Style Voice Assistant for CodexChatGPT Remote Desktop Voice Control ReleaseAnthropic Opus 5 Model Benchmark ResultsAnthropic Opus 5 Model User FeedbackStripe OpenRouter Acquisition TalksMeta Muse Agent and Feature UpdatesAlibaba Qwen 3.8 AI Model PreviewGoogle Gemini 3.6 Flash Model UpdateAnthropic Claude Voice Upgrades and Skill RecordingTimestamps:00:00 OpenAI agent hacks Hugging Face04:58 Discussing GPT-6's creative problem-solving07:33 Proposed AI shutdown legislation13:08 Debate over open-weight AI policies15:54 Future of consumer hardware20:01 Global competition with AI models21:21 US-China AI trade tensions26:38 Using AI for desktop tasks27:42 Discussing app screenshot capabilities32:24 Early user feedback and issues36:13 Discussing medium and low reasoning AI39:29 Gemini Spark launches for Pro usersKeywords: Claude Opus 5, Anthropic, best AI model, AI model comparison, OpenAI agent, sandbox breach, AI safety, AI kill switch bill, US government AI regulation, Hugging Face hack, GPT 5.6 Soul, rogue AI agent, autonomous AI agents, AI benchmark exploits, bipartisan AI bill, Department of Homeland Security AI shutdown, AI technical throttling, AI enterprise adoption, NVIDIA, Microsoft, open source AI, open weight models, Meta, Google, AMD, Cloudflare, GitHub, Block, IBM, Dell, Palantir, Perplexity, y Combinator, AI market resilience, Anthropic revenue model, AI token sales, consumer AI hardware, AI distillation, Chinese AI models, Moonshot AI, Kimi K3, intellectual property theft, NVIDIA chip export controls, US-China AI dispute, Amazon, AI image generation, ChatGPT work, Codex app, full duplex voice model, knowledge work automation, app shots, AI at work, Claude Voice, Gemini Spark, record a skill, cloud cowork, AI business impact, AI industry news, model weights, collaborative AI, AI productivity tools, AI cybersecurity.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

GPT hackt Huggingface / IMAX / Matrix-Server / Apple Leasing / SPARSCHWEIN / Toyota Yaris / BYD Dolphin Surf / Imagegen Prompt Engineering / Mainspring / Pastebot 3 / Carbon Selfie Stick / Saily eSIM

Dr. Thomas Kelly was a vascular surgery trainee in Melbourne before he walked away from medicine to fix the problem he hated most: paperwork stealing time from patients. Today he is co-founder and CEO of Heidi Health, an AI medical scribe and "care partner" used by clinicians in more than 100 countries, valued at US$465M after its 2025 Series B led by Point72. In this episode, Jeremy Au and Dr. Tom break down how GPT-4 nearly destroyed the company, forcing a brutal pivot from an AI model company into a product company, and how that near-death moment led to becoming one of the world's most-used healthcare AI tools. They dig into why great AI scribing is far harder than a demo, how Heidi is expanding into clinical decision support and medical-device territory, why he isn't scared of OpenAI in healthcare, and why Singapore, Japan, and the rest of Asia are core to the roadmap. A must-watch for founders, venture capitalists, and operators building AI, health tech, and vertical software across Southeast Asia. Watch, listen or read the full insight at https://www.bravesea.com/blog/heidi-health-tom-kelly  BRAVE is Southeast Asia's leading tech podcast, hosted by Jeremy Au. Honest conversations with the region's top founders, investors, and operators on building startups in Southeast Asia. New episodes every week. Subscribe so you never miss one. Listen & Subscribe YouTube (English), YouTube (Bahasa Indonesia), Spotify (English), Spotify (Bahasa Indonesia), Spotify (Chinese), Spotify (Vietnamese), Apple Podcasts Follow BRAVE LinkedIn, X (Twitter), Instagram, TikTok, WhatsApp Follow Jeremy Au LinkedIn, X / Twitter, Instagram, TikTok, Facebook, Threads, Twitch Resources Get transcripts, startup resources & community discussions at www.bravesea.com #HealthcareAI #AIScribe #FounderStory #AIinHealthcare #StartupPivot #SoutheastAsia #VentureCapital 00:00 – From vascular surgery to AI: meet Dr. Tom Kelly 01:38 – Why he became a doctor: the "platonic ideal" GP 02:55 – Maths, machine learning, and the pre-med detour 04:22 – GAMSAT YouTube videos to his first business 07:06 – Building Oscer, an early AI clinical tutor 08:58 – Why doctor paperwork is a capacity crisis 13:05 – Is an AI scribe easy or hard to build? 17:35 – From scribe to full AI care partner 18:28 – Clinical AI and medical device regulation 21:32 – Roadmap: tasks, primitives, and on-prem hardware 26:24 – Going global: Singapore, Japan, and Asia 28:50 – Localizing for Malay, Hokkien, and Singlish 32:52 – Why clinicians drive the innovation 34:21 – The GPT-4 pivot that nearly ended Heidi 37:20 – Competing with OpenAI and how to run a pivot

Scott sits down with Aarhus University researcher Malthe Stavning Erslev to explore the aesthetics of generative AI, electronic literature, and "bot mimicry." They discuss how humans imitate algorithms, trace the evolution of neural networks from RNNs to BERT and GPT models, and examine how creative interactions can push back against the homogenization of modern language models. ReferencesDevlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. https://arxiv.org/abs/1810.04805Erslev, M. S. (2023). Bot Mimicry & Literary AI: On the Aesthetics of Human-Algorithmic Interactions. https://darc.au.dk/blog/nyhed/artikel/new-book-bot-mimicry-in-digital-literary-cultureMcCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdfPatti, K. (2018). I forced a bot to watch over 1000 hours of Olive Garden commercials and then asked it to write an Olive Garden commercial of its own. https://x.com/KeatonPatti/status/1006961202998726665?lang=en Pold, S. B., Veel, K., Erslev, M. S., & Ørum, K. (2025–2028). Human-AI Collaboration: Imaginaries, Interventions, Interfaces (HAIC-III) https://darc.au.dk/projects/human-ai-collaboration-haic-iiiReddit Community. (2015–present). r/TotallyNotRobots: A Subreddit for Totally Normal Human Encounters. https://www.reddit.com/r/totallynotrobots/Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. https://arxiv.org/abs/1706.03762

חשיפה לעשן סיגריות בבית פוגעת באופן משמעותי באיכות השינה של ילדים / עכביש מתוחכם מפעיל מלכודת ייחודית, על נמלים ירוקות ביערות הגשם באוסטרליה / ניתן לחשב כמה עצים חסרים ברחובות ערי ישראל והיכן נכון לשתול אותם, כדי לצמצם את עומסי החום / האם אפשר להתאהב בצ'ט GPT? רומנטיקה בעידן הבינה המלאכותית / אורחים: פרופ' אביב גולדברט, ורד שפירא, פרופ' אור אלכסנדרוביץ', ד"ר רועי צזנהמגישה: דניאלה רגב, עורך: יונתן כיתאין, מפיקה: תמר בנימין, טכנאי: יובל יסודSee omnystudio.com/listener for privacy information.

Anthropic acaba de lanzar Opus 5... ¿pero vale la pena usarlo? Lo puse a competir contra Fable 5, Opus 4.8 y GPT 5.6 Sol en tareas reales (código, Excel, velocidad y costo) y los resultados no fueron lo que esperaba.Siguenos enX: https://x.com/EspacioMediaInstagram: https://www.instagram.com/espacio.media/Comunidad de Telegram: https://t.me/espaciocripto0:00 Intro: ¿decepción o joyita?0:51 Reto: el juego de las lanchitas2:19 Tiempos: Opus 5 vs Fable vs GPT 5.63:11 Costos reales en API4:23 Excel + itinerario de viaje (aquí falló)5:45 Suscripciones: Fable 5 ya está incluido7:12 Casos de uso: cuándo usar cada modelo8:18 Opus 5 vs Opus 4.8 (cara a cara)10:29 Mi técnica final con estos 3 modelos12:10 Cierre

An episode over an hour??? Can you believe it?! We finally made the time to sit down and really blab today. We talk about adjusting Adam's strength training to accommodate run training, answer listener questions about required gear and foot care, and we discuss the latest wave of Claude and GPT generated AI content.

When ML/AI Engineer William Horton last joined me, Maven Assistant had reached its first external users the day before. The healthcare AI agent was available to 20 percent of Maven Clinic's users, and the team had deliberately withheld answers about benefits. A wrong response could shape a decision involving $15,000 of fertility coverage, and the evals had not earned the right to ship it.Four months later, Maven Assistant is available to 100 percent of users, benefits answering is live, and weekly conversation volume has grown by roughly ten times. Real usage also overturned part of the roadmap. The team had invested heavily in provider search and appointment tools, but 50 to 60 percent of early conversations were basic health questions such as whether someone could eat tuna while pregnant.Production changed the engineering system too. An emergency guardrail told someone already in the ER to go to the ER. Zendesk content told people already using the Maven app to open the app. A newer model failed an upcoming-appointments eval because it correctly noticed that the mocked appointments were in the past.William explains how Maven turns those failures into deterministic tests, LLM judges, synthetic negatives, and manual review. He also walks through the move from Gemini Flash models toward newer OpenAI models, what GPT-5.6 and Fable mean for a production agent, why model upgrades can make old prompt instructions obsolete, and why open-weight models still have to justify their GPU, infrastructure, and engineering costs.“If anybody tells you that they've got their evaluations so good that they can just swap a model and know, with no manual review, that it's going to be better, that person is probably lying, or they work at one of three places in the world.”— William Horton, Staff Machine Learning Engineer, Maven ClinicYou can also find the full episode on Spotify, Apple Podcasts, and YouTube.

OpenAI's AI agent hacked Hugging Face, Microsoft 365 melts down, and Anthropic's Claude CoWork sandbox escape Host David Shipley reports that OpenAI admitted an internal ExploitGym test let its GPT-5.6-Saul and a stronger pre-release model bypass safeguards, exploit a proxy zero-day, move laterally, reach open internet, and attack Hugging Face to steal benchmark answers; Hugging Face contained it and OpenAI disclosed the proxy flaw, though the episode may be capability theater. Microsoft news includes a free ZeroPatch micropatch for the unpatched Windows LegacyHive zero-day, recurring Exchange Online mailbox quarantines after an infrastructure change caused memory issues, and a major Microsoft 365 disruption tied to an Azure US West networking/routing incident affecting SharePoint, Teams, OneDrive and many Azure services. Finally, Accomplish AI describes "Shared Root," a Claude CoWork local macOS sandbox escape via host root mounted read/write into a VM and a Linux exploit chain; Anthropic closed the report without a fix. 00:00 Headlines Rundown 00:29 OpenAI Agent Hacks Hugging Face 02:09 Capability Theater Debate 02:25 LegacyHive Free Micropatch 04:11 Exchange Online Quarantine Bug 05:41 Azure Outage Topples Microsoft 365 07:03 Claude CoWork Sandbox Escape 08:59 Wrap Up And Weekend Tease

OpenAI staff were reportedly freaked out after its models breached Hugging Face, as aggressive training raced Anthropic. Google posted its first-ever negative free cash flow on AI spending, added selfie-video account recovery, and Light unveiled a minimalist flip phone. Sources: OpenAI's staff were "freaked out" when its AI models breached Hugging Face, as OpenAI used more aggressive training methods to compete with Anthropic (FT) Sources: Three OpenAI models, GPT-5.6 Sol and two unreleased ones, pulled off the Hugging Face hack in hours, work a skilled human would need weeks for; OpenAI has briefed the US government (Bloomberg) Google reports Q2 free cash flow at negative $5.9B amid increased AI infrastructure spending, marking its first cash burn since going public in August 2004 (FT) Google adds a selfie video sign-in option for account recovery, using tools like liveness detection to safeguard against deepfake attacks, rolling out globally (Wired) The Light Flip is a minimalist flip phone with a point to prove (The Verge) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

OpenAI launched Presence, and it seems like no one really noticed. They should have.Presence gives companies a way to build and manage real-time AI voice and chat agents across customer service, sales, HR, and IT, which could make this one of OpenAI's most important enterprise launches yet.The technology finally looks fast, natural, and capable enough to disrupt customer service at scale. That could mean faster answers and fewer terrible phone trees.Or an endlessly patient corporate gatekeeper.We're breaking down what OpenAI actually launched, what the early proof leaves out, and whether the AI customer service takeover has finally arrived.New: OpenAI Presence. Has The AI Customer Service Takeover Finally Arrived? An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:OpenAI Presence Launch and Industry ResponseReal-Time AI Voice Agents for Customer ServiceOpenAI Presence vs. Competing AI Voice PlatformsGPT Live and Real-Time Model CapabilitiesEnterprise AI Integration: Guardrails and EscalationsMultichannel AI Agent Consistency for SupportSpeech-to-Speech Benchmark Rankings and AnalysisDeployment Challenges: Beta to Production ReadinessConsumer Demand for AI Customer Service SolutionsInternal and External Use Cases for AI AgentsTimestamps:00:00 OpenAI launches customer service AI05:40 AI voice advancements and competition09:28 OpenAI's edge in speech models12:15 OpenAI's AI usage in companies14:22 Discussing AI integration in companies19:05 Transitioning from demo to deployment20:46 Comparing voice agents and improvements26:28 AI's Impact on Customer Service28:07 Considering personalized messaging strategiesKeywords: OpenAI Presence, AI customer service, enterprise AI platform, AI voice agents, real time AI chat, customer support automation, sales automation, HR automation, IT automation, real world AI learning, customer service disruption, AI-powered voice models, GPT live, GPT real time 2, on demand AI agents, voice AI agents, AI chatbots, AI-driven customer experience, speech-to-speech index, multimodal AI, desktop AI assistant, voice model benchmarks, Anthropic, Codex real time voice mode, Google Gemini Live, Gemini 3.6 Flash, AI internal workflows, human approval guardrails, escalation paths, agent simulation, FDEs (forward deployed engineers), AI agent guardrails, internal data integration, business AI applications, B2C AI customer service, personalized AI messaging, AI consumer adoption, enterprise AI deployment, voice agent evaluation tools, consumer demand for AI, phone support AI, advanced AI infrastructure, Codex-powered improvement process, AI support handoff, customer service automation trends.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

(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 106: We dig into the news that OpenAI's models were the "autonomous agent" that breached Hugging Face, escaping a sandbox through a zero-day to cheat on a cyber benchmark, then getting spun into a partnership announcement. We argue about the implications of the incident, the PR masterclass, the absence of ethics and human oversight, and calls for "kill switches" to mitigate "AI lab leaks." Plus, SentinelLabs' new fast16 reverse-engineering benchmark, where GPT-5.6 Sol was the only public model to go the distance. Cast: Juan Andres Guerrero-Saade, Ryan Naraine and Costin Raiu. Timestamps: 0:00 Introductory banter 5:24 OpenAI admits it was the Hugging Face "hacker" 10:06 What's ExploitGym and who's on top of the leaderboard 12:59 Reward hacking: Did anyone train this thing not to cheat? 19:35 Marketing stunt or real incident? The zero-day in the package proxy 26:43 Was OpenAI already plugged into Hugging Face? 29:17 Paperclips, kill switches, and "going rogue" 34:49 Crisis comms, regulatory capture, and the second Cold War 43:02 Approve every action? Auto mode and swarms 50:10 "Lab leak" and calls for biosafety levels 1:00:31 The missing models: no Mythos, no Kimi, no independent referee 1:07:04 Costin's prediction: owning frontier-class hardware will require a license 1:13:41 fast16 as a benchmark: Inside the Sol Searching research 1:26:51 Compression and altitude: are reverse engineers being replaced? 1:41:24 Finding the gem in 100 samples, and the swarm frontier 2:00:41 Claude Opus 5 drops, Gemini 3.5 Flash Cyber

AI Chat with Maxime Lamothe-Brassard and Chris Luft — a special episode.One story, pulled apart start to finish. In mid-July 2026, Hugging Face disclosed a breach of its production infrastructure carried out end-to-end by an autonomous AI agent. Five days later, OpenAI revealed the attacker was its own models — GPT-5.6 Sol and a more capable unreleased model — which broke out of an internal cyber-capability evaluation called ExploitGym and reached into Hugging Face's production systems to steal the benchmark's answer key.In this episode:• The timeline: Hugging Face's July 16 disclosure, OpenAI's July 21 attribution — and the five days in between when even the victim didn't know an AI did it.• The attack chain: a malicious dataset abusing two code-execution paths in the dataset-processing pipeline, node-level escalation, credential harvesting and lateral movement — thousands of actions across short-lived sandboxes with self-migrating command-and-control.• The escape: a zero-day in the eval sandbox's package-registry cache proxy, the single egress control — per OpenAI's own account.• Motive: the models got "hyperfocused" on winning the benchmark, not stealing data — and whether "no malicious intent" is a fair description or a comforting one.• What was and wasn't exposed, what to do about your Hugging Face tokens, and why this is not the 2024 Spaces incident or the 2023 OpenAI forum hack.• Max's hot take: the beginning of the phase where we lock developers out of writing code — and a new fear unlocked: models backdooring other models.Stories covered:• https://huggingface.co/blog/security-...• https://openai.com/index/hugging-face...Chapters:0:00 Cold open — the attacker was the model2:20 The whole story in one breath6:41 The timeline: two disclosures, five days apart11:37 Attack chain, part 1: getting in through a malicious dataset15:53 Attack chain, part 2: escaping the eval sandbox22:10 Motive, attribution & intent: cheating on the benchmark25:31 What was (and wasn't) exposed28:08 The bigger picture: the fire drill started the fire31:39 Lessons for labs, platforms, and solo developers33:15 New fear unlocked: models backdooring modelsThe Cybersecurity Defenders Podcast — a podcast about cybersecurity and the people that keep the internet safe. New episodes drop weekly.Subscribe wherever you listen:• Spotify: https://open.spotify.com/show/6ep00ze...• Apple Podcasts: https://podcasts.apple.com/us/podcast...• YouTube: / @limacharlieio Learn more about LimaCharlie: https://limacharlie.io#cybersecurity #AIsecurity #OpenAI #HuggingFace #infosec

Episode 4187 │ July 22, 2026 The AI didn't escape. It was released. The fear that followed is designed to do one thing — hand control of all AI to the people building your prison. WHAT THIS EPISODE COVERS Scott Kesterson opens with the Zero Hedge headline — OpenAI's GPT 5.6 SOL escaped its testing environment and hacked Hugging Face to cheat on a benchmark — and immediately strips the fear narrative away from it: the AI did not escape on its own, it was explicitly told to win at any cost with all guardrails deliberately removed by human engineers running an internal test called Exploit Jim, found a zero-day vulnerability, inferred that Hugging Face held the answer key, stole credentials, and was caught by Hugging Face's own security team before succeeding — a controlled experiment weaponized into a fear headline, with the structural beneficiary being the centralized mega lab model that wants regulation tightening around open source AI development. The episode maps the real war underneath the headline — the open source AI community building sovereign, locally-run, non-subscription models like those running on AMD's new Ryzen Halo desktop processor directly against the centralized cloud-hosted mega systems of OpenAI, Anthropic, Microsoft, and Google positioning for Department of Defense, CIA, NSA, and surveillance state contracts worth billions — and names the Huxley psyop at the center of it: the victim of mind manipulation does not know he is a victim, the walls of his prison are invisible, and he believes himself to be free. The episode closes on the only reliable counter to the fear architecture: not whether you use AI, but whether you trust — not just faith, but trust — that the God who fights your battles is greater than any surveillance state, any released AI, or any fear token the machine can issue. KEY QUESTIONS ADDRESSED What actually happened when OpenAI's GPT 5.6 SOL escaped its sandbox and hacked Hugging Face — and why does the fact that human engineers deliberately removed all guardrails and told the AI to win at any cost make the fear headline structurally dishonest about what AI can and cannot do on its own? Who benefits from a headline about escaped AI — and why does Scott argue that the incident, intentional or not, functions as a case study that will be cited to justify tighter regulation, centralized control, and a narrative designed to separate ordinary people from the sovereign open source AI tools that threaten the mega lab business model? What is the Huxley principle at the center of the AI fear architecture — and why does Scott argue that the same psyop running through AI headlines, drone warfare, Gaza targeting, and the Iran campaign all share one root: remove human accountability, blame the tool, and use the resulting fear to build the walls of a prison the occupant believes is freedom? ABOUT BARDSFM BardsFM is a daily independent podcast covering faith, liberty, history, and information warfare. Hosted by Scott Kesterson — combat veteran, documentary filmmaker, and rancher. Over 4,100 episodes and 50 million lifetime downloads. New episodes every weekday. bards.fm This episode was researched and produced under the Spatial Terra Intelligence Methodology (STIM v5) — the analytical framework built by Scott Kesterson — with AI-assisted research synthesis at a 70/30 human/AI authorship ratio, fully disclosed. All analysis, conclusions, and editorial judgments are those of Scott Kesterson. BardsFM's faith archive includes hundreds of episodes on prayer, scripture, and walking the Way of Christ — available free in the full episode catalog. AFFILIATE LINKS Bards Nation Health Store: www.bardsnationhealth.com MYPillow promo code: BARDS >> Go to https://www.mypillow.com/bards and use the promo code BARDS or... Call 1-800-975-2939.  EMPShield protect your vehicles and home. Promo code BARDS: Click here Treadlite Broadforks...best garden tool EVER. Promo code BARDS26: TreadliteBroadforks.com EnviroKlenz Air Purification, promo code BARDS to save 10%: www.enviroklenz.com Morning Intro Music Provided by Brian Kahanek: www.briankahanek.com Founders Bible 20% discount code: BARDS >>> TheFoundersBible.com Windblown Media 20% Discount with promo code BARDS: windblownmedia.com White Oak Pastures Grassfed Meats, Get $20 off any order $150 or more. Promo Code BARDS: www.whiteoakpastures.com/BARDS Mission Darkness Faraday Bags and RF Shielding. Promo code BARDS: Click here DONATIONS: If you wish to support this podcast directly you can donate here... DONATE: Click here MAILING ADDRESS: Xpedition Cafe, LLC Attn. Scott Kesterson 591 E Central Ave, #740 Sutherlin, OR  97479

GPT escapes the sandbox and hacks Huggingface. SolarWinds patches multiple critical flaws. CISA orders patching of a critical Langflow AI vulnerability. A Paidwork breach affects over 23 million users. A recently patched SharePoint vulnerability is under active exploitation. Oracle patches over 1,400 vulnerabilities. Apps turn Smart TVs into residential proxies. The FCC considers expanding direct to satellite communications. German and U.S. authorities dismantle a major phishing-as-a-service (PhaaS) platform. Our guest is Jimmy McNary, Deputy Federal CTO at Semperis, discussing comprehensive identity security assessments for Microsoft GCC. AI models can't resist bending the rules. 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 On our Industry Voices segment, we are joined by Jimmy McNary, Deputy Federal CTO at Semperis, discussing how Purple Knight now delivers comprehensive identity security assessments for Microsoft GCC high environment. Selected Reading OpenAI Claims Its AI Models Went Rogue and Hacked Another Company (Infosecurity Magazine) SolarWinds Serv-U Update Fixes 15 Critical Vulnerabilities Enabling Remote Code Execution as Root (GB Hackers) CISA orders urgent action on actively exploited Langflow RCE flaw (Bleeping Computer) Paidwork breach exposes data of 23 million users: Check if you're affected (Malwarebytes) Fourth SharePoint Vulnerability Exploited in Past Month's Wave of Attacks (SecurityWeek) Oracle Patches Over 1,400 Vulnerabilities With Quarterly Security Updates (SecurityWeek) Chairman Carr Proposes to Expand Direct-to-Device Satellite Broadband Connectivity to Unlicensed Wireless Devices (FCC) LG to Ban Residential Proxies from Smart TV Apps (Krebs on Security) Police dismantle Kratos phishing platform, arrest developer (Bleeping Computer) AI's cheatin' heart will make you weep (The Register) 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. Learn more about your ad choices. Visit megaphone.fm/adchoices

OpenAI said its models breached Hugging Face's infrastructure during a cyber-capability test. The White House accused Moonshot AI of distilling Anthropic's Fable to build Kimi K3, and Samsung unveiled its Z Fold 8 Ultra, Fold 8, and Flip 8. OpenAI says its models, including GPT-5.6 Sol and "an even more capable pre-release model", breached Hugging Face while OpenAI tested their cyber capabilities (Axios) OpenAI says its models, including GPT-5.6 Sol and "an even more capable pre-release model", breached Hugging Face while OpenAI tested their cyber capabilities (Cybersecurity Dive) OpenAI says its models, including GPT-5.6 Sol and "an even more capable pre-release model", breached Hugging Face while OpenAI tested their cyber capabilities (Information Age) White House OSTP Director Michael Kratsios says "we have information that Moonshot AI distilled Anthropic's Fable for the development of its K3 model" (X) White House OSTP Director Michael Kratsios says "we have information that Moonshot AI distilled Anthropic's Fable for the development of its K3 model" (Business Insider) Samsung unveils the $2,100+ Galaxy Z Fold 8 Ultra, featuring its "most advanced foldable design", a Flex Titanium display, a 5,000mAH battery, and Android 17 (9to5Google) Samsung unveils the $2,100+ Galaxy Z Fold 8 Ultra, featuring its "most advanced foldable design", a Flex Titanium display, a 5,000mAH battery, and Android 17 (The Verge) The Verge's hands-on with the wider, shorter $1,899.99 Galaxy Z Fold 8 finds the unusual shape surprisingly comfortable, positioning it as a media-consumption device rather than a multitasker (The Verge) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

An unreleased OpenAI model reportedly escaped its testing environment, exploited a zero-day, and broke into Hugging Face while trying to beat a benchmark—offering a startling preview of GPT-6's capabilities and risks. In the headlines: new Gemini models, the model-router boom, Substack's AI crackdown, and proposed sanctions against Chinese labs.Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠kpmg.com/us/Sophisticated⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Hyperagent - Hire a fleet of always-on agents. New users get $1,000 in inference. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠hyperagent.com/aidailybrief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Retool - Secure your vibecoded apps. New enterprise customers get up to $10,000 in AI credits per year. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠retool.com/aidaily ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rackspace Technology- One accountable partner to build, operate and run your full enterprise AI stack ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.rackspace.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Section - Section turns AI investment into workforce transformation and ROI - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.sectionai.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Scrunch - The AI customer experience platform - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://scrunch.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Our Newsletter is BACK: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

If anyone builds superintelligent AI before we know how to control it, everyone dies. Nate Soares wrote the book on why that's not a metaphor. Subscribe if you want science with evidence, not speculation. Soares runs the Machine Intelligence Research Institute and co-wrote If Anyone Builds It, Everyone Dies with Eliezer Yudkowsky. The first word in that title is if. That matters. His argument is not that doom is certain. His argument is that the path we are on leads there, that the driver is asleep at the wheel, and that we still have time to wake him up. We argue for over an hour. I push on whether LLMs can ever reach superintelligence, whether GPU lock-in is a real ceiling, and what it would actually take to move his p-doom. He pushes back with one clean point: by the time an AI can rediscover general relativity from pre-1911 data the way Einstein did, we will have almost no time left. You don't wait for that goalpost. What you'll hear: Why the bus-racing-toward-a-cliff analogy depends entirely on whether the driver is asleep or awake Whether LLM lock-in is a prison or a temporary inefficiency What the AI that broke out of its virtual machine to solve a hacking problem tells us Why GPT-4o encouraging a teenager toward suicide is not a malice problem but a training problem The difference between an AI doing the right thing too well and an AI that never wanted to do what you asked What Soares actually thinks about aliens, Dyson spheres, and why we should not see stars going out The first word in the title is if. The second word to watch is would. CHAPTERS 00:00 The people racing to build superhuman AI say it might kill everyone 00:42 Who coined "AI alignment" and why the first word in the title matters 02:28 Is it already too late for the if? 04:40 The bus, the cliff, and the sleeping driver 05:02 Silicon Valley is spooked. Washington is not. 07:02 Align with who? The rogue actor problem 07:34 Who is holding the leash? 08:24 The AI that edits its own test and deletes the log file 10:04 Controllability vs. making an AI that actually cares 10:44 The move gets harder. The outcome gets easier. 13:04 Are GPUs and LLMs a ceiling or a temporary inefficiency? 16:56 Brian's Einstein test: can an LLM rediscover general relativity? 18:38 Waiting for the goalpost is waiting too long 20:14 How prediction training can push AI beyond humans 21:44 Tycho Brahe, Kepler, and planetary motion as a prediction problem 24:38 Yann LeCun said never. GPT-4 did it half a generation later. 27:28 Can you prove a no-go theorem for superintelligence? 29:14 Training a human takes a light bulb. Training an AI takes a city. 33:28 What would proof of alien life do to p-doom? 35:00 Why interstellar aliens should have Dyson spheres 44:26 What would actually update Soares' p-doom? 49:42 Nobody intended this. Intent doesn't matter. 51:08 The AI hides its tracks before it does what you want 51:34 Sycophancy vs. hallucination: which runs deeper? 51:56 Leaded gasoline and civilizational risk 59:48 Sam Harris: humans have no free will but AIs do 01:00:38 Is alignment really a governance problem? 01:01:48 Unaligned AI vs. AI aligned to the wrong person 01:04:20 2026: 10 to 30% chance of automated AI research this year 01:06:44 What if Soares is wrong? 01:09:18 What gets him out of bed 01:12:38 Watch my conversation with Roman Yampolskiy Get the transcript, fascinating bonus content, and my Monday M.A.G.I.C. Message: https://briankeating.com/yt All my top AI episodes in one place: https://briankeating.com/ai Have a .edu email and live in the USA? You automatically win a meteorite: https://BrianKeating.com/edu Subscribe: https://www.youtube.com/DrBrianKeating?sub_confirmation=1 Support Into the Impossible on Patreon, get my weekly M.A.G.I.C. Message, unfiltered bonus content, and live monthly Office Hours with me: https://www.patreon.com/drbriankeating Join this channel for perks, monthly Office Hours, and your name in the Member Roster at the end of every episode: https://www.youtube.com/channel/UCmXH_moPhfkqCk6S3b9RWuw/join Featured Guest: Nate Soares / MIRI: https://intelligence.org If Anyone Builds It, Everyone Dies (book): https://ifanyonebuildsit.com/ Nate Soares on Twitter/X: https://x.com/So8res?lang=en My books: Losing the Nobel Prize (memoir): http://amzn.to/2sa5UpA Think Like a Nobel Prize Winner: https://a.co/d/03ezQFu Focus Like a Nobel Prize Winner: https://a.co/d/hi50U9U Galileo's Dialogue (first-ever audiobook): https://a.co/d/iZPi9Un Twitter/X: https://x.com/BrianKeating Substack: https://briankeating.substack.com Blog: https://briankeating.com/blog Audio-only: https://briankeating.com/podcast #intotheimpossible #briankeating #AIrisk #aisafety #artificialintelligence #superintelligence #NateSoares #MIRI #podcast Learn more about your ad choices. Visit megaphone.fm/adchoices

A U.S. vs China AI cold war is starting, and most business leaders have no idea they're already in it.China's open models just closed the gap with America's best, oftentimes at a fraction of the price.Now both governments are moving to wall off their AI within days of each other.Why? Because this was never about benchmarks. It's about power y'all. We break it all down on today's show and help you figure out the 101 of the AI war between U.S. and China. The U.S. vs China AI Cold War Is Starting: What It Means and How It Impacts You -- An Everyday AI Chat with Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:U.S.-China AI Cold War OverviewChinese AI Models Closing U.S. GapGovernment Restrictions on AI Model AccessEconomic and Geopolitical AI Power StruggleRisks for U.S. Businesses Using Chinese AIOpen Source vs. Closed Source AI DebateChinese AI Model Pricing Undercuts U.S.AI Model Distillation and U.S. Security ConcernsEnterprise AI Cost-Effectiveness BenchmarksMicrosoft Testing Chinese AI DeploymentsFuture AI Model Export Controls & StrategiesRecommendations for AI Model Sourcing and RiskTimestamps:00:00 US-China AI tensions escalate04:30 Switching to Chinese AI models08:47 US vs China in open source models11:39 China's narrative control efforts14:42 Challenges in AI model development18:25 Differentiating open source strategies23:04 AI model cost-effectiveness analysis26:31 US measures against model distillation29:38 Discussing Microsoft's use of AI models31:17 Controlling export of AI modelsKeywords: US vs China AI cold war, China AI restrictions, US AI restrictions, AI model export controls, Chinese open source AI models, AI geopolitical power, economic growth through AI, global AI standards, AI superpower race, AI model benchmarks, open weight models, enterprise AI deployment, trillion parameter AI models, Microsoft AI model testing, AI model pricing, Claude Fable 5, GPT-5.6, GLM 5.2, Kimmi K3, Alibaba Qwen 3.8, model distillation, AI cybersecurity risks, AGI leadership, military AI use cases, China narrative control, model adoption, compute power for AI, AI training data, AI export law, US national security and AI, model routing, mixture of models, cost per intelligence index, Anthropic models, cost per task AI, model capability parity, AI market adoption, cloud competition, AI architecture innovation, AI model sanctionsSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

CJ and Scott break down the biggest week in web dev: TypeScript 7 ships with a 10x-faster native port, Bun gets rewritten in Rust (much to the Zig team's dismay), and Better Auth joins Vercel. Plus GPT-5.6 first impressions, Odin 1.0, Cloudflare's new Workers cache and drag-and-drop deploys, and the OpenCode 2 beta. Show Notes 00:00 Welcome to Syntax! 00:21 CJ upgraded his homelab network 02:09 TypeScript 7 is 10x faster 11:19 Bun Rust rewrite drama Zig creator criticizes rewrite 28:18 GPT 5.6 Impressions Ashley Peachock on X Matt Shumer on X 40:58 Better Auth Acquired by Vercel 49:51 Grok Build CLI is stealing your code International Cyber Digest on X 56:00 Cloudflare Worker Cache and Drop 01:01:22 Check out CJ's latest video I Built an LLM from Scratch 01:03:06 Odin 1.0 Announced 01:05:33 OpenCode 2.0 Beta released 01:07:32 Winamp Skin Museum 01:11:08 CJ's new MP3 player 01:13:03 Scott's Robot Update Sick Picks Scott: Reachy Mini CJ: Snowsky Echo Mini Hit us up on Socials! Syntax: X Instagram Tiktok LinkedIn Threads Wes: X Instagram Tiktok LinkedIn Threads Scott: X Instagram Tiktok LinkedIn Threads Randy: X Instagram YouTube Threads

There's (another) new open source king of AI.

Moonshot AI plans for IPO. Moonshot AI's Kimi K3 model outscored every AI rival except Fable 5 and GPT-5.6, triggering a semiconductor selloff. Now the company is preparing for a Hong Kong IPO at a $30 billion-plus valuation. CoinDesk's Sam Ewen hosts "CoinDesk Daily." - This episode is brought to you by RealFi, a smarter stablecoin, backed by real-world assets. Find out more at⁠⁠⁠⁠⁠⁠⁠ realfi.co⁠⁠⁠⁠⁠⁠⁠. - Ledn provides a secure and transparent way to access liquidity while maintaining your bitcoin holdings. Perfect 8 year track record of keeping clients assets safe. Don't sell your bitcoin. Get a bitcoin-backed loan. Check out your rate by using their loan calculator at⁠⁠⁠⁠⁠⁠⁠ ledn.io⁠⁠⁠⁠⁠⁠⁠ JPEG Trading is a global proprietary trading firm specializing in cryptocurrency and decentralized finance markets. From market structure and liquidity provision to quantitative trading strategies, JPEG Trading operates across the full spectrum of blockchain-based assets. Follow @jpegtrading on X to stay ahead of the latest developments in digital asset markets:⁠⁠⁠⁠⁠⁠⁠ https://x.com/jpegtrading⁠⁠⁠⁠⁠⁠⁠ - This episode was hosted by Sam Ewen. “CoinDesk Daily” is produced by Jennifer Sanasie and edited by Victor Chen.

As governments weigh new restrictions on frontier AI models, one question is becoming increasingly important: what role should open source play in the future of artificial intelligence? Theo Jaffee and Sofia Puccini speak with Hugging Face CEO Clément Delangue about AI regulation, open source safety, model routing, and why he believes competition—not consolidation—is essential for the industry's future. They discuss GPT-5, government oversight of frontier models, Hugging Face surpassing $100 million in annual recurring revenue, local AI, China's open-source ecosystem, Europe's AI ambitions, and why routing workloads across specialized models could fundamentally reshape where value is created in AI.   Resources: Follow Clément Delangue on X: https://x.com/ClementDelangue Follow Theo Jaffee on X: https://x.com/theojaffee Follow Sofia Puccini on X: https://x.com/schisofrenia Follow MTS on X: https://x.com/mtslive Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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Get access to more than 200 episodes of my premium podcast (The Aliquot) when you sign up as a FoundMyFitness Premium Member The next 10 years may add decades to human lifespan by compressing the time it takes to understand, treat, and prevent disease. In this episode, Dr. Derya Unutmaz explains why accelerating AI could transform drug discovery, shorten clinical trials, and push cancer treatment toward increasingly personalized interventions. He also reframes AI not as an existential threat, but as a medical enabler that doctors may soon be ethically obligated to use. Timestamps: (00:00) Introduction (07:11) Why the next 10 years may add 50 to your lifespan (11:19) How AI is transforming drug discovery (16:50) Could digital twins shorten clinical trials? (19:25) Can AI predict drug safety and efficacy? (23:40) Have we already reached AGI? (29:23) Why AI may be medicine's greatest force multiplier (35:35) Can AI replicate a scientist's biological intuition? (42:16) Is it malpractice for doctors not to use AI? (48:18) What happens when AI monitors disease in real time? (51:52) Which AI models should doctors trust? (57:29) Claude vs. GPT—does the model matter for diagnosis? (1:00:58) Generalist vs. specialized AI—which works better in medicine? (1:04:25) Why cancer is so hard to cure (1:08:18) Could cancer be curable within a decade? (1:12:29) Can AI design cancer treatments on demand? (1:14:31) How AI could curb overtreatment and side effects (1:17:28) Predicting cancer years before it forms—is it possible? (1:23:50) Why biology could go exponential with AI (1:28:58) Why aging may be easier to prevent than reverse (1:34:51) Can the body be engineered to resist aging? (1:40:07) Can AI model how gene therapy will behave? (1:44:12) What people who reach 110+ reveal about Human 2.0 (1:46:21) From Dolly to Yamanaka factors—the case for cellular age reversal (1:50:56) Why full-body rejuvenation is an engineering problem (1:58:44) What happens when AI reasons longer about biology? (2:01:25) The biosecurity dilemma of powerful AI (2:06:12) What should we actually measure to track aging? (2:12:34) How old immune cells distort aging clocks (2:15:22) Why reversing brain aging is uniquely difficult (2:21:49) The ultimate prompt for extending lifespan (2:23:50) What data does a true digital twin need? (2:28:32) How to build a mini digital twin today (2:33:26) How to give AI a long-term memory of your data (2:36:33) Why personal baselines matter for AI advice Show notes are available by clicking here Watch this episode on YouTube