Podcasts about prompting

Over the past year Chris Perry, Chief Learning Officer at firstmovr, has had literally, and I mean that literally, hundreds of conversations with ecommerce leaders and doers about the ways in which AI can be wielded to simplify work and improve outcomes. As they often do at firstmovr, they generously created a new series of free content called PROMPTED that clearly lays out some of those AI cheat codes that can supercharge your work and help you see around corners. We interrogated him about a few of those to get you started.

Philipp Maderthaner ist Unternehmer, Agentur-Founder, Investor und er hat ein Setup entwickelt, das KMUs durch die KI-Transformation bringt. Im Gespräch mit Podcast Host Johannes Pracher geht's um Ambition, Klarheit und warum du KI nicht „bedienst“, sondern führst. Außerdem stellt er sein neues Signature-Event "Business Mindset Mastery vor. Sie sprechen über Wie nutzt du KI als Sparringpartner, damit deine Ambition wächst? Warum ist Prompting weniger Handwerk – und mehr Haltung? Was musst du beenden, um wirklich Tempo aufzunehmen? Welche Jobs/Branchen bleiben menschlich stark: Handwerk, Erlebnis, Community? Warum Vertrauen & Erfahrung künftig mehr zählen als Wissen? Mehr Infos zu Philipp Maderthaner's exklusiven Signature-Event "Business Mindset Mastery": https://philippmaderthaner.com/mindsetmastery Zum Podcast "Philipp Maderthaner Unplugged": https://philippmaderthaner.com/podcast Zum Youtube-Kanal: https://www.youtube.com/@philippmaderthaner

Im Podcast begrüßt dich Thomas Mangold und präsentiert dir vier außergewöhnliche KI-Hacks, die deine tägliche Arbeit mit künstlicher Intelligenz auf ein neues Level heben können. Ganz gleich, ob du bereits intensiv mit KI arbeitest oder sie nur gelegentlich nutzt – diese Tipps versprechen massiven Zeitgewinn, verbesserten Output und eine spürbare Vereinfachung deiner Arbeitsprozesse. Freu dich auf praxistaugliche Methoden wie die Prompt-Umkehrtechnik, das Säulendokument-Prinzip, den gnadenlosen Kritiker und das Bauplangerüst. Thomas Mangold verrät dir, wie du in Zukunft bessere Prompts erstellst, aus einem Dokument verschiedenste Inhalte generierst, deine Ergebnisse vorher smart überprüfen lässt und mit durchdachter Struktur zu optimalen Resultaten gelangst. Natürlich gibt's auch einen wahren Bonus-Tipp zum Abschluss! Viel Spaß und Inspiration beim Zuhören – und vielleicht kennst du ja jemanden, der von CAI noch profitieren kann? Dann teile gerne diese Folge! **Hier geht es zum Werbepartner dieser Podcast-Folge**: https://selbst-management.biz/podcast-partner Links: - Kostenloser Mangold-Academy Bonus-Bereich: https://my.mangold.academy/anmeldung-vip-bereich-2/ - Goodie des Monats: https://my.mangold.academy/courses/einstieg-in-das-selbstmanagement/lessons/goodie-des-monats/ - SelbstmanagementRocks Masterclass: https://selbst-management.biz/selbstmanagement-rocks-masterclass/ - Mein LinkedIn Profil: https://www.linkedin.com/in/thomasmangold/

Wer entscheidet, was ChatGPT antwortet? Nicht nur die Entwickler – auch ein einziger Text, ein sogenannter Systemprompt, der vor jeder Anfrage steht. Und Elon Musk kann den Systemprompt von Grok ändern - damit beeinflussen, was Millionen Menschen täglich an Antworten bekommen. Das klingt technisch, ist es aber nicht: Und genau solche Zusammenhänge versucht Letitia Parcalabescu ihren Zuschauern beizubringen. KI ist längst in der Mitte der Gesellschaft angekommen. Aber das Verständnis für das, was dahintersteckt, hält nicht Schritt. Begriffe wie Large Language Model, Tokenization oder Systemprompt geistern durch Medien und Meetings – und werden kaum erklärt. Letitia ist Forscherin bei Aleph Alpha Research und hat ihren Doktor in Computerlinguistik an der Universität Heidelberg gemacht. Bekannt ist sie aber vor allem für ihren YouTube-Kanal "AI Coffee Break" – kurze, lockere Videos, in denen eine Kaffeebohne die neueste KI-Forschung erklärt. Und so senkt sie die Schwelle dafür, sich mit komplexen Themen auseinanderzusetzen. Im Gespräch mit uns erklärt sie, welche KI-Begriffe wirklich jeder kennen sollte, warum Sprachmodelle besser antworten, wenn man ihnen 10 Euro verspricht – und warum Wissenschaft kein steifes Format braucht, um ernst genommen zu werden. Reinhören und anschließend den Kanal auf YouTube abonnieren: AI Coffee Break with Letititia. Die Kaffeebohne wartet.

It's been revealed the Prime Minister Anthony Albanese was forced to evacuate the Lodge last night due to a bomb threat linked to performances by a classical Chinese dance and music group which is banned in China.  The Shen Yung group - which has been linked to the Falun Gong spiritual movement - is due to hold several concerts in Australia over the coming months.But a newspaper linked to Falun Gong has reported that organisers have been sent threatening emails demanding that the shows be cancelled.The ABC has now confirmed that an email was sent to a supporter falsely claiming that explosives had been placed around the prime minister's residence, and that they would be detonated if the performances by Shen Yung proceeded.The New South Wales Premier Chris Minns says anyone involved in gang crime across the state 'will have the book thrown at them', as police question two men arrested over the alleged kidnapping of Sydney grandfather Chris Baghsarian.Yesterday, police discovered what they believe to be Mr Baghsarian's body in Sydney's north west.Detectives say they believe the intended target was actually a relative of an alleged gangland figure.Australian mortgage borrowers are being warned to brace for more interest rate rises this year, with inflation remaining stubbornly high.January figures show prices rose 0.4 per cent in the month and 3.8 per cent annually - slightly higher than analysts expectations.

Preview for later today: Liz Peek joins John Batchelor to discuss how AI developments are causing market sell-offs in software and logistics, prompting investors to seek alternatives to MAG 7 stocks.1963

Five years. 218 episodes. 110 hours of content. To celebrate, five returning guests flip the script and interview Sani about the agentic web, the future of web optimization, and what makes this podcast tick. Kelly Wortham, Iqbal Ali, Talia Wolf, Jon MacDonald, and Shiva Manjunath each bring their own questions, their own perspectives, and a few personal ones too.Chapters00:00 - Five years of No Hacks01:33 - Kelly Wortham: Why the shift to the agentic web?05:17 - Kelly Wortham: The secret to being a great podcast host08:57 - Iqbal Ali: Why Web MCP is a big deal12:23 - Iqbal Ali: What excites you about 2026?13:58 - Talia Wolf: What everyone misses about optimizing for AI agents15:33 - Talia Wolf: The misleading advice in the industry18:19 - Jon MacDonald: Why brands need agentic web data now25:38 - Jon MacDonald: NBA All-Star Weekend hot takes29:22 - Shiva Manjunath: The skeptic's case against agentic web hype37:56 - Shiva Manjunath: If you were a meme38:37 - What's next for No HacksKey TakeawaysAI middleware is coming to every interaction - Chrome has 3 billion browsers, Apple is putting AI into Siri across every device. There will be an AI layer between every user and every website. This is not five years away. It is happening now.Web MCP could make the agentic web actually work - Current AI agents take 3-5 minutes to fill a basic form on well-coded pages. Web MCP provides a standard interface between your front end and AI agents, making interactions reliable regardless of your HTML quality.Optimizing for AI agents is not a separate discipline - A fully functional website built for humans gets you 80-90% there. Accessibility, semantic HTML, schema markup, fast load times. All the basics you felt bad about skipping? They matter now more than ever.Citation tracking in LLMs is misleading - Prompting an LLM 100 times and averaging your position to 4.7 is not useful data. The rankings model does not translate to AI. Bing Webmaster Tools just launched AI tracking in beta, and Google will have to follow. That is when real measurement begins.Getting ready for AI agents means making your website better for humans- There is not a single reason not to do it. Better technical health, better standards compliance, better user experience. The work is the same.This is not about websites going away - Stores did not go away when e-commerce arrived. Websites will not go away when AI agents arrive. But there is a new channel, and if your site is not ready for it, you can disappear from discovery entirely.Guest HostsKelly WorthamFounder of the Test and Learn Community (TLC). Asked about the shift to the agentic web and what makes a great podcast interviewer.Iqbal AliExperimentation and AI consultant, founder of Ressada. Asked about Web MCP and what excites Sani about 2026.Talia WolfCRO expert, founder of GetUplift, author of "Emotional Targeting." Asked about what people miss when optimizing for AI agents and what common industry advice is wrong.Jon MacDonaldFounder of The Good, author of three books on website optimization. Asked about why agentic web data matters for brands and shared NBA All-Star Weekend hot takes.Shiva ManjunathHost of the From A to B podcast. Brought the skeptic's perspective on agentic web hype and asked what meme Sani would be.No Hacks is a podcast about web performance, technical SEO, and the agentic web. Hosted by Slobodan "Sani" Manic.

CEO & President Kyle and Graphic Designer & Brand strategist Kelsey explore how prompting has evolved from using AI like a “smarter Google” to structured strategies that deliver sharper, less generic results.They break down the CRIT framework (Context, Role, Interview, Task), share why detailed context reduces hallucinations, and explain how prompt libraries and model memory speed up repeatable work. The conversation also dives into context engineering with tools like Microsoft 365 Copilot and Google Workspace Gemini to make AI outputs more relevant and secure.Plus: common prompting mistakes, model comparisons, multimodal inputs, and how to onboard teams without losing brand consistency.Listen now to level up how you work with AI.00:00 Prompting Then vs Now: From “Smarter Google” to Strategic Skill 00:39 Why AI Sounds Vanilla: Averages, Models & AI Slop 01:33 Prompt Engineering & the CRIT Framework 02:35 Interview-Style Prompts: Fewer Hallucinations, Better Results 04:10 Garbage In, Garbage Out: Treat AI Like a New Hire 05:04 Let AI Help Write Prompts + Tools & Libraries 07:08 Why One-Liners Fall Flat (Contractor Analogy) 07:55 From Prompts to Systems: Templates & Model Memory 11:21 Context Engineering: Files, Memory & Workplace Data (Copilot/Gemini) 13:27 Over-Prompting: Context Limits & When to Reset 16:26 Set Outcomes, Don't Micromanage 18:22 Smarter Models: Gemini & Claude Need Less Steering 19:06 Claude Opus vs ChatGPT: Speed vs Detail 20:27 Multi-Model Workflow: Use Each for Its Strength 21:20 Why New Models Feel Smarter 22:11 Ask AI to Improve Your Prompts 24:42 Planning Mode: Structured Builds & AI Interviews 26:13 Training Teams: Frameworks, SOPs & Safe Experimentation 31:47 Multimodal & Voice Prompting (Gemini's Edge) 33:15 Wrap-Up & What's Next

KI-Agenten sind keine Zukunftsmusik mehr – und Du brauchst auch keinen Programmierer dafür. In dieser HVH Education Folge zeigt Dir Marcel Pesch Gründer der Academy4.ai, wie Du Dir Deinen eigenen digitalen Mitarbeiter baust. Live auf der Bühne entsteht für HVH-Member Matthias Hilger der persönliche Assistent "Tommy". Marcel demonstriert eindrucksvoll, wie man DSGVO-konform mit Tools wie Langdock arbeitet, warum "Prompting" eigentlich nur gutes Delegieren ist und wie Du per Sprachbefehl Termine und E-Mails komplett automatisierst.

Willkommen zu unserem KI-Update im Februar 2026! Heute habe ich mich wieder mit meinem Kollegen Thomas Fröhlich hingesetzt, um über die wichtigsten KI-Themen des Monats zu sprechen – und ich verrate euch, wir hätten noch stundenlang weiterreden können! Gleich zu Beginn sind wir tief in eine uralte Diskussion eingetaucht, die durch KI eine ganz neue Relevanz bekommt: Ist KI ein Feature oder ein eigenständiges Produkt? Ich liebe Thomas' Schraubenzieher-Analogie dazu, die das Ganze perfekt auf den Punkt bringt. Wir sind uns einig, dass KI vor allem als Sparrings-Partner in unserem Alltag eine riesige Rolle spielt – ob für Kochrezepte oder komplexe berufliche Fragen. Das führt uns direkt zur Frage, ob wirklich jeder zum Prompt-Engineer werden muss. Ich bin der Meinung: Nein! Aber wir haben beleuchtet, warum das Prompting – ähnlich dem Schreiben eines detaillierten Drehbuchs – eine Fähigkeit ist, die nicht jedem liegt. Eine weitere große Frage war, ob KI ein "Muss" oder ein "Kann" ist. Wir haben darüber gesprochen, dass der niedrigschwellige Einstieg ins Gespräch mit KI für jeden möglich und empfehlenswert ist, während die tiefere Integration in komplexe Arbeitsabläufe mehr fordert. Zuletzt haben wir uns mit der spannenden Frage beschäftigt: Macht KI uns wirklich produktiver oder schafft sie nur mehr Arbeit? Hier hat Thomas das faszinierende Konzept von "Bad Work, Good Work, Great Work" vorgestellt, und wir haben diskutiert, wie KI uns dabei helfen kann, uns auf die wirklich bedeutungsvollen Aufgaben zu konzentrieren. Links in dieser Ausgabe Zur Homepage von Jonas Piela Zum LinkedIn-Profil von Jonas Piela Zum LinkedIn-Profil von Thomas Fröhlich Whitepaper: KI verantwortungsvoll einsetzen Das Einzige, was riskanter ist als KI, ist sie zu ignorieren. Ladet euch jetzt das Whitepaper von Thoughtworks herunter und setzt KI verantwortungsvoll ein.

Click to watch the full episode on YouTube!What if you could offload the busywork that keeps piling up every week and get back hours of focus without hiring a full team? In this episode, I break down how I've been using AI tools as a practical “assistant” for content, real estate, and everyday workflows so tasks stop slipping through the cracks and your systems actually feel manageable.In this episode of the First Sip Podcast, I break down:- Why I started using AI as leverage- How I use tools to capture leads, organize conversations, and reduce the back-and-forth across platforms- Simple automations that handle repeat questions and route people into a CRM without manual work- How AI-powered content workflows turn one episode into multiple short clips and marketing assets- A real example of turning a voice note into a polished presentation using transcription and slide tools- How beginners can start using AI to move past analysis paralysis and build a “virtual assistant” that asks the right questionsHere's the Master Prompt for you to create your own AI Assistant:[Executive Assistant Master prompt ](https://www.notion.so/Executive-Assistant-Master-prompt-2ff557a368e0813e9084c6c76ee132e7?pvs=21) Thank you for listening, and as always…enjoy your first sip! Timestamps:00:00 – Using AI as an assistant for weekly tasks and focuS02:38 – Real estate follow-ups, marketing, and the need for leverage03:31 – Using ChatGPT like a personal assistant and what you'll get by the end04:00 – Turning repeatable weekly tasks into faster workflows04:41 – Using forms, chatbots, and automations to capture info upfront04:58 – Zapier example: sending form responses directly into your CRM05:17 – Instagram DM auto-replies and filtering inbound requests07:00 – Turning long-form episodes into short-form content at scale07:36 – Tools like Opus Clip and CapCut for transcript-based clipping09:32 – Why the future of sales is changing fast10:02 – Real example: building a presentation with AI instead of PowerPoint11:02 – Transcribing audio with Descript11:33 – Using NotebookLM to generate a slide deck from the transcript12:34 – Creating a master prompt with ChatGPT or Gemini 13:32 – Prompting explained: clear instructions lead to better outputs15:50 – Using transcripts to create marketing visuals and infographics16:40 – Starting AI as a beginner and finding your real starting point17:10 – AI for goals: fitness, money, business, and life planning20:32 – The “virtual assistant” master prompt and how it works22:43 – Content recommendation: pair tools with a specific YouTube tutorial23:13 – Practical AI use cases: bloodwork, financial planning, and personal goalsWhat did you think about this episode?--------------------------------

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.Jeremy Zakis describes an aggressive flock of over one hundred cockatoos targeting his home and neighborhood, with the destructive birds stripping trees and performing low fly-bys, prompting fears of further property damage.

Prompting ist nur der Anfang. Wahre KI-Power liegt im Systemdesign durchdachter Automatisierungen. Entwickeln Sie klare Prozesse, dokumentieren Sie diese, standardisieren Sie Abläufe und automatisieren Sie intelligent. Von Support-Systemen bis Event-Nachbereitung revolutionieren KI-Workflows Ihr Unternehmen. Tools wie Zapier, Make und n8n machen es möglich. ----------------------------------------------------------- Lesen Sie den kompletten Beitrag: 580

AI is no longer a tool sitting on the side of our workflow — it's starting to participate in it. In this episode of The FutureProof Advisor, I explore the shift from automation to augmentation, and now toward agent-based execution. Drawing from the Anthropic Economic Index and the rise of tools like OpenClaw and Claude Cowork, I unpack what it means when AI moves from informing us to acting on our behalf — and why that changes how advisory firms must think about technology.The real insight isn't about replacing people. It's about how mature adopters use AI collaboratively, not passively. Prompting is no longer a gimmick — it's the modern version of thinking clearly in writing. And as agent-based systems gain autonomy, firms must be intentional about workflow design, security boundaries, and how human judgment stays in the loop. If everyone has access to the same models, differentiation doesn't come from the tool — it comes from imagination and orchestration.The broader implication is bigger than productivity. As AI begins to erode traditional SaaS advantages and makes custom builds dramatically cheaper, firms face a strategic question: buy, build, or redesign entirely? The advisors who thrive won't be the ones chasing every new tool. They'll be the ones who rethink how they work, define how AI fits into that system, and remain accountable for the outcomes it produces.

What if you woke up every day with absolute clarity about why God created you? In this powerful message, Driven by a Dream, we explore how God gives every person a unique, God-ordained purpose—and how to live it out with faith, perseverance, and humility.Drawing from Ephesians 2:10 and the life of Joseph in Genesis, this sermon walks through The Four Phases of Living With Divine Purpose:The Spirit's Prompting, Certain Uncertainty, Predictable Resistance, and Uncommon Clarity. Through Joseph's journey—from favored son, to betrayal, to prison, and ultimately to God's appointed position—we discover how God uses delay, struggle, and suffering to shape us for greater impact.This message also invites you to reflect on your spiritual gifts, core values, and past experiences to begin discerning your own chazown—your God-given vision. Along the way, you'll be encouraged with real-life testimony, biblical truth, and the hope that God's promises never arrive late.If you've ever wondered “Why am I here?” or felt stuck between promise and fulfillment, this sermon will remind you that God is in control—and He is faithfully working all things together for His purpose and your good.

KI ist kein Hype mehr: Warum jetzt die Phase der echten Transformation beginnt   Die letzten drei Jahre haben vieles verändert. Was mit Neugier, Skepsis und einzelnen Experimenten begonnen hat, ist heute eine ernstzunehmende Realität für Selbstständige und Unternehmer. Künstliche Intelligenz ist nicht mehr das nächste große Ding, über das man spricht – sie ist längst Teil des Arbeitsalltags geworden. Die entscheidende Frage ist nicht mehr, ob KI bleibt, sondern wie bewusst und wirksam du sie für dich und dein Business nutzt.   Torsten Körting auf LinkedIn: LinkedIn - https://www.linkedin.com/in/torstenkoerting/   Vom Schock zur Akzeptanz und was bisher gefehlt hat Viele kennen die klassischen Modelle des Wandels: Schock, Verneinung, Verwirrung, Akzeptanz. In der KI-Entwicklung haben diese Phasen zwar eine Rolle gespielt, doch sie greifen zu kurz. Es gibt Menschen und Unternehmen, die waren lange ahnungslos, andere sind direkt in den Hype gesprungen. Entscheidend sind jedoch die Phasen danach: Integration und Transformation. Integration ist die Spielwiese. Hier wird ausprobiert, getestet, automatisiert. Transformation beginnt dort, wo KI produktiv, strategisch und qualitativ eingesetzt wird. Genau an diesem Punkt stehen heute immer mehr Unternehmer. Nicht mehr links im Widerstand, sondern rechts im bewussten Einsatz. KI im Alltag: Vom Tool zur Selbstverständlichkeit KI hat sich in rasantem Tempo weiterentwickelt. Was früher mühsames Prompting war, ist heute Dialog, Assistenz und Co-Creation. Ob Text, Bild, Video oder Prozesse – KI ist in nahezu allen Business-Tools angekommen. CRM-Systeme, Office-Anwendungen, Marketing- und Vertriebsplattformen integrieren KI inzwischen standardmäßig. Das bedeutet: Du wirst ihr kaum noch ausweichen können. Und das ist auch gut so. Denn der Fokus verschiebt sich vom Experimentieren hin zum Return on Investment. Es geht nicht mehr um Spielerei, sondern um Wirkung, Qualität und Skalierung im eigenen Geschäftsmodell. Human Intelligence zuerst – warum KI Führung braucht So leistungsfähig KI auch ist: Ohne menschliche Intelligenz bleibt sie beliebig. Strategie, Haltung, Reflexion und Verantwortung lassen sich nicht automatisieren. Genau hier liegt die größte Chance für Selbstständige und Unternehmer und für alle, die andere auf diesem Weg begleiten. KI verstärkt, was bereits da ist. Sie braucht klare Ziele, saubere Prozesse und Menschen, die bereit sind, Verantwortung zu übernehmen. Erst wenn Human Intelligence und künstliche Intelligenz zusammenspielen, entsteht echte Wirksamkeit – für dein Business und für deine Kunden. Fazit: Jetzt entscheidet sich, wer gestaltet und wer nur reagiert Die Phase des Wartens ist vorbei. Die KI-Blase platzt nicht – sie integriert sich. In Geschäftsmodelle, in Prozesse, in den Alltag. Jetzt ist der Moment, strategisch zu denken, Anwendungsfälle sauber zu definieren und KI operativ umzusetzen. Wenn du dich heute bewusst positionierst, kannst du nicht nur dein eigenes Business transformieren, sondern auch für andere einen echten Mehrwert schaffen. Die KI ist gekommen, um zu bleiben. Die Frage ist nur: Gestaltest du aktiv mit oder lässt du dich treiben?     Noch mehr von den Koertings ...  Das KI-Café ... jede Woche Mittwoch (>350 Teilnehmer) von 08:30 bis 10:00 Uhr ... online via Zoom .. kostenlos und nicht umsonstJede Woche Mittwoch um 08:30 Uhr öffnet das KI-Café seine Online-Pforten ... wir lösen KI-Anwendungsfälle live auf der Bühne ... moderieren Expertenpanel zu speziellen Themen (bspw. KI im Recruiting ... KI in der Qualitätssicherung ... KI im Projektmanagement ... und vieles mehr) ... ordnen die neuen Entwicklungen in der KI-Welt ein und geben einen Ausblick ... und laden Experten ein für spezielle Themen ... und gehen auch mal in die Tiefe und durchdringen bestimmte Bereiche ganz konkret ... alles für dein Weiterkommen. Melde dich kostenfrei an ... www.koerting-institute.com/ki-cafe/   Mit jedem Prompt ein WOW! ... für Selbstständige und Unternehmer Ein klarer Leitfaden für Unternehmer, Selbstständige und Entscheider, die Künstliche Intelligenz nicht nur verstehen, sondern wirksam einsetzen wollen. Dieses Buch zeigt dir, wie du relevante KI-Anwendungsfälle erkennst und die KI als echten Sparringspartner nutzt, um diese Realität werden zu lassen. Praxisnah, mit echten Beispielen und vollständig umsetzungsorientiert. Das Buch ist ein Geschenk, nur Versandkosten von 9,95 € fallen an. Perfekt für Anfänger und Fortgeschrittene, die mit KI ihr Potenzial ausschöpfen möchten. Das Buch in deinen Briefkasten ... https://koerting-institute.com/shop/buch-mit-jedem-prompt-ein-wow/   Die KI-Lounge ... unsere Community für den Einstieg in die KI (>2800 Mitglieder) Die KI-Lounge ist eine Community für alle, die mehr über generative KI erfahren und anwenden möchten. Mitglieder erhalten exklusive monatliche KI-Updates, Experten-Interviews, Vorträge des KI-Speaker-Slams, KI-Café-Aufzeichnungen und einen 3-stündigen ChatGPT-Kurs. Tausche dich mit über 2800 KI-Enthusiasten aus, stelle Fragen und starte durch. Initiiert von Torsten & Birgit Koerting, bietet die KI-Lounge Orientierung und Inspiration für den Einstieg in die KI-Revolution. Hier findet der Austausch statt ... www.koerting-institute.com/ki-lounge/   Starte mit uns in die 1:1 Zusammenarbeit Wenn du direkt mit uns arbeiten und KI in deinem Business integrieren möchtest, buche dir einen Termin für ein persönliches Gespräch. Gemeinsam finden wir Antworten auf deine Fragen und finden heraus, wie wir dich unterstützen können. Klicke hier, um einen Termin zu buchen und deine Fragen zu klären. Buche dir jetzt deinen Termin mit uns ... www.koerting-institute.com/termin/   Weitere Impulse im Netflix Stil ... Wenn du auf der Suche nach weiteren spannenden Impulsen für deine Selbstständigkeit bist, dann gehe jetzt auf unsere Impulseseite und lass die zahlreichen spannenden Impulse auf dich wirken. Inspiration pur ... www.koerting-institute.com/impulse/   Die Koertings auf die Ohren ... Wenn dir diese Podcastfolge gefallen hat, dann höre dir jetzt noch weitere informative und spannende Folgen an ... über 440 Folgen findest du hier ... www.koerting-institute.com/podcast/   Wir freuen uns darauf, dich auf deinem Weg zu begleiten!  

Many people think being good with AI means knowing how to write clever prompts.But Chris Lema says real expertise today goes much deeper than that.He explains that the real skill is AI orchestration—knowing how to guide AI step by step, the same way a professional would do real work.Instead of using AI as a shortcut, he encourages us to build clear systems that reflect our own standards, experience, and personal voice. When you create simple stages for tasks like writing, editing, and refining content, the results become better, faster, and more consistent.He also reminds us that being honest about how we use AI builds trust. People respect clear methods more than pretending everything was done without help.In the end, an AI orchestrator is like a conductor—using human wisdom to direct technology toward excellent results.And when you think this way, AI stops sounding generic and starts becoming a powerful tool for authentic and scalable creativity.Welcome to the Fruitfulujah Podcast.

From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

AP correspondent Karen Chammas reports, a key nuclear pact between the U.S. and Russia has expired.

AP's Lisa Dwyer reports on the launch of a copy of the Wegovy pill.

In this episode, Bryan McCann joins Henrik and Jeremy to explore how search is evolving from simple queries into more conversational and agent-driven systems, and why prompting is likely a temporary skill. Bryan shares how his definition of productivity changed as an AI researcher, moving away from doing the work himself and toward designing plans and experiments that machines could run continuously.The conversation expands to leadership and organizational design. Bryan explains why helping others learn how to work with AI became his highest-leverage activity, and offers a simple rule of thumb: try to get AI to do the task first, and treat anything it can't do as an interesting research problem. Henrik and Jeremy connect this to Bryan's view that organizations may increasingly resemble neural networks, with information flowing more freely and decisions less tied to rigid hierarchies.Key Takeaways:Productivity can be measured by machine output, not human effortBryan explains how “keeping the GPUs full” became his primary measure of productivity.Prompting is useful, but likely temporaryThe episode discusses why future systems may rely less on explicit prompts and more on inferred context.Try AI first, then learn from what it can't doTasks AI struggles with can reveal meaningful research opportunities.Leadership is about scaling othersBryan shares how his focus shifted from scaling himself to helping his team increase impact.Organizations may benefit from neural-network-like designBetter information flow and fewer bottlenecks can improve decision-making.YOU: You.comBryan's website: bryanmccann.orgLinkedIn: linkedin/company/youdotcom/00:00 Intro: Keeping the GPUs Full00:22 Meet Bryan McCann: CTO & co-founder of You.com00:43 Why Search Is Breaking - and Why It Becomes a Skill01:41 From Search to Agents03:18 The Case for Proactive, Context-Aware AI04:30 We Don't Need New Hardware - We Need Trust05:43 The Trust Problem of Always-On Listening07:57 Trust as the Real Bottleneck (Not AI Capability)09:52 Delivering Immediate Value to Earn Trust12:13 Business Models and Escaping the Attention Economy17:27 What “Agents” Really Mean - and Why the Term Will Fade20:37 Productivity, Parkinson's Law, and Keeping the Machines Running23:52 Scaling Yourself vs. Scaling Your Team29:57 Building Culture: Automate, Throw Away, Rebuild35:46 Designing Organizations Like Neural Networks45:02 Recruiting for Initiative in an AI-Native Organization49:18 The debrief 

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

Sean McMeekin discusses Molotov's 1940 Berlin visit, noting Stalin's brazen demands for influence in Bulgaria and Turkey caused talks to collapse, prompting Hitler to plan Operation Barbarossa, while Roosevelt began lifting moral embargoes anticipating a German-Soviet clash.1931 STALIN AND BERIA

01-30-26 - Late Night Stop At Taco Bell Did Not Sit Well For Holmberg - Phoenix Gives New Speed Camera Contract Prompting Our Calls To Again NOT Pay Any Photo Radar Tickets And Jew/Wop Law will Have Your BackSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

01-30-26 - Late Night Stop At Taco Bell Did Not Sit Well For Holmberg - Phoenix Gives New Speed Camera Contract Prompting Our Calls To Again NOT Pay Any Photo Radar Tickets And Jew/Wop Law will Have Your BackSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Prompting AAC is abuse? Let's talk about that and a few other AAC conversations that keep coming up again and again.In today's solo episode, I'm diving into five hot topics in AAC that have been surfacing repeatedly in my recent calls, trainings, and collaboration meetings. After more than 20 years as a speech therapist and being dually certified as a BCBA, I've seen how confusing, overwhelming, and sometimes divisive AAC conversations can become. I also remember very clearly when AAC felt intimidating to me too.This episode is about cutting through the noise, grounding ourselves in research, and having better, more collaborative conversations about AAC. I share real scenarios clinicians are facing right now, from AAC evaluations that drag on far too long to device access barriers to strong opinions about prompting that simply don't align with the science. My goal is to help you feel more confident, more informed, and better equipped to advocate for your students and clients.Whether you're newer to AAC or have years of experience, these topics matter. AAC is a student's voice, and we have a responsibility to protect, support, and expand it in thoughtful, ethical ways.#autism #speechtherapyWhat's Inside:Why AAC evaluations should be thorough, but not take nine months, and what may be going wrong when they doHow to approach parent-purchased devices, including those bought online, with collaboration instead of fearThe ongoing core versus fringe vocabulary debate, and why research supports using bothWhy prompting is a teaching tool, not abuse, and how misinformation can harm collaboration and progressMentioned In This Episode:Earn CEUs with a community of peers. Join the ABA Speech ConnectionTake the All About AAC bundleABA Speech: Home

It's telling to hear NATO's future is not “guaranteed” according to a former NATO boss. After a jaw-dropping US threat to take Greenland shocked the world. President Trump's first year back in power is also overshadowing the United Nations' 80th milestone. Prompting calls to retool the world's top diplomat.  In this episode of Disorder, hosted by Mark Lobel, the former head of NATO tells Disorder we should make the United Nations Secretary-General the "chairman" of the Security Council. Brazil's former Ambassador to the United Nations says the role should be a single mandate term of six or seven years, "to retain the willingness to displease certain sources of power”. Recorded at a special UNA-UK event, George Robertson and Ambassador Antonio Patriota reveal how Donald Trump's disorderly approach is causing a major re-think of organisations and leadership in the world. Speaking in the same building that ushered in the United Nations General Assembly in 1946, exactly 80 years ago this month.  The Brazilian Ambassador to the UK didn't hold back ...  On Venezuela:  “I feel very uncomfortable as a South American to witness an intervention that is a flagrant violation of international law.”  On Nigel Farage joining climate talks:  “I don't think (he's) very eager to engage on this topic”  On presenting the Nobel Peace Prize to Trump:  “As a South American, I felt embarrassed by this gesture, because I don't think it enhances anybody's dignity to do that.”  Plus - George Robertson tips a British politician as the next big thing ... and it's not who you expect! Stay news of a special live event with Disorder and the UNA-UK for mega orderers, and to join our Mega Orderers Club and come along, and get ad-free listening, early release episodes, and bonus content, visit https://disorder.supportingcast.fm/  Producer: George McDonagh Subscribe to our Substack - https://natoandtheged.substack.com/ Disorder on YouTube - https://www.youtube.com/@DisorderShow Show Notes Links: You can get in touch with Mark, to host or speak at your event here: ⁠https://www.mark-lobel.com/getintouch⁠  To join our Mega Orderers Club in honour of Greg, for ad free listening and early release episodes, visit https://disorder.supportingcast.fm/ UNA UK website www.una.org.uk UN official article summarising the event - https://news.un.org/en/story/2026/01/1166783 Gordon Brown's call to action for democracies to reinvigorate the international order, highlighting the Attorney General's speech at the event - https://www.theguardian.com/commentisfree/2026/jan/20/donald-trump-greenland-world-plan-leadership Devex video interviews on Insta - https://news.un.org/en/story/2026/01/1166783 Sky News - 'Is the US attacking the UN's principles?' (Interview with the President of the General Assembly) https://news.sky.com/video/is-the-us-attacking-the-uns-principles-13495782  The Guardian - 'Guterres warns of ‘powerful forces' undermining ‘global cooperation.'' https://www.theguardian.com/world/2026/jan/17/antonio-guterres-warns-forces-undermining-global-cooperation-un-80th-anniversary-secretary-general-multilateralism-international-law  NPR - 'United Nations leaders bemoan global turmoil as the General Assembly turns 80.' https://www.npr.org/2026/01/18/nx-s1-5678366/united-nations-general-assembly-80-london#:~:text=LONDON — Just over 80 years,the importance of international cooperation.  Full speech by SG: https://webtv.un.org/en/asset/k1u/k1uo45t198 Learn more about your ad choices. Visit megaphone.fm/adchoices

Summary In this episode of the Future of Dermatology Podcast, Dr. Faranak Kamangar and Dr. Jonathan Chen discuss the intersection of artificial intelligence and dermatology. They explore the trust paradox of AI in medical diagnostics, the implications for medical education, and the evolving role of physicians in an AI-driven landscape. The conversation highlights the importance of empathy, judgment, and the need for effective prompting techniques when working with AI tools. They also touch on the future of AI in healthcare and its potential to enhance patient care while acknowledging the limitations and ethical considerations involved. Learn more at: https://med.stanford.edu/ai-in-meded/resources-and-tools.html https://bench.arise-ai.org/ Takeaways - AI can outperform physicians in certain tasks. - The trust paradox raises questions about AI in diagnostics. - Humans may hinder AI's effectiveness in medical decision-making. - Medical education must adapt to include AI training. - Prompting techniques are crucial for effective AI use. - Empathy and judgment remain essential in healthcare. - AI can assist in complex patient conversations. - AI is already integrated into medical practice. - Rethinking medical education is necessary for future doctors. - AI's role in dermatology is rapidly evolving. Chapters 00:00 - Introduction to AI in Dermatology 02:10 - The Trust Paradox of AI in Medicine 05:07 - AI vs. Human Physicians: A New Paradigm 09:46 - Medical Education in the Age of AI 13:05 - Prompting AI: Best Practices for Clinicians 17:57 - The Role of Empathy and Judgment in Medicine 21:11 - AI in Complex Patient Conversations 26:16 - Future Directions in AI and Dermatology

A failed 1864 Union raid led by Dahlgren intended to burn Richmond and kill Confederate leaders, prompting a Confederate Secret Service response involving political subversion. Meanwhile, author Herman Melville embedded with Union cavalry, writing poetry about the terror of facing Mosby's elusive rangers in the "shadows."1865 FIVE FORKS

Headline: Fatal Shark Attacks and Catastrophic Heat Disrupt Australia Day Guest: Jeremy ZakisA tragic shark attack killed a 12-year-old boy near Sydney, prompting beach closures across the region. Simultaneously, a severe heatwave causing temperatures near 120°F has triggered total fire bans, cancelling Australia Day fireworks and barbecues. Bushfires threaten Victoria while a cyclone approaches Western Australia.1842

This week on the Builder Marketing Podcast, Greg and Kevin share effective AI prompting strategies to generate more accurate, relevant, and useful content specifically tailored for home builder marketers. https://www.buildermarketingpodcast.com/episodes/303-ai-prompting-strategies-for-home-builders-greg-bray-and-kevin-weitzel  

Relebogile Mabotja speaks to Tiffany Markman, Communication consultant, Writer and Speaker on how best to prompt AI platforms for maximum value. They touch on the need to edit the results to make them sound personal and like you. 702 Afternoons with Relebogile Mabotja is broadcast live on Johannesburg based talk radio station 702 every weekday afternoon. Relebogile brings a lighter touch to some of the issues of the day as well as a mix of lifestyle topics and a peak into the worlds of entertainment and leisure. Thank you for listening to a 702 Afternoons with Relebogile Mabotja podcast. Listen live on Primedia+ weekdays from 13:00 to 15:00 (SA Time) to Afternoons with Relebogile Mabotja broadcast on 702 https://buff.ly/gk3y0Kj For more from the show go to https://buff.ly/2qKsEfu or find all the catch-up podcasts here https://buff.ly/DTykncj Subscribe to the 702 Daily and Weekly Newsletters https://buff.ly/v5mfetc Follow us on social media: 702 on Facebook https://www.facebook.com/TalkRadio702 702 on TikTok: https://www.tiktok.com/@talkradio702 702 on Instagram: https://www.instagram.com/talkradio702/ 702 on X: https://x.com/Radio702 702 on YouTube: https://www.youtube.com/@radio702 See omnystudio.com/listener for privacy information.

Christian Steiger von Lexware zeigt, wie KI die Buchhaltung revolutioniert. Erfahre, warum „Prompting“ ein Auslaufmodell ist, wie das Lena-Prinzip Unternehmertum automatisiert und warum Software künftig zum strategischen KI Co-CEO wird. Ein tiefer Einblick in die Transformation für den deutschen Mittelstand.

It's frustrating when spend 3 hours writing one Instagram post that gets you 3 likes and a fire emoji from your best friend. While your colleague is posting twice daily, responding to all her DMs, AND still making it to yoga class. Here's what she knows, that you don't: It's not about WHETHER to use AI or not – it's HOW to use it without sounding like a robot. In this episode I'm going to share my framework that I use to cut content creation time in half while staying 100% authentic to my voice, and how AI is actually making my brain smarter, not lazier, and how it can help you too!Hot Topics from this episode:Why AI isn't making you lazy – it's actually training your brain to be more effectiveThe 3-part framework for using AI authentically: Training, Prompting, and AnalyzingHow to set up AI projects that understand YOUR voice, YOUR clients, and YOUR methodologyThe exact prompts I use to get client-centered language (not practitioner jargon)Why you should NEVER post the first draft (and what to look for when editing)How to cut content creation time dramatically while posting more consistentlyReal examples of how proper AI use can lead to more consults, email opens, and conversionsImportant Links:Follow me on Instagram →  igniteyourwellnessbusinessReady to work with me? Book a consultation call on my website!→ https://igniteurwellness.com/business-coach-for-health-coaches/Revenue Shift LIVE workshop: https://workshop-momentum.qwkcheckout.com/revenue-shift-checkoutJane's app: https://janesoftware.partnerlinks.io/Alison-mclean-podcastFor a free month use code: IGNITE1MO

In addition, NASA's Hubble Space Telescope is examining a newly discovered cloud of gas and dark matter that may represent a long-predicted but never-before-observed “failed” galaxy Learn more about your ad choices. Visit podcastchoices.com/adchoices

Guest: General Blaine Holt. The U.S. seized a Russian-flagged vessel, the "Bella 1," in the Atlantic, prompting a Russian submarine to track the operation. This Cold War-style confrontation highlights the risks of miscalculation as Washington enforces an energy quarantine against Venezuela, targeting shadow fleet vessels involved in illicit activities.

Guest: Jonathan Schanzer. Chaos persists in Syria with airstrikes against ISIS and factional fighting, prompting Israelisecurity concerns. In Gaza, Hamas refuses to disarm despite U.S. pressure and Israeli control over roughly half the territory, signaling a continuation of conflict rather than a ceasefire or reconstruction.1920 ALEPPO

01-06-26 - Mountain Lion Kills Hiker In Colorado Prompting Search And Our Curiosity Again At How Good We Must Taste - Flyers Play By Play Man Caught On Hot Mic Saying While You're Down ThereSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

01-06-26 - Mountain Lion Kills Hiker In Colorado Prompting Search And Our Curiosity Again At How Good We Must Taste - Flyers Play By Play Man Caught On Hot Mic Saying While You're Down ThereSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Drew and Rory stumble back from the holiday chaos—one fresh off vacation, the other barely resurrected from a mystery NYC illness.Between fever dreams and booger fingers, they somehow manage to tear into ChatGPT's Image 1.5 disappointment, expose why Nano Banana Pro is quietly dominating their workflows, and reveal the Weavy automation setup that's actually working (while FreePik continues its reign of mediocre terror). The duo gets brutally honest about why OpenAI feels like it's slipping, why negative prompting might be more important than what you actually want to create, and how to build your own custom AI tools in Google AI Studio without selling your soul to another subscription. Plus: vintage Kodak rally cars, the art of perfect thumbnails, coconut water in cocktails, and why their illness prevention protocols involve more vitamin C than common sense. If you survived their holiday absence and made it through the mandatory 20-minute ramble tax, you'll be rewarded with legitimate workflow gold that actually ships.---⏱️ Midjourney Fast Hour00:01 A Mr. Sniffles cold open05:18 Prompting while sick, then getting cooked on X07:35 An “Am I an AI artist?” reality check15:08 Moodboards, unsettling styles, and “what counts as art”27:39 Blade, Pluribus, and movie still inspiration sites31:42 Midjourney V8 quiet, Style Creator alpha changes37:45 The pace of releases and tool fatigue40:37 World models, Veo 3, and the next leap43:28 ChatGPT Image 1.5 talk and why it's still behind46:12 Nano Banana Pro flex, Freepik waits, and why it matters49:17 Weavy workflow walkthrough: from ref to shot list55:26 Contact sheets, “mini LoRA” vibes, and system rules59:14 Kling o1 keyframes: why 3–10 seconds is a cheat code01:03:32 Real text and brand risks in outputs01:06:52 Build your own Nano tool in Google AI Studio01:08:01 Writing models: ChatGPT vs Gemini vs Claude01:09:23 Negative prompting becomes the main event01:11:25 Wrap, thumbnails, and holiday chaos

12-15-25 - TMZ Reporting Billy Crystal And Larry David Seen At Rob Reiner Home Prompting Frank's Idea Of Detective Duo Jewman And ThriftySee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

12-15-25 - TMZ Reporting Billy Crystal And Larry David Seen At Rob Reiner Home Prompting Frank's Idea Of Detective Duo Jewman And ThriftySee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this GoodKind Podcast episode, Clayton, Amy, and Chris explore how artificial intelligence is quietly reshaping everyday life — from school and creativity to productivity and parenting. What begins as a practical conversation about using AI for efficiency quickly turns into a deeper discussion about discernment, formation, and what should (and should not) be offloaded to technology.The team unpacks what AI does well — summarizing information, organizing ideas, brainstorming, and speeding up tasks — while also naming its limitations, including its tendency to sound confident even when it's wrong. They discuss why better prompts matter, how AI can short-circuit learning if used too early, and why struggle and effort still play a critical role in creativity, wisdom, and growth.They also reflect on how parents are already navigating AI in schools, writing assignments, music, and communication — often faster than expected — and why modeling intentional use matters more than setting rigid rules. Throughout the conversation, they return to a central question: Which human is this replacing? — and how that question can guide healthier decisions around technology.If you've ever wondered whether using AI is making things easier at the cost of meaning… or how to integrate helpful tools without letting them become formative forces… this episode offers a thoughtful, grounded framework for using AI with clarity, boundaries, and purpose — especially in family life.You learn how AI works best as a support tool for information and efficiency, not a replacement for creativity or wisdom.Clear prompting leads to better results, while vague questions often produce shallow or incorrect output.AI excels at summarizing, brainstorming, and organizing information — but still requires discernment.Not everything should be offloaded; relationships, creativity, and formation matter too much.Overusing AI can weaken creative and learning muscles, especially for kids.Asking “Which human is this replacing?” helps clarify whether AI use is appropriate.Modeling intentional AI use shapes how children understand effort, learning, and meaning.00:00 Introduction to AI and Everyday Life02:41 What AI Is Good At (and What It's Not)05:28 Prompting, Accuracy, and Discernment08:47 AI, Creativity, and the Cost of Ease12:11 Parenting, School, and Early AI Exposure15:36 Which Human Is This Replacing?18:42 Modeling Healthy Technology Habits21:10 Final Thoughts and Key TakeawaysKeywords:artificial intelligence and families, Christian parenting and technology, AI and creativity, using AI responsibly, parenting in the age of AI, technology and formation, discernment with AI, raising kids with technology, meaningful learning vs convenienceTakeawaysChapters

Episode 73: Psst, content design is about to have a major momentIn 2025's final episode of Content Rookie, I introduce you to what I have coined the ”roguessance” of content design.I truly believe content design is about to have a major moment, thanks to developments like Google Disco redefining how we design and build experiences and interfaces as a whole. What are suddenly the most valuable skills on the market? Content strategy. Content systems. Clear comms. Prompting. Negotiating. Who does that best? Well, we content folks, of course!Why the latest developments in LLMs and AI are leveling the playing field for builders, writers, and designers, and how we can take ownership, are just some of the learning outcomes in this episode.Happy New Year, and thanks for listening to Content Rookie!Connect with the host, Nicole Michaelis:nicoletells.comhi@nicoletells.comhttps://www.linkedin.com/in/nicoletells/

Collab with Artlist and get 2 extra months for free here:https://artlist.io/artlist-70446?artlist_aid=the505podcast_2970&utm_source=affiliate_p&utm_medium=the505podcast_2970&utm_campaign=the505podcast_2970The 10 Minute Personal Brand Kickstart (FREE): https://the505podcast.courses/personalbrandkickstartWhat's up Rock Nation! Today we're joined by Jeff Su. He's an ex-Google employee, turned full-time creator and AI educator. Jeff helps solopreneurs and creators turn AI tools into real leverage, not just shortcuts.In this episode, Jeff shares why AI-native creators will outpace everyone in 2026, how to use AI to replace a 10-person content team, and why good prompts are built on systems, not templates. He also breaks down his repurposing workflow, the red team prompt strategy, and why AI won't replace you, but a smarter creator using AI will.Check out Jeff here:https://www.youtube.com/ ⁨@JeffSu⁩  https://www.instagram.com/j.sushie/SUSCRIBE TO OUR NEWSLETTER: https://the505podcast.ac-page.com/rock-reportKostas' Lightroom Presetshttps://www.kostasgarcia.com/store-1/p/kglightroompresetsgreeceCOP THE BFIGGY "ESSENTIALS" SFX PACK HERE: https://courses.the505podcast.com/BFIGGYSFXPACKTimestamps: 0:00 – Intro1:03 – How Creators Can Use AI as a Tool, Not a Threat2:53 – AI Isn't Replacing You—Bad Creators Are Replaceable4:16 – Why AI Content Won't Kill Human-Made Content5:12 – Using AI at Google vs. as a Creator6:49 – What Are Gemini Gems and How Do They Work?8:09 – ChatGPT vs Claude vs Gemini: Which AI for What Task?10:41 – Why Most People Should Start with ChatGPT12:03 – AI's Impact on Solo Creators and Business Scaling12:44 – The Smart Way to Create 50+ Podcast Clips a Month14:18 – Sponsored Segment: Artlist15:49 – The Biggest Trap Creators Fall Into with AI18:59 – A Hybrid Approach to AI Video Clipping20:32 – The 3 Levels of AI Fluency: Curious, Literate, Native22:19 – Why You Need to Use Text Expanders for Prompting23:18 – Text Expander Tools: Alfred, Raycast & More25:39 – Getting Better AI Results Starts with Better Prompts26:28 – Why Most People Never Advance with AI Tools28:57 – There's No AI Playbook (Yet)—And Why That Matters32:02 – Winning Skeptics Over to the Power of AI33:21 – Reverse Prompt Engineering Explained35:28 – Building a Prompt Database in Notion37:50 – Organizing Your AI Workflow Like a Pro39:21 – Jeff's Research Process Using ChatGPT & Notion41:25 – What is Red Teaming and How to Use It With AI43:12 – Behind Jeff's YouTube Workflow: From Idea to Upload46:02 – How AI Helps Explain Complex Concepts Clearly47:12 – What to Include in Your ChatGPT Custom Instructions50:02 – Evergreen vs. Limiting Custom Instructions50:58 – Why Custom Instructions Can Hurt More Than Help52:53 – Best Practices for Structuring Effective Prompts54:50 – How Prompting Is Like Excel Shortcuts for AI56:16 – Why You Need Battle-Tested Prompts for Your Workflow1:01:33 – Why Reverse Prompting Saves You Hours1:02:13 – Prompting with Hashtags & XML: Advanced Tips1:04:09 – Using AI to Improve Video Prompts for GenAI Tools1:07:05 – Notion Setup: Jeff's Full YouTube Content System1:10:05 – Using AI to Add Clarity Without Losing Personality1:11:33 – Avoid the “Curse of Knowledge” With AI Assistance1:13:40 – How Custom Instructions Shape AI Tone of Voice1:14:40 – Where Most People Go Wrong With Custom Instructions1:16:36 – How Overly Specific Instructions Pigeonhole AI1:17:46 – Bad vs. Good Examples of Custom Instructions1:19:19 – AI Bias: Why Tools May Overfit to Your Role1:20:06 – Best Custom Instructions for General Use1:26:06 – How AI Boosts Productivity Across Roles1:27:15 – Final Tips for Personalizing AI Assistants1:29:36 – Balancing Efficiency With Authenticity in Content1:32:19 – Post Pod DebriefIf you liked this episode please send it to a friend and take a screenshot for your story! And as always, we'd love to hear from you guys on what you'd like to hear us talk about or potential guests we should have on. DM US ON IG: (Our DM's are always open!) Bfiggy: https://www.instagram.com/bfiggy/ Kostas: https://www.instagram.com/kostasg95/ TikTok:Bfiggy: https://www.tiktok.com/bfiggy/ Kostas: https://www.tiktok.com/kostasgarcia/

Rising Tensions: Hezbollah's Rearmament and Hamas's Defiance: Colleague Jonathan Schanzer warns that Hezbollah has rebuilt its strength in Lebanon using Iranian weapons, prompting Israeli threats of a full-scale attack, noting that Hamas refuses to disarm in Gaza, supported by Turkey and Qatar, while the U.S. moves to designate Muslim Brotherhood branches as terrorist organizations. 1953

Get the free NotebookLM guide: https://clickhubspot.com/wph Episode 87: Can AI know you well enough to create audio content tailored to your interests—without you ever prompting it? Nathan Lands (https://x.com/NathanLands) is joined by Raiza Martin (https://x.com/raizamrtn), one of the creators behind Google's NotebookLM and now co-founder of Huxe, a new startup carving the path toward truly personal AI. Raiza shares her unconventional journey from leading innovation at Google to building Huxe, an app that generates personalized audio (think “AI radio made just for you”) driven by your daily habits and interests—without you having to initiate the conversation. The episode dives into what it means to design AI for real human needs, the balancing act of privacy and helpfulness, and the vision for a future where your AI assistant proactively enhances your everyday life. They also discuss where AI products are succeeding, how creating trust is an overlooked challenge, and why the next breakthrough will be more about fitting seamlessly into our routines than about being “smarter.” Check out The Next Wave YouTube Channel if you want to see Matt and Nathan on screen: https://lnk.to/thenextwavepd — Show Notes: (00:00) From NotebookLM to Huxe (04:29) Finding AI's Killer Use Case (06:33) Users' Unique Huxe Usage Insights (12:24) Proactive AI: A New Approach (16:02) Daily Brief Insights (17:21) VCs Monitoring Funding Opportunities (21:33) ChatGPT's Role in Everyday AI (24:43) App Adoption and Design Insights (29:20) Uniquely Human Skills Matter (31:11) Modern Childhood and Connection (33:27) Understanding Tech Basics for Kids — Mentions: Raiza Martin: https://www.linkedin.com/in/whatsaraiza Huxe: https://www.huxe.com/ NotebookLM: https://notebooklm.google/ ChatGPT Pulse: https://openai.com/index/introducing-chatgpt-pulse/ Factory: https://factory.ai/ Get the guide to build your own Custom GPT: https://clickhubspot.com/tnw — Check Out Matt's Stuff: • Future Tools - https://futuretools.beehiiv.com/ • Blog - https://www.mattwolfe.com/ • YouTube- https://www.youtube.com/@mreflow — Check Out Nathan's Stuff: Newsletter: https://news.lore.com/ Blog - https://lore.com/ The Next Wave is a HubSpot Original Podcast // Brought to you by Hubspot Media // Production by Darren Clarke // Editing by Ezra Bakker Trupiano

11-24-25 - Michael Bidwill Spotted At Ra Ra Room Before End Of Overtime Cardinals Loss Prompting John To Think He Must Be Meeting w/Someone To Sell The TeamSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Europe's military buildup, presidential use of the National Guard, and a “gospel-first” approach to sidewalk counseling. Plus, Janie B. Cheaney on the cost of scoffing, bringing site to the blind, and the Tuesday morning newsSupport The World and Everything in It today at wng.org/donateAdditional support comes from Cedarville University—a Christ-centered, academically rigorous university located in southwest Ohio, equipping students for Gospel impact across every career and calling. Cedarville integrates a biblical worldview into every course in the more than 175 undergraduate and graduate programs students choose from. New online undergraduate degrees through Cedarville Online offer flexible and affordable education grounded in a strong Christian community that fosters both faith and learning. Learn more at cedarville.edu, and explore online programs at cedarville.edu/onlineFrom The Issues, et cetera podcast. Expert guests, Expansive topics, Extolling Christ. More at issuesetc.orgAnd from Asbury University — where students are known, supported, and prepared to lead. Customized visits available. asbury.edu/visit