Podcasts about Getting Started

If your ADHD brain struggles with procrastination, or you often find yourself thinking, "Why can't I just do this thing?!" episode 344 is for you. We often think "getting started" is one simple action. But for the ADHD brain, task initiation is actually a complex chain of 5-6 distinct steps, each of which demand our executive functions. When we collapse these steps all into one, we miss the nuance of where the breakdown happens and we default to shame instead of solutions. In this episode, we're taking apart the "I'm just a procrastinator" narrative. You'll learn how to identify which of the 6 stages is actually blocking you from starting and find one small, specific way to make it remove the friction and take the next step. In episode 344, you will discover: The 6 hidden layers of task initiation (and why 'just doing it' is biologically harder for us). How to pinpoint exactly which step has you stuck. One specific micro-step to remove the friction from the step that's holding you back. Work With Me:

Send a textShould your team be selling merchandise on Amazon?In this episode, Jeremy breaks down the real strategic implications of adding Amazon as a sales channel — from margin math and SEO strategy to customer data ownership and cannibalization risk. If you're responsible for revenue, merchandise, or digital marketing, this is your practical roadmap before you jump in.Key Topics CoveredWhy Amazon is more search engine than storefront — and why that mattersThe real math behind Amazon's 15% referral feeFBA vs. FBM: Fulfillment by Amazon vs. Merchant fulfillmentThe hidden cost of losing first-party customer dataWhy you should never push your fans from Shopify to AmazonHow Amazon SEO works (and why semantic SEO matters)Why city/state-forward merchandise should launch before team-branded itemsHow to prevent Shopify cannibalizationPricing strategy: Why you may want to charge more on AmazonUsing Amazon strictly as an acquisition channelConnecting Shopify to Amazon with Marketplace ConnectModeling margin before listing a single productChapters00:00 Introduction to Selling Merchandise on Amazon 01:59 Why Amazon Is a Powerful Sales Channel 03:48 Revenue Potential During Peak Seasons 05:42 Fulfillment Options: FBA vs FBM 07:08 Understanding Amazon Fees and Margins 08:32 Customer Data Ownership and Marketing Challenges 10:54 The Importance of SEO and Search Demand 13:14 Keyword Strategies and Search Terms 14:58 Starting with City and State Apparel 18:23 Semantic SEO and Listing Optimization 20:12 Connecting Shopify and Amazon 21:32 Getting Started and Learning the Platform 22:29 Pricing, Margins, and Protecting Your Brand 23:25 Strategies to Increase Sales and Customer Lifetime Value 24:46 Balancing Amazon and Shopify for Growth 26:10 Next Steps and Deeper ConversationsCore Strategic Takeaways1. Amazon is an acquisition engine — not a loyalty platform. You will gain reach. You will gain visibility. But Amazon owns the customer relationship — not you.2. Start broad before going branded. City-forward, state-pride, and general baseball apparel can build search velocity and reviews before you launch deeper team SKUs.3. SEO is the real game. Amazon rankings are driven by relevance + performance + conversion velocity. Without visibility, there are no sales.4. Model your numbers before you move inventory. Understand your true profit after fees. Align pricing carefully. Consider charging slightly more on Amazon to protect margin.Resources MentionedShopify Marketplace ConnectMarketplace Connect TipsFulfillment by Merchant Overview & Referral FeesAmazon Seller CentralAmazon Seller UniversitySemantic SEO research toolsJungle ScoutHelium 10Sports Marketing Machine on LinkedInSports Marketing Machine on InstagramBook a call with Jeremy from Sports Marketing Machine

Send a textDiscover how to build a thriving coaching program from the ground up. Stacey shares her journey of developing “Mirror Love,” a program for women seeking self-love and empowerment. Learn essential steps for launching your coaching business. This episode provides a clear roadmap for aspiring coaches. Learn how to define your niche, create compelling course content, and strategize your launch. Stacey and Joel discuss the importance of a strong framework and practical steps to build your coaching program. 00:00 Stacy's Coaching Program: Mirror Love 04:03 Identifying the Target Audience 06:12 The Marketing Pitch and Sales Fractals 10:27 Book vs. Coaching: Community Value 14:52 Developing a Coaching Framework 17:19 Stacy's Seven Steps to Self-Love 22:21 Pricing and Proof of Concept Ready to launch your own coaching program? Visit joelmalm.com/coaching to share your story and get personalized guidance. #CoachingBusiness #OnlineCourse #SelfLoveCoach #Entrepreneurship #CoachingTips

Silver Wars Appear To Just Be Getting Started With governments chest thumping their strategic metal stockpiles, while investment into the mining sector is starting to ramp into overdrive, we're entering historic times in the silver market (and a handful of other metals for that matter). But with the price going up to $121, then down to $64, and then back up to $90, what is this all going to look like in a few months or years? Michael McNair joins me again on the show for the final portion of his interview, where he explains what MUST happen to the U.S. strategic metals, as the reshoring of manufacturing starts to get underway. So to be prepared before the rest of the investment world, click to watch this video now! - To follow Michael McNair on twitter go to: https://x.com/michaeljmcnair - To find out more about First Majestic Silver's record 4th quarter earnings go to: https://www.firstmajestic.com/investors/news-releases/first-majestic-reports-q4-2025-and-full-year-2025-financial-results-announces-quarterly-dividend-payment - Get access to Arcadia's Daily Gold and Silver updates here: https://goldandsilverdaily.substack.com/ - Join our free email list to be notified when a new video comes out: click here: https://arcadiaeconomics.com/email-signup/ - Follow Arcadia Economics on twitter at: https://x.com/ArcadiaEconomic - To get your copy of 'The Big Silver Short' (paperback or audio) go to: https://arcadiaeconomics.com/thebigsilvershort/ - #silver #silverprice #gold And remember to get outside and have some fun every once in a while!:) (URL0VD) This video was sponsored by First Majestic Silver, and Arcadia Economics does receive compensation. For our full disclaimer go to: https://arcadiaeconomics.com/disclaimer-first-majestic-silver/Subscribe to Arcadia Economics on Soundwise

This is episode 4 of 5 in the Productivity with AI Reset Series. In this series, we are resetting our productivity for an AI World, realigning how to do business as an online entrepreneur so we can take advantage of the greatest technology transformation we have ever seen.Monetization is not the starting line of online business success. The making money part is not where you begin to be successful. Monetization is a signal. A signal that you are solving a real problem for real people.  All the AI in the world will not help you, if you are not delivering a solution.When the money does not show up immediately, many aspiring entrepreneurs assume their idea is bad and they did everything wrong. But the real issue is not idea, effort, intelligence or likability. It is sequence.  In this episode, Case discusses how money does not respond to hustle. Money responds to value delivered consistently. And if you understand the relationship — and learn how monetization naturally emerges when you stop chasing income and start building relevance.  You are on your way to wealth. Action Plan: Simplify your monetization path through: Education Value CommunityTo have an enjoyable life  in our global, advanced tech society, create value.  To have the business, career, finances and lifestyle you desire, follow a proven path that has delivered in good times and bad.  The path of entrepreneurship. And online entrepreneurship is the fast track for aspiring entrepreneurs.Learn the skills, access the resources and be inspired to live the life of your dreams right here on the Ready Entrepreneur podcastTo find more resources, strategies and ideas for aspiring entrepreneurs visit the Ready Entrepreneur website: https://www.readyentrepreneur.com/To download a free guide for Preparing to Become an Online Entrepreneur, click here:  https://www.readyentrepreneur.com/start/You can get an exclusive discount on the ebook and audiobook version of Recast: The Aspiring Entrepreneur's Practical Guide to Getting Started with an Online Business click here: https://www.caselane.net/recastConnect with CaseFacebook: @readyentrepreneurHQ Instagram: @readyentrepreneur Twitter X: @caselaneworld Pinterest @caselane 

Another day, another batch of headlines that made us raise an eyebrow. We talked about what's happening, what's not being said, and what probably needed to be said louder.

Another day, another batch of headlines that made us raise an eyebrow. We talked about what's happening, what's not being said, and what probably needed to be said louder.

Feb 24, 2026 – What if the massive disruption we've seen in the stock market from AI is just the beginning of a much deeper transformation? In this compelling conversation, Cris Sheridan sits down with strategist Robert Van Battenburg...

This week we start with Jason's follow up to Ring launching its ‘Search Party' feature. It turns out, according to a leaked email he got, the feature is only starting with finding lost dogs. After the break, Emanuel explains why we've learned nothing about amplification when it comes to the recent looksmaxxing trend. In the subscribers-only section, Sam explains how Grok produced the real name of a sex worker who performs pseudonymously. 1:11 - Leaked Email Suggests Ring Plans to Expand ‘Search Party' Surveillance Beyond Dogs⁠ 30:26 - ⁠We Have Learned Nothing About Amplifying Morons⁠ ⁠Grok Exposed a Porn Performer's Legal Name and Birthdate—Without Even Being Asked⁠ YouTube version: https://youtu.be/IEq8dlnLP8o Learn more about your ad choices. Visit megaphone.fm/adchoices

Send a textBefore you invest in liquid, labels, or a launch strategy, the most important question any founder can ask is: does this brand actually have a viable path to market?This Expert Talk features Felipe Gonzalez-Gordon, Partner and COO of Colangelo & Partners, delivering a data-driven framework for launching a spirit, wine, or RTD brand in today's saturated market, drawn from his presentation at Bar Convent Brooklyn 2025. Felipe walks through the financial and strategic groundwork that separates brands that scale from those that stall: how to size your addressable market realistically, how to model capital requirements, and how to allocate marketing spend without burning through runway prematurely. The episode also tackles brand positioning with precision, examining how founders can identify and defend a differentiated position on the shelf in a category where retailer fatigue and distributor consolidation make first impressions increasingly important. Featured Guests:Felipe Gonzalez-Gordon, Partner & COO, Colangelo & PartnersMentioned in this episode:Colangelo & PartnersWatch on YouTube: How to Launch a Beverage Alcohol Brand: A Data-Driven FrameworkWant to stay in the know about new episodes from the podcast? Fill out the form below: https://share.hsforms.com/1MEb-81x2TXi3f15qO_yEpA4tip1Learn More About Park StreetSign up for our Daily Industry Newsletter.Sign Up for our Monthly Newsletter.Check out Park Street's Guide to Getting Started in the U.S. MarketFollow us for more industry insights onLinkedIn FacebookTwitterInstagram

This week's episode is split into two parts.The first was recorded live at Top Drawer back in January, where we sat down with Hannah Adams to talk about AI and what it really means for buyers and brands.The second dives into Hannah's career journey from buyer to business transformation specialist and what that evolution teaches us about modern retail careers.Part One: Live from Top Drawer - AI in RetailRecording live brought such energy to this conversation. We wanted to strip AI back to what actually matters in day-to-day retail roles.Hannah shares her experience at ASOS, where she worked on implementing Microsoft Copilot, and breaks down how tools like ChatGPT, Claude and Gemini can support buyers in practical ways.We cover real use cases, competitor analysis, trend research, product development ideas, supplier emails, negotiation prep and contract reviews as well as the importance of strong prompting and protecting sensitive business data.The key message? AI is an assistant, not a replacement. Your judgement and instinct still matter most- AI simply helps you move faster and think wider.Part Two: Hannah's Career PivotHannah started her career at Evans within Arcadia Group before moving into buying roles at Topman during the High Street's early 2000s peak.She shares the reality of international supplier travel- from Taiwan to Mauritius and the buzz of developing product and trading weekly sales.Her move into business transformation was driven by both lifestyle considerations and curiosity. She explains how transformation roles act as the bridge between technical teams and commercial teams, helping retailers adopt new systems in ways that genuinely work for buyers and merchandisers.Her journey is a brilliant example of how transferable buying skills really are — negotiation, stakeholder management and commercial thinking open far more doors than many realise.We finish by reflecting on what we all still love about buying: the thrill of sales, backing your instinct, building supplier relationships and seeing product come to life.Three Takeaways1) AI won't replace buyers - but buyers who use AI well will have an advantage.2) Buying skills are incredibly transferable. Retail careers don't have to be linear to be successful.3) Whether it's new technology or a new role, growth comes from experimenting, staying curious and backing yourself.Let us know what resonates most and whether you've started exploring AI in your own role yet.Support the showIf you've liked this episode please rate, follow, subscribe and share :) - and if you already have, thank you!Follow us @buyingandbeyond on Instagram Send us a DM or email hello@buyingandbeyond.co.uk Find out more about us www.buyingandbeyond.co.uk If you'd like to show a little more love, then head here to give us just a little bit *extra* and show us your support :) thank you! https://www.buzzsprout.com/2300060/support

Chris Van Vliet: Stop Doing This If You Want to Succeed | The Hopeaholics PodcastIn this episode of the The Hopeaholics Podcast, we sit down with Chris Van Vliet to talk about the mindset, discipline, and persistence it takes to build a successful career in media and content creation. Chris shares his journey from getting started in broadcasting to landing major opportunities, navigating career setbacks, and ultimately creating one of the most recognized interview platforms in his space. The conversation dives into confidence on camera, personal branding, consistency, taking professional risks, and the habits that separate people who succeed from those who quit. Whether you're a creator, entrepreneur, or someone chasing a big goal, this episode is packed with practical insights, motivation, and real-world lessons on resilience, growth, and defining success on your own terms.Check out Chris Van Vliet's Episode with Natalie:https://www.youtube.com/watch?v=P6mq0W7yAVA#thehopeaholics  #redemption #recovery #AlcoholAddiction #AddictionRecovery #wedorecover #SobrietyJourney #MyStory #Hope #wedorecover #treatmentcenter #natalieevamarieJoin our patreon to get access to an EXTRA EPISODE every week of ‘Off the Record', exclusive content, a thriving recovery community, and opportunities to be featured on the podcast. https://patreon.com/TheHopeaholics Go to www.Wolfpak.com today and support our sponsors. Don't forget to use code: HOPEAHOLICSPODCAST for 10% off!Follow the Hopeaholics on our Socials:https://www.instagram.com/thehopeaholics https://linktr.ee/thehopeaholicsBuy Merch: https://thehopeaholics.myshopify.comVisit our Treatment Centers: https://www.hopebythesea.comIf you or a loved one needs help, please call or text 949-615-8588. We have the resources to treat mental health and addiction. Sponsored by the Infiniti Group LLC:https://www.infinitigroupllc.com Timestamps:00:01:42 — Getting Started in Broadcasting00:05:18 — The First Big Career Break00:09:47 — Learning Confidence on Camera00:14:03 — Moving Cities for Opportunity00:18:29 — Early Career Struggles00:23:11 — Interviewing Celebrities for the First Time00:28:56 — Building a Personal Brand00:34:40 — Taking Risks Professionally00:40:12 — Mindset and Self-Improvement00:47:05 — Transitioning Into Podcasting00:53:27 — Consistency and Discipline01:00:44 — Career Setbacks and Resilience01:08:19 — Advice for Content Creators01:15:33 — Defining Success Personally01:23:58 — Gratitude and Perspective

Welcome to the world of Magic story! In the spirit of our Magic 101 series, this week we're giving listeners an updated basics of Magic lore, including understanding what the multiverse is, the planeswalkers who inhabit it, and what happened during the Mending and Spark Rupture. If you're looking to learn more about this wonderful fantasy setting, this is your place to start. MTGStory.com MTGLore.com MTG.Wiki If you'd like to support the show, you can find us on Patreon at Patreon.com/TheVorthosCast!

OCaml – уникальный язык и по своему историческому значению, и по фичам. Он сильно повлиял практически на все современные языки, на нем до сих пор написаны многие из их компиляторов, и одновременно с этим он считается идеальным входом для новичков в мир функционального программирования. А погружаемся в этот язык мы вместе с Павлом Аргентовым, программистом из Evrone, который страстно любит OCaml и пишет на нем очень много кода. Также ждем вас, ваши лайки, репосты и комменты в мессенджерах и соцсетях!
 Telegram-чат: https://t.me/podlodka Telegram-канал: https://t.me/podlodkanews Страница в Facebook: www.facebook.com/podlodkacast/ Twitter-аккаунт: https://twitter.com/PodcastPodlodka Ведущие в выпуске: Евгений Кателла, Егор Толстой Полезные ссылки: Официальные ресурсы Документация и туториалы: OCaml.org — официальный сайт. Getting Started, документация, packages https://ocaml.org/ OCaml Manual — полная справка по языку. Формальная семантика, все языковые конструкции https://ocaml.org/manual/ Real World OCaml — практическая книга (2nd Edition).Jane Street, Yaron Minsky, Anil Madhavapeddy https://dev.realworldocaml.org/ CS3110: Data Structures and Functional Programming (Cornell). Лучший образовательный ресурс для начинающих https://cs3110.github.io/textbook/ Инструменты: OPAM — package manager. 4,600+ packages https://opam.ocaml.org/ Dune — build system. Композируемая, быстрая система сборки https://dune.build/ Merlin — IDE support (LSP). Автодополнение, type information, jump to definition https://github.com/ocaml/merlin OCamlFormat — code formatter. Opinionated formatting https://github.com/ocaml-ppx/ocamlformat Полный список ссылок на странице выпуска https://podlodka.io/465

"Join Tina Tran in this video as she breaks down how to start a successful rental business in 2026. From purchasing a vehicle to hiring employees and securing storage, she covers the three key operational pillars so you can start smart, stay lean, and avoid costly mistakes as you grow."Download The Ultimate Checklist for Free:

Two hundred episodes.Six years of conversations.Hundreds of hours recorded.Countless guests.Countless stories.Episode 200 is more than a milestone. It is a reset.In this episode, I reflect briefly on the journey so far and introduce the next evolution of PaddyTalks Golf.The show now moves forward with four clear lanes:

Tax refund season brings opportunity. And for financial coaches, it also brings temptation. When a client receives a large windfall — $3,000, $7,000, even $20,000 — it's easy to get excited about the progress they could make. Debt payoff. Emergency funds. Investments. Big wins. But sometimes in that excitement, we forget to pause and ask: What do they want? In this episode, Cody and Maria unpack a subtle but important coaching lesson — one that often surfaces during tax season: Why coaches can unintentionally overstep when windfalls arrive How goal-chasing can override client ownership The importance of asking before advising Why splitting a refund may increase long-term engagement How mature coaches measure success differently This conversation isn't really about tax refunds. It's about leadership.It's about restraint.It's about remembering that great coaching helps clients make confident decisions — not just efficient ones. Because it's not about what's mathematically optimal. It's about what they're ready to own.

Just Getting Started - Part 1 - What if... by

Send a textBriz joins the show to talk about his experience in developing a fully realized 3D video game starting from zero and he and Trav reflect on a few of the horror games they've played together on the couch.Alex will be back soon!Briz on YouTube and BlueSkySupport the show Find links for all things network related here: https://linktr.ee/polymedianetwork Find Travis on BlueSky Find Alex on BlueSky Send us an email drunkfriendpodcast@gmail.com Visit our Subreddit reddit.com/r/polymedia

Why Metals War With China Is Just Getting Started... If you think we've seen the U.S. and China duking it out over the past year, Vince Lanci explains why it's still just getting started. And why the metals are at the heart of it. So to find out more, click to watch this video now! - To find out more about the latest production numbers from Dolly Varden silver, go to: https://dollyvardensilver.com/dolly-varden-silver-intersects-4-66-g-t-gold-over-48-49-meters-including-52-15-g-t-gold-and-306-g-t-silver-over-1-01-meters-at-homestake-silver-deposit/ - Get access to Arcadia's Daily Gold and Silver updates here: https://goldandsilverdaily.substack.com/ - Join our free email list to be notified when a new video comes out: click here: https://arcadiaeconomics.com/email-signup/ - Follow Arcadia Economics on twitter at: https://x.com/ArcadiaEconomic - To get your copy of 'The Big Silver Short' (paperback or audio) go to: https://arcadiaeconomics.com/thebigsilvershort/ - #silver #silverprice #gold And remember to get outside and have some fun every once in a while!:) (URL0VD) This video was sponsored by Dolly Varden Silver and Arcadia Economics does receive compensation. For our full disclaimer go to: https://arcadiaeconomics.com/disclaimer-dolly-varden-2025/Subscribe to Arcadia Economics on Soundwise

AI Agents. AI Agents everywhere.

View This Week's Show NotesStart Your 7-Day Trial to Mobility CoachJoin Our Free Weekly Newsletter: The AmbushIn a world obsessed with “optimal” routines, Dr. Rachel Pojednic cuts through the noise with a grounded, evidence-based approach to longevity and performance. This conversation is a reset for anyone overwhelmed by conflicting health advice, anxious about wearable scores, or stuck chasing perfect protocols that collapse under real life stress.You'll learn what the science actually supports, what's still uncertain, and how to build a simple, sustainable health strategy using the biggest levers first—movement, nutrition, sleep, stress, and relationships—before you bother with the “fun stuff.” Dr. Pojednic also shares what she's learned studying wellness therapies in industry and academia, why most people misunderstand Zone 2, and what to track if you want a clearer picture of your health over time.What You'll Learn in This EpisodeWhy “protocol life” is making people more confused (and often less healthy)The difference between big levers (high impact) and little levers (fine-tuning) for longevityWhat to track that's actually useful: A1C trends, fasting glucose, lipids, resting heart rateWhy wearable metrics can conflict—and how that can create anxiety and false certaintyA clearer, non-hype explanation of HRV and why “low” isn't always “bad”What Zone 2 is really for (and why it isn't a magical mitochondrial hack)How to think about supplement safety, including third-party testing and the “lead in protein powder” scareA simple 7–30 day “one change” experiment to build habits that survive real lifeIf you've ever felt like you're “failing” health because you can't follow a perfect routine—or you've been pulled in six directions by influencers, devices, and contradictory advice—this episode gives you something rare: a sane framework. You'll walk away with fewer rules, better priorities, and a practical way to measure progress that doesn't depend on hype, fear, or the latest trend.Chapters(00:00) - Intro(01:39) - The Problem with Protocols(05:29) - Rachele's Backstory and Research Journey(13:06) - Rachele's Research Focus(18:45) - Sponsor: Vitality Blueprint(20:40) - Science Communication and Social Media(23:24) - Getting Started in Science Communication(25:10) - Bridging Research and Real-World Applications(29:35) - New Lane for Performance Therapy(31:05) - Key Metrics to Track(32:07) - Importance of Observable, Measurable Data(34:34) - Need for Common Diagnostic Suite(40:19) - Current State of Healthcare and EHRs(42:32) - Momentous: Protein Powder Insights(44:44) - Subscribe to This Podcast(46:30) - Basics We Can All Agree On(47:10) - Regular Tracking Essentials(53:10) - Heart Rate Variability (HRV)(54:42) - Wearables and Big Games(57:06) - Desire to Train(59:28) - Big Opportunity and Challenges(1:00:30) - Rapid Fire: Zone 2(1:03:02) - LMNT: Try a Personal Experiment(1:06:58) - Your Micro-Experiment(1:10:34) - Rachele's “Infinite Shelf” Recommendation(1:14:55) - Join The Starrett SystemWebsite | Instagram | Facebook | YouTubeCheck our Dr. Rachele's courses at Strong ProcessHuge thanks to our sponsors, Vitality, Momentous, and LMNT.

This is episode 3 of 5 in the Productivity with AI Reset Series. In this series, we are resetting our productivity for an AI World, realigning how to do business as an online entrepreneur so we can take advantage of the greatest technology transformation we have ever seen.One action is to change our understanding of how productivity is achieved. Willpower is not a foundation for productivity. It is too unpredictable and can collapse at moments when life pulls you along.  And anyway, consistency is not about pushing harder — it is about designing better systems.In this episode, Case discusses how to build momentum that lasts, without relying on motivation, willpower, or hustle culture.  We use Momentum.  Momentum is the quiet force that makes progress feel natural.  But momentum is not something you feel, it is something you build, which means you can use AI to propel you along.  Your Action Plan: Define your Minimum Viable Day Implement a Weekly Momentum Review To have an enjoyable life  in our global, advanced tech society, create value.  To have the business, career, finances and lifestyle you desire, follow a proven path that has delivered in good times and bad.  The path of entrepreneurship. And online entrepreneurship is the fast track for aspiring entrepreneurs.Learn the skills, access the resources and be inspired to live the life of your dreams right here on the Ready Entrepreneur podcastTo find more resources, strategies and ideas for aspiring entrepreneurs visit the Ready Entrepreneur website: https://www.readyentrepreneur.com/To download a free guide for Preparing to Become an Online Entrepreneur, click here:  https://www.readyentrepreneur.com/start/You can get an exclusive discount on the ebook and audiobook version of Recast: The Aspiring Entrepreneur's Practical Guide to Getting Started with an Online Business click here: https://www.caselane.net/recastConnect with CaseFacebook: @readyentrepreneurHQ Instagram: @readyentrepreneur Twitter X: @caselaneworld Pinterest @caselane 

What if you're not stuck because you don't know enough, but because you think you should know everything before you begin? If you've ever felt overwhelmed by where to start, unsure how to market yourself, or quietly battling imposter syndrome, this episode is for you. We're exploring why so many singers hesitate to step into teaching — and how a simple 5-Day Challenge might be the reset that brings clarity, confidence, and momentum. Could five focused days change everything? WHAT'S IN THIS PODCAST? 0:52 The things that overwhelm us in singing teaching and business 7:42 Pushing the ‘reset' button 11:47 What is a 5-day challenge?  16:27 Can I do it again if I've already done one? 17:11 Why a 5-day challenge helps singing teachers 20:14 What shift might you notice from doing a 5-day challenge?  21:44 The 5-day challenge special offer 22:30 The next BAST Training 5-day challenge   About the presenter HERERELEVANT MENTIONS & LINKS   Line HiltonThe StageSinging Teachers Talk - Ep.192 How to Set Boundaries as a Singing Teacher Singing Teachers Talk - Ep. 239 10 Common Barriers to Being a Singing Teacher and How to Overcome Them Insight TimerThe Artist's Way by Julia Cameron Phil HarrisonThe Business of Stories by Susan Payton Singing Teachers Talk - Ep.126 ‘The Business of Stories' - Game-Changing Marketing Strategy Kaya Herstad-Carney REGISTER FOR THE BAST TRAINING 5-DAY CHALLENGE, HERE 

Today we're talking about how virtual assistants get the best, long-term, high-quality clients. And keep them. Getting hired is one thing. Getting kept on long-term is another. Because there are a lot of virtual assistants who can land a client. But far fewer who keep that client long term, grow the relationship, and become the VA the client never wants to lose. That's what we want. High quality clients that we can work with for a long time. What is different about the VAs that the clients never want to lose? They're not necessarily more talented. They don't know more tools or stuff.  They don't work more hours. The difference is how they show up once the contract is signed. So today I want to walk you through the difference between a VA who gets hired and a VA who gets kept long-term, and more importantly, how you can start positioning yourself as the second one. Long-term clients don't happen by accident. They happen when you build your VA business with structure, confidence, and clarity from the beginning. That's one of the reasons I created my Getting Started as a Virtual Assistant self-study program. It walks you through how to set up your services properly, position yourself professionally, and attract the kinds of clients you actually want to work with. Not just whoever says yes first, as tempting as that may be! Whether you want to get started the right way, or stop all the guessing and uncertainty and start building your VA business the right way, check it out. Check out the program here: https://getstartedva.com Start strong. Build smart. And set yourself up to work with clients who stay. That's all I've got for you this week. I'm Tracey DAviero, The Confidence Coach for Virtual Assistants. Thanks so much for tuning in. I'll see you next time!

Dave says there are three reasons why started a new walking habit can be a struggle. During today's ten-minute walk, he offers five suggestions.For additional motivation, sign up to get Dave's FREE weekly email. He send it out early on Thursday morning.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

If you've ever wondered how Oracle Database really works inside AWS, this episode will finally turn the lights on.   Join Senior Principal OCI Instructor Susan Jang as she explains the two database services available (Exadata Database Service and Autonomous Database), how Oracle and AWS share responsibilities behind the scenes, and which essential tasks still land on your plate after deployment.   You'll discover how automation, scaling, and security actually work, and which model best fits your needs, whether you want hands-off simplicity or deeper control.   Oracle Database@AWS Architect Professional: https://mylearn.oracle.com/ou/course/oracle-databaseaws-architect-professional/155574 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, Anna Hulkower, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   ------------------------------------------------------------   Episode Transcript:   00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:26   Lois: Hello and welcome to the Oracle University Podcast! I'm Lois Houston, Director of Communications and Adoption with Customer Success Services, and with me is Nikita Abraham, Team Lead: Editorial Services with Oracle University.  Nikita: Hi everyone! In our last episode, we began the discussion on Oracle Database@AWS. Today, we're diving deeper into the database services that are available in this environment. Susan Jang, our Senior Principal OCI Instructor, joins us once again.  00:56 Lois: Hi Susan! Thanks for being here today. In our last conversation, we compared Oracle Autonomous Database and Exadata Database Service. Can you elaborate on the fundamental differences between these two services?     Susan: Now, the primary difference is between the service is really the management model. The Autonomous is fully-managed by Oracle, while the Exadata provides flexibility for you to have the ability to customize your database environment while still having the infrastructure be managed by Oracle.   01:30 Nikita: When it comes to running Oracle Database@AWS, how do Oracle and AWS each chip in? Could you break down what each provider is responsible for in this setup?  Susan: Oracle Database@AWS is a collaboration between Oracle, as well as AWS. It allows the customer to deploy and run Oracle Database services, including the Oracle Autonomous Database and the Oracle Exadata Database Service directly in AWS data centers.   Oracle provides the ability of having the Oracle Exadata Database Service on a dedicated infrastructure. This service delivers full capabilities of Oracle Exadata Database on the Oracle Exadata hardware. It offers high performance and high security for demanding workloads. It has cloud automation, resource scaling, and performance optimization to simplify the management of the service.  Oracle Autonomous Database on the dedicated Exadata infrastructure provides a fully Autonomous Database on this dedicated infrastructure within AWS. It automates the database management tasks, including patching, backups, as well as tuning, and have built-in AI capabilities for developing AI-powered applications and interacting with data using natural language. The Oracle Database@AWS integrates those core database services with various AWS services for a comprehensive unified experience.  AWS provides the ability of having a cloud-based object storage, and that would be the Amazon S3. You also have the ability to have other services, such as the Amazon CloudWatch. It monitors the database metrics, as well as performance. You also have Amazon Bedrock. It provides a development environment for a generative AI application.   And last but not the least, amongst the many other services, you also have the SageMaker. This is a cloud-based platform for development of machine learning models, a wonderful integration with our AI application development needs.  03:54 Lois: How has the work involved in setting up and managing databases changed over time?  Susan: When we take a look at the evolution of how things have changed through the years in our systems, we realize that transfer responsibility has now been migrated more from customer or human interaction to services. As the database technology evolves from the traditional on-premise system to the Exadata engineered system, and finally to the Autonomous Database, certain services previously requiring significant manual intervention has become increasingly automated, as well as optimized.  04:34 Lois: How so?  Susan: When we take a look at the more traditional database environment, it requires manual configuration of hardware, operating system, as well as the software of the database, along with initial database creation. As we evolve into the Exadata environment, the Exadata Database, specifically the Exadata cloud service, simplifies provisioning through web-based wizard, making it faster and easier to deploy the Oracle Database in an optimized hardware.     But when we move it to an Autonomous environment, it automates the entire provisioning process, allowing users to rapidly deploy mission-critical databases without manual intervention, or DBA involvement. So as customers move toward Autonomous Database through Exadata, we have fewer components that the customer needs to manage in the database stack, which gives them more time to focus more on important parts of the business.  With the Exadata Database, it provides a co-management of backup, restore, patches and upgrade, monitoring, and tuning. And it allows the administrator the ability to customize the configuration to meet their very specific business needs. With Autonomous Database, it's now fully automated and it's a greater responsibility is shift toward the service. With Autonomous Database on dedicated infrastructure, it provides that fine-grained tuning more for Oracle to help you perform that task.  06:15 Nikita: If we narrow it down just to Oracle and AWS for a moment, which parts of the infrastructure or day-to-day ops are handled by each company behind the scenes?  Susan: When we take a look at Oracle Database@AWS, it operates under a shared responsibility model, dividing the service responsibilities between AWS, as well as Oracle, as well as you, the customer.   The AWS has the data center. Remember, this is where everything is running. The Oracle Database@AWS, the Oracle Database infrastructure may be managed by Oracle and run in OCI, but is physically located within the AWS regions, as well as the availability zones and the AWS data centers.  The AWS infrastructure, in this case, is AWS's responsibility to secure the environment, including the physical security of the data center, the network infrastructure, and the foundational services like the compute, the storage, and the networking, all within AWS.  The next thing of who's responsible for the shared responsibility, it's Oracle. And that would be the hardware. We provide the hardware. While the hardware may physically reside in the AWS data center, Oracle's Cloud Infrastructure operational team will be the one managing this infrastructure, including software patching, infrastructure update, and other operations through a connection to OCI. This means Oracle handles the provisioning, as well as the maintenance of any of the underlying Exadata infrastructure hardware.  When we take a look at the next thing that it manages, it is also responsible besides the infrastructure of the Exadata. It is also the ability to manage the hardware, the environment of that hardware through the database control plane. So Oracle manages the administration and the operational for the Oracle Database@AWS service, which resides in OCI. So this includes the capabilities for management, upgrade, and operational features.  08:37 Nikita: And what are the key things that still remain on the customer's plate?   Susan: If you are in an Exadata environment or in an Autonomous environment, it is you, the customer, who is responsible for most of the database administration operation, as well as managing the users and the privileges of the user to access the database. No one knows the database and who should be accessing the data better than you.  You will be responsible for securing the applications, the data of the database, which now allows you to define who has access to it, control the data encryption, and securing the application that interacts with the Oracle Database@AWS.  09:29 Lois: Susan, we've talked about both Autonomous Database and Exadata Database Service being available on Oracle Database@AWS, but what's different about how each works in this environment, and why might someone pick one over the other?  Susan: Both databases, even though they run on the same Exadata Cloud Infrastructure, both can be deployed on both public cloud, as well as the customer data center, which is Oracle Cloud@Customer.  The Autonomous Database is a fully managed, completely automated environment. And this provides a capability of having a fully Autonomous Database Service running on a dedicated Oracle Exadata Infrastructure within your AWS data center.  The Exadata is a service that is provided and managed by Oracle and is physically running in the AWS data center, but is designed for mission critical workload and includes RAC environment, Real Application Cluster, offering a high performance availability and full feature capability that is similar to other Exadata environment, such as those running in our customers' data center.  The primary difference is really between the two services. When you take a look at the Exadata, the customer only pays for the compute resources that is used. Autoscaling can be used for a variety or variable resources, the workload, to automatically scale to the compute resources up or down when required.  The Autonomous Database also has automatic optimization for data warehousing, transaction processing, as well as JSON workload. The Exadata service, the customer again, also pays for the compute resources that they allocate. But that's the key thing. The customer can initiate the scaling because it's very specific to the workload that is needed.  So when you take a look at the two database services, one gives the ability to let Oracle fully manage it, including the scaling capability. The other, the Exadata, provides you the capability of having the environment that it's running on the infrastructure be managed by Oracle that adds a database administrator. You may wish to have a little bit more granular control of how you want the database to not only be scaling, but how you wish to customize how the database will be running.  12:10 Nikita: Focusing on Autonomous Database for a moment, what should teams know about how it actually runs within AWS?   Susan: The Autonomous Database on the Oracle Database@AWS brings the power of the Oracle's self-managing, self-securing, and self-repairing database into your AWS environment.  It provides the capability of the database automatically, automates many of the traditional, complex, and time-consuming database management tasks, such as the provisioning of the database, the patching, the backing up, and the scaling, and the performance tuning, reducing the need for any manual intervention by the database administrator.  Running the Autonomous Database in your AWS region enables low latency access for your AWS applications and services that is deployed within AWS, thus improving performance and response time. With the Autonomous Database, it automates many of the traditional things that is now automatically done by Oracle. It also supports integration with various AWS services, such as the ability of the not in addition to AIM, but the cloud formation, the CloudWatch for monitoring and the S3 for the storage.  You can easily migrate existing Exadata workload, including those running on Oracle RAC to AWS with minimum or no change to any of your databases or applications. In addition, there's a really powerful capability and feature of the database is called zero ETL, and that's zero extract, transformation, and load.  It's an integration capability with services like your Amazon Redshift, enabling near real time analytics and machine learning on your transactional database that is stored within the Autonomous Database on in your AWS environment. So with the Autonomous Database, it checks off many of the boxes for automatic capability, securing, tuning, as well as scaling the database.  With the Autonomous Database in the Dedicated Exadata Infrastructure, the Exadata Cloud Infrastructure resource represents the physical system, which can be expanded with storage, as well as compute services, the compute host. This now provides the ability to have an isolated zone for the highest protection from other tenants. The data is stored on a dedicated server only for one customer. That would be you.  14:56 Lois: Could you explain the role of Autonomous VM? What are its primary benefits?  Susan: The virtual machine or as we refer to them as the cluster, includes the grid infrastructure and provides a private network isolation. This provides you the capability of having custom memory, core, and storage allocation.  The Oracle Grid Infrastructure includes the Oracle Clusterware, which manages the cluster, as well as the servers, and ensure that the database can failover to another server in case of any failure.  15:34 Be a part of something big by joining the Oracle University Learning Community! Connect with over 3 million members, including Oracle experts and fellow learners. Engage in topical forums, share your knowledge, and celebrate your achievements together. Discover the community today at mylearn.oracle.com.  15:55 Nikita: Welcome back! Susan, what is the Autonomous Container Database?  Susan: With the Autonomous Container Database, and you need that if you're going to create an Autonomous Database, you need to provision that within your Autonomous Exadata VM Cluster. It serves as a container to hold or to house one or more Autonomous Databases.  This allows multiple Autonomous Databases to coexist in the same infrastructure while still being logically separated. And this allows for the separation of databases based on their intended use. Think of a database for production. Think of a database for development. Think of a database for testing. You may have different database versions within the same infrastructure.  This isolation makes it easier for you to be able to meet your SLA, your Service Level Agreement, any long-term backups you may have, very specific encryption key needs to prevent issues from one database impacting another. So, the ability to have everything be isolated and secure is still grouping it in a manner that will meet your business needs.  17:08 Lois: Looking at Exadata Database Service specifically, what are some standout advantages for customers who deploy it on Oracle Database@AWS? Is there anything in particular they should get excited about in terms of performance or integration with AWS?  Susan: The Exadata Database Service is running on a dedicated Exadata Infrastructure that's deployed within your AWS data center. It delivers the same Exadata service experience in cloud control planes as the Oracle Cloud Infrastructure, allowing you to leverage existing skills and processing across your multi-cloud environment.  It addresses the data resiliency, or residency rather. And that's the scenario where many of our customers has the need. You have a need because of your security compliance to have the data local to you. By having the Exadata Database in your Oracle Database@AWS, it is running in your data center. So, this addresses that very important need, data residency, to have it close to you.  It also allows for seamless integration with other AWS services and applications. So now you have a capability of a hybrid cloud architecture leveraging the benefit of both Oracle Exadata and your AWS system. It has built-in high availability, the RAC application cluster, as well as Data Guard, a capability of addressing disaster recovery capability.  This also provides the ability for you to scale your compute, as well as your storage and your I/O resources independently. So as mentioned with Exadata, you have flexibility of how you want your database to be running individually. So just like the Autonomous, the Exadata Database checks off many of the boxes for running a mission-critical with high availability, highly redundant hardware and software features, along with extreme performance, scalability, and reliability.  This now allows you to run your AI environment, your online transaction processing, your analytic workload on any scale on the Exadata Infrastructure running in the Oracle Cloud. And in this case, running in your data center.  19:45 Nikita: If a business suddenly needs more capacity, how does scaling work with Exadata Database Service versus Autonomous Database on Oracle Database@AWS?   Susan: So with the Exadata scaling, you now can scale to meet expected demands so you know at certain point I will need more. I will then ask it to scale at that point when I will assign it-- and I'm using an example, I will assign it three computer cores all the time. But there may be demands. Think of your end of the quarter, end of the year processing that you may need more. So, you are enabling the compute cores to scale at the time you need it.  And what's cool is it will then, when it's no longer needed, it will then scale back down to the original three cores that you assign. So, you only pay for the enabled cores. But what's very cool about the Autonomous is that it is real-time scaling. So, with Autonomous, now you have the capability using Autonomous Database since it is self-tuning, self-monitoring, the Autonomous Database actually monitors the workload requirement and scales to match the workload demand.  Once the minimum level of the compute is defined and enabled, the automatic scaling is set. Autonomous Database will adjust to the consumption when it's needed, and it will scale back down when it's not. So though the Exadata is pretty cool, it will scale up and down on the workload demand.  This is with the Autonomous is even more powerful. It is real-time scaling based on that usage at that moment. Built-in automatic increase to meet the workload demands when it spikes and it automatically scales back when it's not needed.  A very powerful capability with all of our Oracle databases, the ability, even with traditional, to allow you to define what you may need with Exadata scaling for peak demands, as well as Autonomous scaling for real-time consumption and scaling when needed.  When you look at all of our options, one of the key things to bear in mind is a phrase that we use: performance scale as more servers are added. And what this is really saying is Oracle's automated scaling ability for the database, it basically has the ability to maintain or improve its performance under increased workload by automatically adding computational resources when needed.  This process is also known as horizontal scaling. It involves adding more servers, compute instances, to a cluster to share the processing load. And it has that capability automatically.  22:53 Nikita: There's so much more we can discuss about Oracle Database@AWS, but let's pause here for today! Thank you so much Susan for joining us.  Lois: Yeah, it's been really great to have you, Susan. If you want to dive deeper into the topics we covered today, go to mylearn.oracle.com and search for the Oracle Database@AWS Architect Professional course. Until next time, this is Lois Houston…  Nikita: And Nikita Abraham, signing off!  23:23 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Ericka Adler is joined by Roetzel shareholder Christina Kuta to discuss the growing trend of concierge practices and the initial steps to start a concierge practice. Ericka and Christina explain why choosing the right professional entity matters, how state laws and corporate practice of medicine rules may apply, and the key differences between hybrid concierge practices and cash-only practices. They also cover important compliance considerations for insurance contracts and Medicare, along with essential concierge documents like intake paperwork, patient agreements, HIPAA documents, good faith estimates and informed consents. Find all of our network podcasts on your favorite podcast platforms and be sure to subscribe and like us. Learn more at www.healthcarenowradio.com/listen/

Are your clients technically improving… but not actually moving forward? In this episode of The Financial Coaches Podcast, Cody and Maria unpack a subtle but powerful shift in strategy that has helped certain clients break out of stagnation and finally start making meaningful progress. Traditionally, Cody has taught clients to “backload” their goals — waiting until the end of the month to apply surplus toward debt, savings, or investing. And for many people, that works well. But for clients who struggle with consistency and spending habits, that structure can unintentionally keep them stuck. So what's the alternative? In this episode, you'll learn: The difference between backloading and frontloading financial goals Why some clients plateau even when they stop going backward How to identify which clients need a strategic shift The psychology behind “which hard would you rather choose?” How to balance risk with forward momentum When deeper mindset work is required (and not just a tactic tweak) This conversation is about more than cash flow timing. It's about knowing your client, recognizing patterns, and adjusting your coaching approach so progress actually sticks. Because sometimes the breakthrough isn't working harder. It's structuring the plan differently.

This is episode 2 of 5 in the Productivity with A.I. Reset Series. In this series, we are resetting our productivity for an A.I. World, realigning how to do business as an online entrepreneur so we can take advantage of the greatest technology transformation we have ever seen.Many aspiring entrepreneurs feel overwhelmed — not because they lack motivation, but because they lack filters.  In this episode, Case introduces The Productivity Filter — a simple framework that helps you eliminate distractions, automate low-value work, and amplify the actions that actually build an online business. Productivity isn't about grinding harder…it's about choosing better. Your Action Plan: Learn What to eliminate immediatelyWhat to automate for leverageWhat to amplify for growthTo have an enjoyable life  in our global, advanced tech society, you can create value.  To have the business, career, finances and lifestyle you desire, follow a proven path that has delivered in good times and bad.  The path of entrepreneurship. And online entrepreneurship is the fast track for aspiring entrepreneurs.Learn the skills, access the resources and be inspired to live the life of your dreams right here on the Ready Entrepreneur podcastTo find more resources, strategies and ideas for aspiring entrepreneurs visit the Ready Entrepreneur website: https://www.readyentrepreneur.com/To download a free guide for Preparing to Become an Online Entrepreneur, click here:  https://www.readyentrepreneur.com/start/You can get an exclusive discount on the ebook and audiobook version of Recast: The Aspiring Entrepreneur's Practical Guide to Getting Started with an Online Business click here: https://www.caselane.net/recastConnect with CaseFacebook: @readyentrepreneurHQ Instagram: @readyentrepreneur Twitter X: @caselaneworld Pinterest @caselane 

Over the past year, the Trump administration has been eliminating policies aimed at slowing down climate change – and now, it may go even further. This week, the Environmental Protection Agency plans to repeal the “endangerment finding” that has been the scientific basis of rules limiting greenhouse gas emissions since 2009. To talk more about this endangerment finding and where the fight against climate change goes from here, we spoke to Leah Stokes. She's an associate professor at UC Santa Barbara where she works on climate and clean energy policy and co-host of the climate podcast, A Matter of Degrees. And in headlines, Department of Homeland Security officials testify before Congress, Commerce Secretary Howard Lutnick admits to lunching with his kids on Epstein Island, and the Trump administration takes down a rainbow flag at the Stonewall National Monument in New York City.Show Notes: Check out Leah's podcast – https://www.degreespod.com/ Call Congress – 202-224-3121 Subscribe to the What A Day Newsletter – https://tinyurl.com/3kk4nyz8 What A Day – YouTube – https://www.youtube.com/@whatadaypodcast Follow us on Instagram – https://www.instagram.com/crookedmedia/ For a transcript of this episode, please visit crooked.com/whataday

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She baked her own birthday cake at 65 and it changed everything. A moving and deeply personal story about reinvention, aging, and becoming unfolds as Wendy Green shares how fear, loneliness, and expectations collided with creativity, courage, and choice. Through powerful stories of role models, resilience, and women redefining life after retirement, this conversation challenges everything we think we know about aging and purpose. This is a reminder that it is never too late to start again, try something new, or claim the life that still wants to be lived. Becoming does not stop at any age, it only asks that you show up.   Find out More about Patricia: https://PatriciaDrain.com

In this episode, Sam Ashoo, MD interviews Jeff Willis, MD on the topic of pre-litigation review, being a medical expert, and common pitfalls leading to medical malpractice cases. 0:15 Introduction0:51 Guest Introduction1:20 Jeff's Background2:00 Current Work3:37 How He Got Started6:57 Pre-Litigation vs. Expert Witness8:01 Four Components of Malpractice Cases13:55 Case Review Statistics17:11 When Cases Get Filed18:58 Common Patterns in Cases19:55 Documentation Best Practices22:06 Shift Handoff Problems25:56 Bounce Backs27:25 Medical Record Volume30:00 Audit Trails32:53 Communication with Consultants41:35 Conflicting Documentation43:46 Getting Started in This Work47:37 ClosingEmergency Medicine Residents, get your free subscription by writing resident@ebmedicine.net

Send a textAs on-premise venues navigate an increasingly fragmented spirits landscape, the relationship between brands and bars has reached a critical inflection point. This Expert Talk features Chris Maffeo, the Founder of MAFFEO DRINKS, to examine why most brand-bar partnerships fail before they begin, and how misaligned incentives, from listing fees that deliver no ROI to guest shifts where brand managers never appear, erode trust on both sides.Chris explores the disconnect between boardroom strategy and bar-floor execution, introducing a practical "if-then" framework for sustainable partnerships that balances bartender personal brand-building with genuine brand advocacy. Chris reveals why treating activations as transactions rather than relationships undermines everyone's bottom line, and demonstrates how accountability gaps, from finance teams demanding immediate returns to bartenders rotating between competing portfolios, can be transformed into long-term value creation.Featured Guests:Chris Maffeo, Founder, MAFFEO DRINKSMentioned in this episode:MAFFEO DrinksWatch on YouTube: The On-Premise Playbook: A Guide to High-Value Bar PartnershipsWant to stay in the know about new episodes from the podcast? Fill out the form below: https://share.hsforms.com/1MEb-81x2TXi3f15qO_yEpA4tip1Learn More About Park StreetSign up for our Daily Industry Newsletter.Sign Up for our Monthly Newsletter.Check out Park Street's Guide to Getting Started in the U.S. MarketFollow us for more industry insights onLinkedIn FacebookTwitterInstagram

Welcome to Financial Revelations with David Szafranski. If you'd like us to review and evaluate your portfolio, contact our office today. You don't have to be a client for us to have a conversation—we're happy to take a look and offer insight. Our Amazon mission trips in March and May are full, but you can still be part of the mission. Visit www.nativosusa.org to learn how to get involved. On these trips, we'll be building a church and bringing both a dentist and a doctor to serve the community. Last week was a rough one for the markets, with headlines suggesting the AI trade is cooling off. David disagrees. He believes the AI and chip trade is just getting started. Demand for chips remains strong, and supply continues to lag behind. In his view, this is long-term growth—not a short-term fade. When markets get hit, you buy the chaos. Don't get shaken out by volatility. Stay growth-oriented. Want a surprise? Email Kory@epsf.com with the subject line: "I want the surprise" for your chance to receive a shirt. In the next podcast, David will break down Stepped-Up Basis—what it is, how to get it, where it applies, and how to implement a strategy that helps you pass more wealth on to the next generation.

Technology companies flooding debt markets are just getting started on funding a $4 trillion artificial intelligence boom, according to Bloomberg Intelligence. “This is the tip of the iceberg,” Robert Schiffman, BI’s senior tech credit analyst, tells Bloomberg News’ James Crombie in this special episode of the Credit Edge podcast. “A lot will depend on at what pace industries are embracing AI technologies,” adds Anurag Rana, a senior BI equity analyst who also covers the sector. BI expects AI capital expenditure to exceed $4 trillion in the US through the end of 2030. The trio also discuss the impact of surging bond issuance on credit spreads, the appeal of very long-dated debt in a sector susceptible to disruption and the biggest risks for this year.See omnystudio.com/listener for privacy information.

What happens when your income suddenly drops — not because of poor performance, but because life happens? In this candid episode of The Financial Coaches Podcast, Cody Sizemore and Maria Casillas pull back the curtain on the real financial and emotional challenges of running a coaching business. After battling illness and facing two slower-than-expected months, Cody shares the stress, mindset shifts, and leadership decisions required to stay grounded when revenue fluctuates. Together, they explore the natural ebbs and flows of entrepreneurship, how to prepare financially for unpredictable seasons, and why these experiences ultimately make you a stronger — and more empathetic — coach. If you're building a coaching practice, thinking about going full-time, or navigating inconsistent income, this conversation will help you stay focused, resilient, and prepared for whatever comes next. In This Episode, You'll Learn: Why income volatility is normal in coaching and entrepreneurship How to prepare financially for slower seasons The mindset required to handle financial stress How difficult seasons can increase empathy for your clients Why transparency builds trust and leadership Practical insights for paying yourself as a coach This is an honest look at the realities behind the business — because success isn't about avoiding hard seasons… it's about learning how to navigate them.

WORRIED ABOUT THE MARKET? SCHEDULE YOUR FREE PORTFOLIO REVIEW with Thoughtful Money's endorsed financial advisors at https://www.thoughtfulmoney.comToday's guest has been predicting an oncoming boom in the commodity complex for a while now.And increasingly, it's starting to look like that boom is arriving.If so, how high will it go? And how long will it last?And where may the best opportunities lie for investors?For answers, we turn to macro and commodities expert Tavi Costa formely of Crescat Capital. but now newly running his own show at Azuria Capital.#commodities #gold #oilandgas _____________________________________________ Thoughtful Money LLC is a Registered Investment Advisor Promoter.We produce educational content geared for the individual investor. It's important to note that this content is NOT investment advice, individual or otherwise, nor should be construed as such.We recommend that most investors, especially if inexperienced, should consider benefiting from the direction and guidance of a qualified financial advisor registered with the U.S. Securities and Exchange Commission (SEC) or state securities regulators who can develop & implement a personalized financial plan based on a customer's unique goals, needs & risk tolerance.IMPORTANT NOTE: There are risks associated with investing in securities.Investing in stocks, bonds, exchange traded funds, mutual funds, money market funds, and other types of securities involve risk of loss. Loss of principal is possible. Some high risk investments may use leverage, which will accentuate gains & losses. Foreign investing involves special risks, including a greater volatility and political, economic and currency risks and differences in accounting methods.A security's or a firm's past investment performance is not a guarantee or predictor of future investment performance.Thoughtful Money and the Thoughtful Money logo are trademarks of Thoughtful Money LLC.Copyright © 2026 Thoughtful Money LLC. All rights reserved.

Stablecoins are no longer just a crypto utility, they're becoming the rails of the next financial system, and the real battle over who controls them is only beginning. In this conversation, Tether CEO Paolo Ardoino and Bo Hines explain why Tether built its technology years before regulation caught up, how liquidity and distribution now matter more than ideology, and why banks, governments, and institutions are all racing to secure their place in a system that's quietly reshaping Bitcoin, stablecoins, and global finance from the inside out.

In this episode of the Creators Leverage Guild podcast, Mike and Ben break down the Amazon Influencer Program from the ground up, with a strong focus on beginners and those early in the process.We walk through what the Amazon Influencer Program actually is, how Phase One works, and the different ways you can apply using your existing social media accounts. We also share practical tips for getting approved and setting yourself up for success right from the start.From there, we move into Phase Two and talk through what your first three videos should and should not look like. We cover common mistakes that can slow people down early on, what Amazon is really looking for, and how to avoid unnecessary rejections.A big part of this episode focuses on the importance of volume and consistency, specifically why creating your first 100 videos matters so much. We explain how those early videos help you build confidence, improve your filming process, and better understand the metrics that actually drive engagement and conversions.We also touch on basic equipment considerations, technical tips to make filming easier, and how being part of a community can shorten the learning curve as you grow in the program.If you're brand new to the Amazon Influencer Program or looking for clarity on what to focus on next, this episode lays out a clear path forward.________________________JOIN THE COMMUNITYIf you are looking for deeper strategy, accountability, and honest conversations with other serious content creators, the Creator's Leverage Guild was built for exactly that.Learn more and join here:⁠Creator's Leverage Guild⁠WORK 1-ON-1 WITH MIKE AND BENGet personalized guidance on content strategy, monetization, brand deals, and scaling your creator business.• Book a 1-hour coaching call• Save with a 4-session coaching package⁠Sign Me Up!⁠_________________________JOIN OUR FREE FACEBOOK COMMUNITYConnect with other Amazon Influencers and content creators, ask questions, and stay up to date on what is working right now.⁠Amazon Influencer Success Facebook Group⁠_________________________TOOLS AND RESOURCES FOR CREATORSViral VueMake smarter content decisions and grow faster.⁠Try Viral Vue here⁠Use code STRAHL10 for 10% off for lifeOinkTrack earnings and performance across platforms.⁠Try Oink here⁠Use code STRAHL10 for 10% off for lifeDescriptEdit podcasts and videos faster and easier.⁠Check out Descript here⁠Affiliate links. We may earn a small commission at no extra cost to you.__________________________CONTACTHave a question, collaboration opportunity, or topic request?Email: mike@creatorsleverageguild.com

You don't need a better plan.You don't need more qualifications.And you definitely don't need to wait until you've got the perfect plan.In this episode, we unpack the real reason so many thoughtful, capable people stay stuck at the starting line: fear. Fear of getting it wrong. Fear of being judged. Fear of committing to something that might not work.Rather than trying to “overcome” fear or force confidence, we explore a quieter, more effective reframe: treating your idea like an experiment.Drawing on stories from our own journey, we talk about why mistakes and flaws aren't signs you're failing. They're how learning actually happens. And why perfectionism is often less about high standards, and more about self-protection.In this conversation, you'll explore how to:Reframe fear and mistakes as useful data, not personal failureSpot when perfectionism is keeping you safely stuckBreak through analysis paralysis by doing tiny experimentsMove forward without needing full clarity or certaintyIf you've been sitting on an idea for a while, waiting to feel “ready”, this episode is a gentle nudge to stop polishing and start playing.

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

Part 1 of the 2026 essential checklist walks first-time homebuyers through the smartest way to start—by building a financing plan, avoiding common internet myths, and getting a real pre-approval early. This episode kicks off a multi-part “essential checklist” with a focus on financing and loan strategy, including a rapid-fire myth bust around “deal” listings like short sales and tax lien properties. It explains why the 20% down payment belief is outdated for most first-time buyers, and outlines today's common low-down-payment paths (including FHA, conventional, VA, and USDA where eligible). It also breaks down why “pre-qualification” is mostly marketing noise, why APR matters alongside the interest rate, and why buyers should prioritize finding the right people (a specialist realtor + lender team) over chasing a single number. Finally, it flags that down payment assistance programs and grants change frequently, so buyers should always ask about current options and stacking possibilities. "I'm going to go rapid fire with coaching and tips on the best ways to work your financing, as well as your loan when you're trying to buy your first home.”HighlightsWhat “too good to be true” home deals (short sales, tax liens, foreclosures) are not realistic for most first-time buyers in 2026—and why? How can first-time buyers stop chasing the “best rate” and start comparing loans using APR and total cost instead? Why is “get pre-qualified first” often the wrong first step—and what does a real pre-approval actually do for your plan? What's the smartest order for building your team so your realtor and lender work together (and you don't lose money or lose the house)? Referenced EpisodesEpisode 400 — Starting point / 10-step system Episode 425 — Buyer story referenced in the new listener question segment Episode 426 — Low down payment options + more on down payment assistance Episode 437 — More on the “unicorn team” concept Episode 440 — More on the “20% down” myth For more, check out our updated 2026 First Time Homebuyer's Episode Guide - Over 100 of our BEST Episodes of Detailed Homebuying Knowledge, Interviews, and MORE! Connect with me to find a trusted realtor in your area or to answer your burning questions!Subscribe to our YouTube Channel @HowToBuyaHomeInstagram @HowtoBuyAHomePodcastTik Tok @HowToBuyAHomeVisit our Resource Center to "Ask David" AND get your FREE Home Buying Starter Kit!David Sidoni, the "How to Buy a Home Guy," is a seasoned real estate professional and consumer advocate with two decades of experience helping first-time homebuyers navigate the real estate market. His podcast, "How to Buy a Home," is a trusted resource for anyone looking to buy their first home. It offers expert advice, actionable tips, and inspiring stories from real first-time homebuyers. With a focus on making the home-buying process accessible and understandable, David breaks down complex topics into easy-to-follow steps, covering everything from budgeting and financing to finding the right home and making an offer. Subscribe for regular market updates, and leave a review to help us reach more people. Ready for an honest, informed home-buying experience? Viva la Unicorn Revolution - join us!

Microsoft is burning through billions on AI, but Wall Street is finally demanding to see where the payoff is. The earnings announcement triggered a $357 billion valuation wipe-out, the largest in Microsoft's history and the second-largest in history overall (Nvidia managed to lose $593 billion in value in the wake of DeepSeek in early 2025).Windows Windows 11 has over one billion users - and, surprise, it got their faster than Windows 10 without any of the shenanigans Microsoft to address the quality issues in Windows 11 in 2026 There is already evidence that Microsoft is trying to make Windows 11 suck less: Recent OneDrive changes that address a key ensh*ttification, and let's not forget all those security advances What did Microsoft really promise? Not much Microsoft has new EVPs for Security and Quality Microsoft belatedly delivered the January Week D update last Thursday, a preview of this month's Patch Tuesday Dev and Beta builds both deliver Mark Russinovich's sysmon tool Microsoft earnings deep dive Microsoft reported a net income of $38.5 billion on revenues of $81.3 billion in the quarter ending December 31. Those figures represent gains of 60 percent and 17 percent, respectively, year-over-year Earnings analysis: All eyes are on AI and no one is happy Microsoft spent $37.5 billion on AI infrastructure (capex) in the quarter, up 66 percent YOY, and it's on track to spend $150+ billion in the fiscal year Every single question was about this and how it will ever recoup the costs There are now 15 million paid Microsoft 365 Copilot seats out of 450+ million Microsoft 365 seats OpenAI is Microsoft's biggest Azure customer, but it's unclear if there is any real money there because of accounting tricks Windows, Edge, and Bing all "gained share," PC maker revenues were up just 1 percent, the Windows 10 upgrade cycle was mostly a bust (it's likely that most of it was tied to RAM pricing fears, too) Xbox fell off a cliff with content and services revenues down 5 percent in a holiday quarter somehow and Xbox hardware revenue declined an astonishing 32 percent YOY Standalone Office 2025 suite was a surprise hit, Hood is curious if that continues Microsoft 365 "cost of business" up 10 percent YOY because of AI costs AMD revenues up 34 percent to $10.3 billion Apple delivers record revenues of $143.8 billion; iPhone made more revenues by itself than all of Microsoft AI Microsoft is going to basically make an app store for content makers who wish to be paid for use by AI Anthropic advertises that Claude will be advertising-free, unlike ChatGPT The next Firefox will include the promised AI kill switch and Vivaldi "extends the middle fingerˮ to AI Xbox and games AMD reveals next Xbox console in 2027 We're getting a solid collection of Xbox Game Pass titles for the beginning of February Battlefield 6 was the best-selling shooter of 2025 and EA made $1.9 billion in Q4 Epic Games has big plans for its PC launcher/store Nintendo has now sold 17 million Switch 2s as OG Switch hits 155 million units Tips and picks Tip of the week: Make OneDrive Folder Backup work for you App pick of the week: Bitwarden (TWiT sponsor) RunAs Radio this week: Getting Started using Purview with Erica Toelle Brown liquor pick of the week: Glendronach Ode to These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/969 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsor: zscaler.com/security

Microsoft is burning through billions on AI, but Wall Street is finally demanding to see where the payoff is. The earnings announcement triggered a $357 billion valuation wipe-out, the largest in Microsoft's history and the second-largest in history overall (Nvidia managed to lose $593 billion in value in the wake of DeepSeek in early 2025).Windows Windows 11 has over one billion users - and, surprise, it got their faster than Windows 10 without any of the shenanigans Microsoft to address the quality issues in Windows 11 in 2026 There is already evidence that Microsoft is trying to make Windows 11 suck less: Recent OneDrive changes that address a key ensh*ttification, and let's not forget all those security advances What did Microsoft really promise? Not much Microsoft has new EVPs for Security and Quality Microsoft belatedly delivered the January Week D update last Thursday, a preview of this month's Patch Tuesday Dev and Beta builds both deliver Mark Russinovich's sysmon tool Microsoft earnings deep dive Microsoft reported a net income of $38.5 billion on revenues of $81.3 billion in the quarter ending December 31. Those figures represent gains of 60 percent and 17 percent, respectively, year-over-year Earnings analysis: All eyes are on AI and no one is happy Microsoft spent $37.5 billion on AI infrastructure (capex) in the quarter, up 66 percent YOY, and it's on track to spend $150+ billion in the fiscal year Every single question was about this and how it will ever recoup the costs There are now 15 million paid Microsoft 365 Copilot seats out of 450+ million Microsoft 365 seats OpenAI is Microsoft's biggest Azure customer, but it's unclear if there is any real money there because of accounting tricks Windows, Edge, and Bing all "gained share," PC maker revenues were up just 1 percent, the Windows 10 upgrade cycle was mostly a bust (it's likely that most of it was tied to RAM pricing fears, too) Xbox fell off a cliff with content and services revenues down 5 percent in a holiday quarter somehow and Xbox hardware revenue declined an astonishing 32 percent YOY Standalone Office 2025 suite was a surprise hit, Hood is curious if that continues Microsoft 365 "cost of business" up 10 percent YOY because of AI costs AMD revenues up 34 percent to $10.3 billion Apple delivers record revenues of $143.8 billion; iPhone made more revenues by itself than all of Microsoft AI Microsoft is going to basically make an app store for content makers who wish to be paid for use by AI Anthropic advertises that Claude will be advertising-free, unlike ChatGPT The next Firefox will include the promised AI kill switch and Vivaldi "extends the middle fingerˮ to AI Xbox and games AMD reveals next Xbox console in 2027 We're getting a solid collection of Xbox Game Pass titles for the beginning of February Battlefield 6 was the best-selling shooter of 2025 and EA made $1.9 billion in Q4 Epic Games has big plans for its PC launcher/store Nintendo has now sold 17 million Switch 2s as OG Switch hits 155 million units Tips and picks Tip of the week: Make OneDrive Folder Backup work for you App pick of the week: Bitwarden (TWiT sponsor) RunAs Radio this week: Getting Started using Purview with Erica Toelle Brown liquor pick of the week: Glendronach Ode to These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/969 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsor: zscaler.com/security

Microsoft is burning through billions on AI, but Wall Street is finally demanding to see where the payoff is. The earnings announcement triggered a $357 billion valuation wipe-out, the largest in Microsoft's history and the second-largest in history overall (Nvidia managed to lose $593 billion in value in the wake of DeepSeek in early 2025).Windows Windows 11 has over one billion users - and, surprise, it got their faster than Windows 10 without any of the shenanigans Microsoft to address the quality issues in Windows 11 in 2026 There is already evidence that Microsoft is trying to make Windows 11 suck less: Recent OneDrive changes that address a key ensh*ttification, and let's not forget all those security advances What did Microsoft really promise? Not much Microsoft has new EVPs for Security and Quality Microsoft belatedly delivered the January Week D update last Thursday, a preview of this month's Patch Tuesday Dev and Beta builds both deliver Mark Russinovich's sysmon tool Microsoft earnings deep dive Microsoft reported a net income of $38.5 billion on revenues of $81.3 billion in the quarter ending December 31. Those figures represent gains of 60 percent and 17 percent, respectively, year-over-year Earnings analysis: All eyes are on AI and no one is happy Microsoft spent $37.5 billion on AI infrastructure (capex) in the quarter, up 66 percent YOY, and it's on track to spend $150+ billion in the fiscal year Every single question was about this and how it will ever recoup the costs There are now 15 million paid Microsoft 365 Copilot seats out of 450+ million Microsoft 365 seats OpenAI is Microsoft's biggest Azure customer, but it's unclear if there is any real money there because of accounting tricks Windows, Edge, and Bing all "gained share," PC maker revenues were up just 1 percent, the Windows 10 upgrade cycle was mostly a bust (it's likely that most of it was tied to RAM pricing fears, too) Xbox fell off a cliff with content and services revenues down 5 percent in a holiday quarter somehow and Xbox hardware revenue declined an astonishing 32 percent YOY Standalone Office 2025 suite was a surprise hit, Hood is curious if that continues Microsoft 365 "cost of business" up 10 percent YOY because of AI costs AMD revenues up 34 percent to $10.3 billion Apple delivers record revenues of $143.8 billion; iPhone made more revenues by itself than all of Microsoft AI Microsoft is going to basically make an app store for content makers who wish to be paid for use by AI Anthropic advertises that Claude will be advertising-free, unlike ChatGPT The next Firefox will include the promised AI kill switch and Vivaldi "extends the middle fingerˮ to AI Xbox and games AMD reveals next Xbox console in 2027 We're getting a solid collection of Xbox Game Pass titles for the beginning of February Battlefield 6 was the best-selling shooter of 2025 and EA made $1.9 billion in Q4 Epic Games has big plans for its PC launcher/store Nintendo has now sold 17 million Switch 2s as OG Switch hits 155 million units Tips and picks Tip of the week: Make OneDrive Folder Backup work for you App pick of the week: Bitwarden (TWiT sponsor) RunAs Radio this week: Getting Started using Purview with Erica Toelle Brown liquor pick of the week: Glendronach Ode to These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/969 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsor: zscaler.com/security

Microsoft is burning through billions on AI, but Wall Street is finally demanding to see where the payoff is. The earnings announcement triggered a $357 billion valuation wipe-out, the largest in Microsoft's history and the second-largest in history overall (Nvidia managed to lose $593 billion in value in the wake of DeepSeek in early 2025).Windows Windows 11 has over one billion users - and, surprise, it got their faster than Windows 10 without any of the shenanigans Microsoft to address the quality issues in Windows 11 in 2026 There is already evidence that Microsoft is trying to make Windows 11 suck less: Recent OneDrive changes that address a key ensh*ttification, and let's not forget all those security advances What did Microsoft really promise? Not much Microsoft has new EVPs for Security and Quality Microsoft belatedly delivered the January Week D update last Thursday, a preview of this month's Patch Tuesday Dev and Beta builds both deliver Mark Russinovich's sysmon tool Microsoft earnings deep dive Microsoft reported a net income of $38.5 billion on revenues of $81.3 billion in the quarter ending December 31. Those figures represent gains of 60 percent and 17 percent, respectively, year-over-year Earnings analysis: All eyes are on AI and no one is happy Microsoft spent $37.5 billion on AI infrastructure (capex) in the quarter, up 66 percent YOY, and it's on track to spend $150+ billion in the fiscal year Every single question was about this and how it will ever recoup the costs There are now 15 million paid Microsoft 365 Copilot seats out of 450+ million Microsoft 365 seats OpenAI is Microsoft's biggest Azure customer, but it's unclear if there is any real money there because of accounting tricks Windows, Edge, and Bing all "gained share," PC maker revenues were up just 1 percent, the Windows 10 upgrade cycle was mostly a bust (it's likely that most of it was tied to RAM pricing fears, too) Xbox fell off a cliff with content and services revenues down 5 percent in a holiday quarter somehow and Xbox hardware revenue declined an astonishing 32 percent YOY Standalone Office 2025 suite was a surprise hit, Hood is curious if that continues Microsoft 365 "cost of business" up 10 percent YOY because of AI costs AMD revenues up 34 percent to $10.3 billion Apple delivers record revenues of $143.8 billion; iPhone made more revenues by itself than all of Microsoft AI Microsoft is going to basically make an app store for content makers who wish to be paid for use by AI Anthropic advertises that Claude will be advertising-free, unlike ChatGPT The next Firefox will include the promised AI kill switch and Vivaldi "extends the middle fingerˮ to AI Xbox and games AMD reveals next Xbox console in 2027 We're getting a solid collection of Xbox Game Pass titles for the beginning of February Battlefield 6 was the best-selling shooter of 2025 and EA made $1.9 billion in Q4 Epic Games has big plans for its PC launcher/store Nintendo has now sold 17 million Switch 2s as OG Switch hits 155 million units Tips and picks Tip of the week: Make OneDrive Folder Backup work for you App pick of the week: Bitwarden (TWiT sponsor) RunAs Radio this week: Getting Started using Purview with Erica Toelle Brown liquor pick of the week: Glendronach Ode to These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/windows-weekly/episodes/969 Hosts: Leo Laporte, Paul Thurrott, and Richard Campbell Sponsor: zscaler.com/security

Ready to get started with Purview? Richard chats with Erica Toelle about the first steps you can take to harness the power of Purview in your organization. Erica explains that Purview is an umbrella product that covers several infosec technologies, including information rights management, data loss prevention, structured data governance, and more. When preparing for M365 Copilot, you want to start tagging sensitive information in your organization, and Purview can help by using LLMs to identify potentially sensitive content. You can also monitor how data is used, the types of prompts sent to M365 Copilot, and more. This can help you bootstrap M365 Copilot by using Purview to see which data Copilot uses, and then tune the access rules for that data. "Getting your data estate in order" is not a destination; it's a journey, and Purview can give you a map!LinksMicrosoft PurviewData Security Posture ManagementData Governance with Microsoft PurviewConditional Access with Microsoft PurviewSharePoint Advanced ManagementMicrosoft 365 ArchiveEndpoint Data Loss PreventionPurview Browser ExtensionRecorded January 7, 2026

(Disclaimer: Click 'more' to see ad disclosure) Geobreeze Travel is part of an affiliate sales network and receives compensation for sending traffic to partner sites, such as MileValue.com. This compensation may impact how and where links appear on this site. This site does not include all financial companies or all available financial offers. Terms apply to American Express benefits and offers. Enrollment may be required for select American Express benefits and offers. Visit americanexpress.com to learn more.  ➤ Free points 101 course (includes hotel upgrade email template)⁠https://geobreezetravel.com/freecourse⁠  ➤ Free credit card consultations ⁠https://airtable.com/apparEqFGYkas0LHl/shrYFpUr2zutt5515⁠ ➤ Seats.Aero: ⁠https://geobreezetravel.com/seatsaero⁠ ➤ Request a free personalized award search tutorial: https://go.geobreezetravel.com/ast-form If you are interested in supporting this show when you apply for your next card, check out https://geobreezetravel.com/cards and if you're not sure what card is right for you, I offer free credit card consultations at⁠https://geobreezetravel.com/consultations⁠!Timestamps:00:00 Introduction to Gift Card Buying Tips00:18 Meet GK: The Gift Card Expert00:33 How GK Earned 5 Million Alaska Miles01:30 Getting Started with Gift Card Reselling04:29 Credit Cards for Maximizing Points08:19 Scaling Up Your Gift Card Reselling10:02 The Kroger Advantage12:57 Logistics of Gift Card Reselling15:55 Bulk Selling Gift Cards to Banks16:19 The Challenges of Reselling Gift Cards17:16 Streamlining Gift Card Entry18:10 Mitigating Risks in Gift Card Transactions20:17 Time Commitment and Earnings Potential23:17 Scaling Up and Managing Risks28:24 Getting Started with Gift Card ResellingYou can find Julia at: ➤ Free course: ⁠https://julia-s-school-9209.thinkific.com/courses/your-first-points-redemption⁠➤ Website: ⁠https://geobreezetravel.com/⁠➤ Instagram: ⁠https://www.instagram.com/geobreezetravel/⁠➤ Credit card links: ⁠https://www.geobreezetravel.com/cards⁠➤ Patreon: ⁠https://www.patreon.com/geobreezetravel⁠You can find GK at:➤ Website: ⁠The Whales Club⁠Opinions expressed here are the author's alone, not those of any bank, credit card issuer, hotel, airline, or other entity. This content has not been reviewed, approved or otherwise endorsed by any of the entities included within the post. The content of this video is accurate as of the posting date. Some of the offers mentioned may no longer be available.