Podcasts about Steering

D2C Trends 2026: Steering is still allowed in the US. No fees (for now). But that window is closing. In this episode, we sit down with Chip Thurston from FastSpring to break down:The current state of D2C in the USJapan's 15–20% platform feesBrazil joining the partyApple's 7-day attribution windowGoogle's 24-hour windowWhy this might actually increase D2C adoptionHow to treat web shops like e-commerce brandsWhy hybrid monetization is the real playThis isn't theory.This is how publishers are thinking about 2026.

Architecture Business Club host Jon Clayton interviews structural engineer Sam Dean of Porthouse Dean about common structural design pitfalls in home extensions and how to avoid spiraling costs. They discuss ground conditions as a major uncertainty (especially clay), the influence of nearby large trees and desiccation, and the use of low-cost desktop geological reports based on British Geological Survey borehole data to flag risk. They cover ceiling downstands and why beam position is often a cost-and-aesthetics decision between homeowner, architect, and builder, with installation complexity increasing when first-floor joists run into the beam. Sam explains cantilever “rules of thumb” and how corner bifold-door cantilevers can drive up steel and foundation demands, sometimes requiring columns and large foundations due to uplift forces. They address adding an extra storey and the case for trial holes. They also highlight risks of building onto existing, undocumented steelwork from previous extensions, which can force intrusive investigation or replacement when later loft conversions are planned. Sam explains how the architectural design can affect structural costs and outlines what to expect from a good structural engineering service. They touch on AI-generated architectural information, Sam's launch of an AI review service, and he shares the software tool his business can't work without.Today's GuestSam Dean. He started out as a materials scientist and structural engineer, spent a year in the nuclear industry, then teamed up with his friend Chris Porthouse to start PorthouseDean structural engineering. Sam then got hooked on building business systems and automations - to cut out the boring stuff and let his team do better work. When he's not solving process problems – he's cycling to work, playing and watching football, or baking crusty bread and homemade pizzas.—Episode Highlights00:00 Introduction00:39 Introducing Sam Dean01:38 Managing Risk with Groundworks03:08 The Clay Problem04:08 Trees Near Extensions: The Hidden Foundation Cost Driver04:30 When Is a Site Investigation Worth It? Practical Triggers05:10 Low-Cost Desktop Geology Reports: A Smart Early Warning05:49 Designers Missing Key Site Info (Like Trees)06:59 Case Study: The 20m Oak That Shows Up Too Late07:55 Using Maps + Clay Likelihood to Spot Risk Early08:48 Removed Trees Still Matter: Clay Desiccation Explained10:32 Ceiling Downstands vs Flush Beams: Set Expectations Early11:33 “Where Do I Put the Beam?” Why Engineers Don't Always Decide12:13 Joist Direction Changes Everything (and Can Add Thousands)13:01 Goalpost Frames & Rear Wall Openings: What's Cost-Neutral?14:00 Builder vs Client vs Architect: Who's Steering the Decision?14:37 Protecting the Homeowner: Budget Trade-Offs in Plain English15:36 When Architects Aren't On Site: How Design Intent Gets Lost18:06 Roles, Responsibility & the Principal Designer Confusion19:38 Why Small Projects Are So Cost-Driven (and Getting Worse)21:07 Cantilevers 101: The Rule of Thumb That Saves Your Budget23:12 Corner Bifolds + Floating Roofs: The Cantilever Trap25:23 Engineering Workarounds: Columns, Anchors & Uplift Forces27:34 Adding a Storey: Foundation Reality Checks29:32 Building on Existing Steelwork: The Missing Calculations Problem33:37 Prevention Playbook: Trial Holes, Checks, and Lightweight Options36:46 Quick Wins to Avoid Spiraling Costs (Wind Posts, Pillars, Layout)41:45 What Great Structural Engineering Service Looks Like46:49 The Rise of AI48:55 The One Piece of Software Sam Can't Live Without50:18 Final Thoughts—Key TakeawaysCheck the Ground Early to Avoid Big SurprisesLearn...

A Republican governor compares Washington to a car racing toward a cliff—with the GOP driving. The data backs his warning on deficits, growth, and economic mismanagement.Subscribe to our Newsletter:https://politicsdoneright.com/newsletterPurchase our Books: As I See It: https://amzn.to/3XpvW5o How To Make AmericaUtopia: https://amzn.to/3VKVFnG It's Worth It: https://amzn.to/3VFByXP Lose Weight And BeFit Now: https://amzn.to/3xiQK3K Tribulations of anAfro-Latino Caribbean man: https://amzn.to/4c09rbE

Our Olympic spirit continues to soar (or bounce, if you're Ian) as we place crappy drivers on assorted podiums. Are they winners? OH HECK NO. They are most certainly not. Tune in and find out why. We love you!

The Story of God Podcast

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What if failure isn't something to avoid, but a skill to master? This episode breaks down why startups can't be built on certainty—new markets, new products, and new teams mean you're guaranteed to be wrong a lot. The goal isn't to “be right,” it's rapid error correction: make decisions, ship anyway, learn fast, and recover even faster. The conversation covers how avoiding failure leads to paralysis (“steering a parked car”), why indecision compounds in startups, and how to reduce risk by keeping failures small, reversible, and frequent (kill switches, stop rules, and capped losses). They share early personal stories—school fights and a childhood cattle business collapse—to show how overcoming real consequences builds confidence and resilience. Practical examples include choosing an ICP quickly, improving poor conversion rates through iteration, using vesting/cliffs when picking co-founders, and why even top VCs still miss constantly. The key takeaway: the most dangerous competitor is the one who isn't afraid to get hit, recover, and keep coming back—because that's as close to “invincible” as a founder can get.02:01 Failure Isn't the Enemy: Stop Optimizing for Being Right02:59 The Founder Reality: Uncertainty, Rapid Error Correction & the Boxing Analogy03:44 Safety vs Startups: Why Most People Avoid the Risk05:24 ‘Steering a Parked Car': Indecision Kills Startups07:54 Make the Call, Learn Fast: Small Failures, Big Truths09:02 We're Conditioned to Fear Failure (School, Work, Relationships)11:59 Will's Origin Story: Jason Barker and Learning to Beat the Monster14:48 Choosing to Fail on Purpose: Turning Fear into a Superpower17:06 Ryan's First Big Failure: The Farm/Cattle Business Lesson Begins17:45 Cash-Strapped Expansion: Inventory Leverage & a Brutal Winter18:09 When the Side Hustle Needs a Side Hustle (and the Cost of Neglect)18:35 Failing Hard at 12: Losing Animals and Learning to Plan19:51 Founders Don't Win by Being Right—They Win by Taking Hits21:36 Shipping While Wrong: Marketing Experiments, MVPs, and Momentum22:51 Hiring, Co-Founders & Investors: Why Nobody Can Pick Perfectly24:00 The Real Skill: Recovering From Failure (Resilience as a Reflex)30:26 Small Blast Radius, High Frequency: Reversible Bets & Kill Switches31:13 Failure Is Portable: Building a House, Living ‘Why Not,' No RegretsResources:Startup Therapy Podcasthttps://www.startups.com/community/startup-therapyWebsitehttps://www.startups.com/beginLinkedInhttps://www.linkedin.com/company/startups-co/Join our Network of Top FoundersWil Schroterhttps://www.linkedin.com/in/wilschroter/Ryan Rutanhttps://www.linkedin.com/in/ryan-rutan/What to listen for:

Steering is one of the most misunderstood skills in karting — and one of the biggest reasons drivers struggle with grip, consistency, and confidence.In this episode of the Kart Class Podcast, 18-time Australian Champion David Sera breaks down the fundamental principles of steering in karting, explaining how steering inputs directly affect grip, balance, and lap time.This episode explains why turning the wheel more doesn't make you faster, how over-steering hurts exit speed, and what correct steering should actually feel like in a kart.You'll learn:What proper steering technique really is in kartingHow steering affects grip and tyre performanceCommon steering mistakes young drivers makeWhy smooth steering builds confidence and consistencyHow parents can recognise steering issues from the fenceThis episode is ideal for cadet, junior, and senior kart drivers, as well as parents who want to better understand what their driver is struggling with on track.If your driver feels inconsistent, struggles with grip, or looks fast one lap and slow the next, this episode will give you clarity on one of the most important — and overlooked — fundamentals in karting.Any questions? Drop us a Message!To learn more about what Home – Kart Class has to offer be sure to visit our site.Want to watch the podcast episode instead? Follow us on Youtube hereYou can join us at Instagram here to see the latest tips.

Viewpoints Explained: From Streaming To Steering Wheels: The Subscription CreepSubscriptions were supposed to make life easier. Instead, they've become a quiet drain on our wallets. This segment explores how recurring fees slipped into everything from entertainment to cars.Host: Ebony McMorrisProducer: Amirah Zaveri Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

- China to Ban Yoke Steering Wheels and Mandate Physical Buttons - Trump Administration Eliminates EPA Endangerment Finding in Historic Deregulation - Rivian Stock Surges 25% On 2026 Growth Guidance Despite 2025 Revenue Slump - Waymo Rolls Out 6th-Gen AV Tech Stack Targeting 1 Million Weekly Rides - Canada's Project Arrow Debuts Next-Gen EV Prototypes - Maextro S800 Outsells Mercedes-Maybach and BMW 7 Series in China - White House Considers Lowering Steel and Aluminum Tariffs to Ease Auto Manufacturing Costs - Mercedes-Benz To Sell Daimler Truck Stake to Boost Finances After 50% Profit Drop

- China to Ban Yoke Steering Wheels and Mandate Physical Buttons - Trump Administration Eliminates EPA Endangerment Finding in Historic Deregulation - Rivian Stock Surges 25% On 2026 Growth Guidance Despite 2025 Revenue Slump - Waymo Rolls Out 6th-Gen AV Tech Stack Targeting 1 Million Weekly Rides - Canada's Project Arrow Debuts Next-Gen EV Prototypes - Maextro S800 Outsells Mercedes-Maybach and BMW 7 Series in China - White House Considers Lowering Steel and Aluminum Tariffs to Ease Auto Manufacturing Costs - Mercedes-Benz To Sell Daimler Truck Stake to Boost Finances After 50% Profit Drop

Dive into the challenges of marketing with Loralyn Mears from STEERus! Uncover the art of balance as we explore her journey from burnout to brilliance, helping businesses shine online, snag grants, and gear up mid-level managers for the top. Tune in for her transformative tales and practical advice that could steer your business to success!Here are a few topics we'll discuss on this episode of Hard to Market Podcast.Resources:SteerUsPodcast ChefConnect with Loralyn Mears:LinkedInConnect with our host, Brian Mattocks:LinkedInEmailQuotables:03:50 - Brian: That's normally the way it works. Right? It's never a direct line to success. It's always much more of a search algorithm, a much more of a spiral pattern.Loralyn: Yes. Downward spiral being a lot of it, but yes. And then now I'm spiraling up. I've got it. 'cause it's really the messaging is now really more clear. It's helping people get seen and get paid. And what does that mean? Well, if you're a mid-level manager that everybody's overlooking or you're not getting along with your staff, well, you need to get seen, right? And do all of these things differently so that you get promoted or keep your job. 17:14 - It's so much easier to fix other people's problems rather than your own always plus, right? The cobbler's children has no shoes, right? Because we're always fixing everybody else's thing. So like, my website sucks, but everybody else's that I do looks really good. And that's a whole other thing. 15:11 -  The cost of change is often not measured in dollars. It's often measured in organizational fatigue. It's measured in all sorts of the emotional cognitive expense work that isn't easy. It's the same as going to the gym in a lot of these kind of environments where it's like you have to do the, you know, you have to lift the weights, nobody can do it for you. And so you have folks that come in in the small business space, and very often they wanna buy their way out of a problem. And it's like, no, you exercised your way into this problem, you're gonna need to exercise your way out of it. And that creates a very, very difficult dynamic.17:29 - I think it's the state of overwhelm. I think you talked about change, that there is a cost. Mentally, people aren't ready. They know that they need to make change, but it's too much because they've got to change everything on every axis. Right? It's like that movie, first it was only a little bit, and then everything all at once. And it really does become overwhelming, and it's all-consuming, and people just don't have, not even the, the financial capital, which of course is a constraint. You look at the number of small business loans, and two-thirds of small businesses are subsisting on loans. But you look at just the mental energy, it's too much.23:29 - The third thing I would say on go to market is don't get so hung up on the perfection. It's gotta be 99.99% perfect. I really like this. No, I like this image a little better. Let's craft this. Hmm, that post isn't quite right. Just do it. I really believe Wayne Gretzky greatest hockey player of all time, although Alex mentioned, is like taken over. But that's another story, and good for Ovechkin, but Wayne Gretzky said famously, you miss 100% of the shots you don't take. And so that's what I say, don't wait till you're 99.99, 80% is good enough. Get it going. See if it works, test it, try it, refine it. Connect with our host, Brian Mattocks:LinkedInEmailSchedule a Free Podcast Consult

In the episode of the Consumer Finance Monitor podcast we are releasing today, we examine what may be the most consequential development in New York consumer protection law in nearly half a century: the enactment of the New York State Fair Business Practices Act (the FAIR Act). Signed into law in December 2025 and taking effect on February 17, 2026, the FAIR Act represents the first comprehensive overhaul of New York General Business Law § 349 in almost 50 years. Long focused primarily on deceptive acts and practices, Section 349 has now been expanded to expressly prohibit unfair and abusive business practices as well—bringing New York law far closer to the federal UDAAP framework under the Consumer Financial Protection Act. To explore what changed, why it matters, and how the law will be enforced in practice, Alan Kaplinsky (founder and former leader of the Consumer Financial Services Group at Ballard Spahr LLP and now Senior Counsel and host of Consumer Finance Monitor) is joined by two senior officials from the New York Attorney General's Bureau of Consumer Frauds and Protection who were directly involved in shaping and implementing the statute: ·        Jane Azia, Chief of the Bureau of Consumer Frauds and Protection ·        Alec Webley, Assistant Attorney General and one of the attorneys who helped shepherd the FAIR Act through the legislative process What followed was a wide-ranging and unusually candid discussion of the statute's origins, scope, enforcement implications, and practical lessons for businesses operating in, or affecting, New York. From Deception to Unfairness and Abusiveness For decades, New York's consumer protection regime lagged behind most other states and federal regulators by focusing almost exclusively on deception. As Jane Azia explained, deception alone often fails to capture conduct that is plainly harmful to consumers, particularly where disclosures technically exist but are obscured, consumers are subjected to high-pressure tactics, or businesses exploit significant informational or power asymmetries. The FAIR Act closes those gaps by expressly prohibiting: ·        Unfair practices, modeled closely on the FTC's longstanding unfairness framework ·        Abusive practices, drawing heavily on more than a decade of CFPB enforcement experience Importantly, while the statute borrows from federal concepts of unfairness and abusiveness, New York is not bound to follow future CFPB reinterpretations. As Alec Webley emphasized, the legislature carefully chose its language, expressly incorporating only certain federal elements (such as the FTC's "substantial injury" concept) while deliberately declining to tether New York law to future federal regulatory shifts. Broader Scope Than Federal Law One of the most significant differences between the FAIR Act and federal consumer protection law is scope. Jane Azia pointed out that unlike the federal Consumer Financial Protection Act, which applies primarily to financial services, the FAIR Act applies to all business activity occurring in, or affecting consumers in, New York. That means unfair or abusive conduct by non-financial businesses now squarely falls within the Attorney General's enforcement authority. The statute also avoids many of the preemption constraints that can limit state enforcement against national banks under federal law, because it is a law of general application rather than a banking regulation. No Rulemaking—But Clear Signals The FAIR Act does not grant the Attorney General rulemaking authority, and the AG's office does not currently plan to issue formal regulations or written guidance. Instead, businesses should expect the meaning of "unfair" and "abusive" to be fleshed out through enforcement actions, settlements, and existing federal precedent. That said, the Attorney General has already identified categories of conduct likely to draw scrutiny, including: ·        Steering borrowers into unnecessarily costly repayment options ·        High-pressure sales tactics ·        Obscured or misleading pricing ·        Exploitation of consumers with limited English proficiency ·        Misleading marketing in health care, auto sales, and emerging financial products Several examples discussed on the podcast, including enforcement actions involving e-cigarettes, earned wage access products, and savings account practices, illustrate how the AG's office has already been applying unfairness and abusiveness theories under existing authority, and how the FAIR Act now allows those claims to be brought directly under state law. Remedies and Enforcement Tools The FAIR Act does not dramatically alter the remedies available to the Attorney General, but it reinforces a powerful enforcement arsenal, including: ·        Injunctive relief ·        Restitution ·        Civil penalties ·        Disgorgement ·        Expedited "special proceedings" that can allow the AG to move quickly in court to halt unlawful conduct As a reminder, recent amendments to Article 22-a of the general business law also significantly increased civil penalties for violations of section 349 occurring during disasters or abnormal market disruptions, an issue businesses should not overlook. Extraterritorial Reach and Coordination with Other Regulators The discussion also addresses a recurring compliance question: when New York law applies beyond New York's borders. In general, the statute applies where conduct occurs in New York or where New York consumers are harmed. It can also apply to out-of-state consumers harmed by New York-based businesses. By contrast, purely out-of-state conduct with no meaningful New York nexus typically falls outside the statute's reach. The episode also explores how the Attorney General coordinates with: ·        Other state attorneys general in multi-state investigations, ·        The New York Department of Financial Services, ·        The New York City Department of Consumer and Worker Protection, and ·        Federal agencies such as the FTC. Even as federal consumer protection enforcement ebbs and flows, the states, and New York in particular, remain active and increasingly influential. Practical Takeaways for Businesses A central theme of the discussion was that the FAIR Act is not a reason to relax compliance efforts—quite the opposite. As Alec Webley noted, statutes like this create an opportunity for companies and their counsel to step back, reassess business practices, and ask hard questions: ·        Are consumers complaining about this practice? ·        Is it genuinely necessary to the business? ·        Does it obscure costs or risks? ·        Would the company be comfortable seeing it described on the front page of a major newspaper? Practices that may have survived under a narrow deception standard could now pose real enforcement risk under broader unfairness and abusiveness principles. Looking Ahead Both guests emphasize that the FAIR Act was drafted with care and restraint, and that early enforcement actions are likely to fall squarely within the statute's text and intent. At the same time, emerging technologies, particularly digital marketing, fine-print disclosures on mobile devices, and the use of AI, are clearly on the Attorney General's radar. The bottom line is clear: the FAIR Act marks a fundamental shift in New York consumer protection law. With its February 17, 2026 effective date now here, businesses operating in or affecting New York should be taking this development seriously by reviewing practices, strengthening compliance frameworks, and preparing for a more expansive and assertive enforcement environment. We will continue to track developments under the FAIR Act and report on key enforcement actions and interpretations as they unfold. Consumer Finance Monitor is hosted by Alan Kaplinsky, Senior Counsel at Ballard Spahr, and the founder and former chair of the firm's Consumer Financial Services Group. We encourage listeners to subscribe to the podcast on their preferred platform for weekly insights into developments in the consumer finance industry.

Rocketing down the track at more than 100mph, Olympic Bobsleigh Pilot Chris Spring knows that the difference between winning Gold and crashing is measured in fractions of an inch. We talk becoming a Bobsled/Bobsleigh Pilot, the crash that almost ended his career and every bobsledders love/hate relationship with Cool Runnings. Then, it's Queso and Spinach vs. Salsa and Hummus as we countdown the Top 5 Best Dips. 00:00: Introducing Chris Spring 01:03: What Bobsledding is Really Like 03:40: Steering a Bobsled 05:58: Bobsled Crashes 08:31: Bobsled Tracks 10:54: Bobsled Teams 11:35: Getting in the Bobsled 13:43: Cool Runnings 14:53: Bobsled Lingo 15:33: Worst Bobsled Crash 18:29: Bobsled Chants 21:31: Pointless 45:37: Top 5 Dips Contact the Show Learn more about your ad choices. Visit megaphone.fm/adchoices

If you've experienced high-conflict separation, coercive control, other forms of family violence or post-separation abuse, your brain has adapted to help you survive. But survival wiring can make advocacy, decision-making, and communication inside legal and family systems feel overwhelming.In this episode, Danielle Black explains the science of neuroplasticity and how protective parents can intentionally rebuild capacity, confidence, and internal stability - even when external outcomes feel uncertain.But this episode is also about something deeper:Stepping into your own power. Becoming your own safe place. Putting on your own cape.You'll learn:How trauma and chronic stress shape threat-based brain wiringWhy survival patterns can impact communication, advocacy, and decision-makingHow neuroplasticity allows you to intentionally reshape thought patternsHow the STEER Response Framework™ helps you influence the link between situations, thoughts, emotions, nervous system responses, and outcomesWhy worthiness is the foundation of sustainable recovery and advocacyHow affirmations support intentional brain rewiring (without toxic positivity)This episode is an invitation to stop waiting to be rescued - and instead step into the version of you who leads your own recovery, advocacy, and future direction.Recovery after separation isn't just about legal outcomes.It's about identity.Agency.Capacity.And building a life you choose - on purpose.Includes:• Practical STEER examples• Trauma-informed affirmation guidance• Capacity-building insights for protective parents

This week: Jan Dieleman, president of Cargill's ocean transport business, talks with Ian Welsh about the decarbonisation challenge facing global shipping. They explores fuel choices, energy efficiency, customer demand, regulation and why policy clarity is critical to scaling low and zero carbon solutions in a highly competitive sector. Plus: Innovation Forum's Natasha Bodnar highlights how the energy transition is shifting from ambition to delivery, with companies focusing on infrastructure, energy security, capital discipline and system-wide innovation as 2026 unfolds. And, UN warns water crisis threatens fashion supply chains; researchers say ultra processed foods should face tobacco-style laws; and, Oatly and McCain push deeper food decarbonisation, in the news digest (compiled by Ellen Atiyah). Host: Ian Welsh

Are we witnessing a "rupture" of the rules-based international order? How have tariffs and geopolitical events played out on the ground? And what does this all mean for procurement and supply chain professionals? CIPS global economist Dr John Glen and Fastmarkets commodities expert Ross Yeo make sense of all the noise. Related links CIPS Pulse results Q4 2025: Procurement professionals sound the alarm for sustained consumer price increases in 2026 Find out more about commodity-pricing experts Fastmarkets, a CIPS Official Knowledge Partner Read Canadian Prime Minister's Mark Carney's full speech at Davos

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

Send us a textRocket Mortgage gets sued for mortgage steering!Today, I discuss the ethical implications of mortgage steering and RESPA violations in real estate. I will highlight the significant lawsuit against Rocket Mortgage, alleging unethical practices in referral systems that prioritize financial benefits over consumer interests. I also delve into the responsibilities of real estate agents to act in the best interest of their clients and the potential legal ramifications of failing to do so. Lastly, I discuss the importance of transparency in referrals and the need for agents to resist pressure from brokerages to prioritize financial incentives.Don't forget to like us and share us!Gary* Gary serves on the South Carolina Real Estate Commission as a Commissioner. The opinions expressed herein are his opinions and are not necessarily the opinions of the SC Real Estate Commission. This podcast is not to be considered legal advice. Please consult an attorney in your area.

Steering Our Thoughts To Him [Ps. Jon] by Maretul Har UK

3833 – February 4, 2026 – Steering the Titanic into the Iceberg – They said the Titanic was too GREAT – built too strong to fail… but an iceberg took it down… 1500 people drowned. There were survivors to tell the tale… but the Titanic still lies at the bottom of the sea. The United States of America is powerful ... The post Steering the Titanic into the Iceberg appeared first on CSC Talk Radio.

This video is as close to a self-portrait of western civilization as it gets. This is who we are. This is where we're at. Might as well have giant letters with a big red arrow saying “YOU ARE HERE” overtop it. Reading by Tim Foley.

A new nationwide class action lawsuit is accusing Rocket Companies of illegally steering homebuyers toward its mortgage and closing products — even when better rates may have been available elsewhere. The lawsuit alleges Rocket and its affiliates pressured real estate agents, including those at Redfin, to funnel clients to Rocket Mortgage and its title company, potentially violating the Real Estate Settlement Procedures Act, or RESPA. Rocket denies the allegations and says it will vigorously defend itself. In this episode, Kathy Fettke breaks down what the lawsuit claims, how the alleged referral arrangements worked, why the case references a prior Consumer Financial Protection Bureau investigation, and what this could mean for mortgage competition, agent referrals, and consumer choice going forward. Want to learn more? Visit www.Newsforinvestors.com  Source: https://www.scotsmanguide.com/news/class-action-lawsuit-accuses-rocket-of-illegal-steering-scheme/

In this episode of Ohio Policy Talk, Anastasia and Andrew are joined by Legislative Steering Committee Chair Michael Jones to discuss his background in real estate advocacy and his experience in state and federal politics. Michael breaks down the role of the Legislative Steering Committee, how policy positions are formed, and the key legislative issues Ohio REALTORS® should be watching in 2026.Full Description / Show NotesHear about Michael Jones and his background in real estate, and what drew him into advocacyGet insight into how his experience in state and federal politics influences his leadership as Legislative Steering Committee ChairDiscover what the Legislative Steering Committee does and how it supports Ohio REALTORS®' advocacy effortsTake a closer look at how policy positions are reviewed and decided, from support to opposition or neutralityListen in on the key legislative issues REALTORS® should be watching heading into 2026Learn about how to start getting involved in advocacy

In typical MotorMouth fashion, our first topic is how to diagnose a modern washing machine by reading its trouble codes. Next up is Chris' frustration with small brake bleeder fittings, and we learn that he just needs better tools. Ray talks about a custom ratchet MatcoMan Brian made for him but Chris ain't buyin' it! A new tilt steering column & custom bucket seats are up next for the GTO to complement its new paintjob.

Today, we go deep on swimming but keep it relatively simple so you can work on things that matter and lock them in. We talk about some of the most important fundamentals regarding swimming: breathing, body position, and body control. There will be big wins in your swimming if you can master the basic principles of swimming. We also get into how you should be breaking up your week regarding form, intensity, and endurance. We'll look at getting a bigger return for your energy investment, why the hips are critical, why you're probably too tense in the water, and the soon to be famous Light Switch Test. Topics: Does op 20 swimming list hold up? Swimming and focus on the details Obsession over aero and watts but not swimming 90 Day Challenge Picking up swimming later in life Correct form + muscular endurance End of Ironman running form Uncomfortable doing hard swims? Efficient form for return of energy investment Day One - Pick one day for technique (AR) Day two - Focus on top speed and turnover Day Three - Longer swim for muscular endurance How to breathe Why to breathe every stroke Breath 2-4 Bilateral breathing?? Build a strong side Body position Head is number one Forehead down Body Control Head raises - body sinks A body drill with paddles Outside cues to get a better feel for the water Video from the front cue Splayed out? Hips way too open Hip control Steering wheels in swimming The Light Switch Test   Mike Tarrolly - mike@c26triathlon.com Robbie Bruce - robbie@c26triathlon.com

It's the beginning of the year. Would you like to lose a few stubborn pounds that you may have picked up over the holidays? Have you tried to eat less and work out more? We know how to lose weight, but we still don't do what we know. Why not? Well, according to our guest today, expert life coach Martha Beck, it's because we don't understand the brain-body dynamics of weight loss. Today Martha teaches us that we have to get lean from the brain outward.   In her book "The Four-Day Win" she tells us how to reverse the brain-body programming that makes us overweight, so we can create a new, leaner, and healthier body…for good. Psychology Today, USA Today, and NPR have all referred to Martha Beck as "one of the best-known life coaches in America." She is a monthly columnist for "O," the Oprah Magazine, has been a contributing editor for Real Simple, Redbook, and Mademoiselle, and has written for many other national magazines. She appears frequently as an expert on "life design," on programs such as Good Morning America.  She is the author of "Expecting Adam," "Steering by Starlight," "Finding Your Way in a Wild New World," and more. Info: https://marthabeck.com/

The source of Biblical wisdom is Jesus Christ, and He will direct the life of those who faithfully follow Him.

Have you ever taken a long trip, listened to the GPS on your car, and then asked, “Where am I?”  Modern global positioning systems are pretty good.  But have you ever wondered why you're told to turn off the highway and take what seems like some goat path along the way?  Your life may feel this way sometimes.  But what took place in Joseph's life reminds us it's God who leads and, in faith, we are blessed to follow.

Steering a company from nascent idea to multi-million dollar revenue is rarely a smooth ride. Often, it's a journey fraught with uncertainty, tough decisions, and the internal battles no one sees. For Nathan, the guiding hand through much of this journey has been his coach, Dan Putt. As a co-founder of Reboot, Dan has spent years working with leaders and executives, helping them navigate growth without losing themselves in the process. In this episode, Dan shares not only his unique coaching philosophy - which emphasizes a deep, inside-out understanding of oneself, but also practical tools and frameworks that address common founder sticking points like imposter syndrome, the fear of conflict, and the allure of the "magic bullet" solution. Get ready to rethink your approach to leadership and personal development.Timestamps:00:00 Introduction02:23 Dan's coaching philosophy04:36 The Greek God CEO and imposter syndrome07:11 The "have to" versus "get to" reframe10:00 What problems say about identity12:56 Listening to understand: Beyond the words15:46 Inside-out leadership development18:31 The challenge of competitive responses21:52 Journaling for self-discovery25:21 Practical tips for consistent journaling28:11 Getting clear on what you truly want30:26 The "loyal soldier" concept33:53 How personal traits drive ambition38:39 The shift from "need" to "want"41:43 Conflict as progress and care45:34 Tools for navigating conflict50:47 The "net" framework for communication52:23 Avoiding problems vs. facing them55:29 The temptation of the "magic bullet"59:08 Feeling the fear without dwelling1:01:21 The tantruming toddler metaphor for anxiety1:03:00 Leading with curiosity, not fearIf you enjoyed this episode, please like and subscribe, share it with your friends, and leave a review. I read every single one.Learn more about the podcast: https://nathanbarry.com/showFollow Nathan:Instagram: https://www.instagram.com/nathanbarryLinkedIn: https://www.linkedin.com/in/nathanbarryX: https://twitter.com/nathanbarryYouTube: https://www.youtube.com/@thenathanbarryshowWebsite: https://nathanbarry.comKit: https://kit.comFollow Dan:Website: https://danputt.comLinkedIn: https://www.linkedin.com/in/danputtX: https://twitter.com/danputtCompany Website: https://reboot.ioFeatured in this episode:Kit: https://www.kit.comReboot.io: https://reboot.ioThe Artist's Way by Julia Cameron: https://juliacameronlive.com/books/the-artists-way750words.com: https://750words.comThe Obstacle Is the Way by Ryan Holiday: https://ryanholiday.com/books/the-obstacle-is-the-wayExtreme Ownership by Jocko Willink: https://echelonfront.com/Extreme-OwnershipHighlights:02:47 Understanding the spectrum of coaching approaches08:24 The danger of linking self-worth to problems13:44 The wisdom found at the emotional level23:25 The clarifying power of daily journaling34:05 How the "loyal soldier" shapes our drives45:51 Why true care often requires conflict53:49 Facing uncomfortable feelings builds resilience1:01:05 Approaching anxiety like a tantruming child1:03:54 Shifting from fear to wonder for better leadership

Dr. Douglas Kupas joins Rob Lawrence to kick off EMS One-Stop in 2026, reflecting on his first year as President of NAEMSP — a year he describes as fast-moving, complex and occasionally “whack-a-mole,” with emerging issues demanding real-time leadership while long-term priorities still had to move forward. He shares what he's learned about the presidency, the value of NAEMSP's leadership “bench strength,” and why advocacy and coalition-building across national EMS organizations has become more coordinated, more strategic and more essential. The conversation then turns to what's immediately ahead: the NAEMSP Annual Meeting in Tampa (late January), including pre-conference courses, the flagship Medical Director's Course, and a packed scientific program. Kupas highlights a keynote focused on transforming battlefield trauma care; major research programming through oral abstracts and hundreds of posters; and high-impact sessions spanning clinical care, operations, legal issues, and international perspectives — reinforcing why the Tampa meeting remains a must-attend event for anyone serious about the science and future of EMS. Episode timeline 00:00 – Rob tees up NAEMSP Annual Meeting growth as a “good problem to have” 00:50 – Welcome/Happy New Year 2026; Dr. Kupas introduced as first guest of the year 01:45 – Year one as NAEMSP president: what's surprised Dr. Kupas, pace of work, governance “bench strength” 04:26 – NEMSAC termination: what happened, what NAEMSP hopes comes next 07:02 – Building the pipeline: medical student/resident interest group, travel support ideas 08:47 – “Hot off the press:” NAEMSP accepted into WHO Acute Care Action Network 10:08 – Advocacy “hunting as a pack:” overlapping national orgs, EMS on the Hill coordination 12:40 – Why Hill visits work: stories, staffers and why first-timers matter 16:48 – “White hat” advocacy and patient-centered priorities; ED wall time as a key issue 20:07 – Tampa preview: “It's not just for docs,” NAEMSP membership structure 22:11 – Pre-cons overview: Medical Director's Course, QI workshop, MIH, ventilation, blood, TECC 23:55 – Keynote: Dr. Frank Butler and special intro by Dr. Bob Mabry; Grand Rounds obstetric focus 27:45 – Major legal session format and why legal content draws a crowd 29:28 – Space constraints and future planning: small convention centers; San Diego “buyout” scale 31:49 – Research explosion: oral abstracts, posters, receptions; better ways to access abstracts 34:39 – “Meat of the conference:” operations, clinical topics, international speakers/learning 36:49 – Closing question: Bill details Enjoying the show? Email editor@ems1.com to share feedback or suggest guests for a future episode. 

Host Angel Santana challenges listeners to quit passive scrolling and take control of their attention with a simple 10-minute no-phone reset. Reflect on what you truly want for 2026—peace, purpose, and growth—rather than chasing likes or appearances. Rather than more motivation, aim for clearer direction: stop letting your attention be stolen, set intentional goals, and move forward with faith.

John Tamny, author of Deficit Delusion, editor of RealClearMarkets, and president of the Parkview Institute, joins Steve to challenge one of Washington's most sacred assumptions, that the Federal Reserve “steers” the U.S. economy. Tamny explains why real economic growth has always come from entrepreneurs, capital formation, and market signals, not central bankers, and why both the Left and Right fundamentally misunderstand national debt, deficits, and monetary policy. If the Fed isn't in control, what really powers growth, and what does that mean for America's economic future?

Lloyd Blankfein never chased a master plan. He focused on whatever was right in front of him, and those small decisions carried him from a Brooklyn housing project to leading Goldman Sachs through the worst financial crisis since the Great Depression.In this episode of Big Shot, Harley and David sit down with Lloyd to explore how that path unfolded. He talks about growing up in public housing and sharing a room with his grandmother, then suddenly finding himself at Harvard at 16, arriving in a suit because he had no idea what college culture looked like. He reflects on the dislocation of moving between the projects and the Ivy League and how he learned to navigate both worlds without ever feeling fully at home in either.Lloyd traces his shift from law to commodities, what he absorbed inside J. Aron, and how a crisis inside Goldman in the 1980s reshaped the firm and opened unexpected doors. He also shares what it was like to lead Goldman Sachs through 2008, why Warren Buffett's support mattered at a defining moment, and what it took to keep the firm intact while the global financial system was breaking apart.It is a conversation about chance, focus, resilience, and the surprising places a life can go when you simply take the next step.—In This Episode We Cover:(00:00) Intro(05:15) Lloyd's early days(07:05) How Lloyd graduated early (08:53) How Lloyd ended up at Harvard at 16 (10:56) A glimpse at just how humble his beginnings truly were(13:42) What it was like arriving at Harvard with no roadmap(19:37) Why top public-university talent can match (and sometimes surpass) the Ivies(20:27) What it was like moving between worlds (25:05) Why it took a long time to adjust to the burden of great wealth (27:11) What led Lloyd to law school(28:48) Lloyd's approach of thinking one step ahead(30:35) Why Lloyd quit practicing law (35:16) Lloyd's pivot to finance and initial rejection from Goldman Sachs(41:00) The J. Aron role that pulled Lloyd into Goldman (49:30) Inside the meritocracy of Goldman Sachs (53:08) How Lloyd ended up making partner at Goldman Sachs unexpectedly(1:02:30) Building trust across cultures (1:06:52) What changed after making partner (1:10:10) What sparked Lloyd's retirement and renewed focus on learning(1:14:42) How the 1994 crisis set the stage for Lloyd to become CEO(1:22:00) Steering the firm through the 2008 financial crisis(1:28:22) The deal with Warren Buffett (1:37:58) Risk-taking vs. risk management (1:39:04) How anxiety fuels Lloyd's risk management style (1:42:00) Lloyd's biggest accomplishment at Goldman Sachs (1:46:21) A case for self-acceptance —Where To Find Lloyd Blankfein: • X: https://x.com/lloydblankfeinWhere To Find Big Shot: • Website: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.bigshot.show/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@bigshotpodcast⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  • TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.tiktok.com/@bigshotshow⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/bigshotshow/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  • Harley Finkelstein: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/harleyf⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ • David Segal: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://twitter.com/tea_maverick⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠• Production and Marketing: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co⁠

Kyle “caboose” Waters of Cyber Unicorns enlightens this extended episode! Having a combined passion for people and security, Kyle demonstrates how to effectively educate the masses. Steering away from typical training and using unique approaches, this talk explores how to break through the barriers that create vulnerabilities. Like, subscribe, and share with your network to stay informed about the latest in cybersecurity! We want to hear from you! Reach out at unsecurity@frsecure.com and follow us for more: LinkedIn: https://www.linkedin.com/company/frsecure/ Instagram: https://www.instagram.com/frsecureofficial/ Facebook: https://www.facebook.com/frsecure/ BlueSky: https://bsky.app/profile/frsecure.bsky.social About FRSecure: https://frsecure.com/ FRSecure is a mission-driven information security consultancy headquartered in Minneapolis, MN. Our team of experts is constantly developing solutions and training to assist clients in improving the measurable fundamentals of their information security programs. These fundamentals are lacking in our industry, and while progress is being made, we can't do it alone. Whether you're wondering where to start or looking for a team of experts to collaborate with you, we are ready to serve.

In this episode, host Clay Edwards addresses misconceptions about marijuana use and driving through personal anecdotes and listener interactions. Responding to a texter's claim that "pothead geniuses" believe marijuana users "don't get out and drive" and just "sit at the house and chill," Edwards shares a story of a FedEx driver who delivered a package while reeking of marijuana, confirming that users do drive under the influence. He humorously admits to once priding himself on being a "really good high driver" in his youth, though he notes it now causes him anxiety. Listener Cheyenne recounts losing her husband to a drug-impaired driver, emphasizing the dangers and irresponsibility of driving high. Edwards also discusses the strong odor of modern marijuana detectable even from vehicles on the road, advocating against public consumption while supporting reclassification, and ties it to broader debates on drug myths and safety.

In this episode of French Insider, Melissa Hughes, a senior associate in Sheppard Mullin's Labor and Employment Practice Group and member of the French Desk, joins us to explore the use of AI for automated decision-making throughout the employment life cycle, including the associated risks and how they can be mitigated.   What we discussed in this episode:  How does AI interact with the workplace? From an employment perspective, where does AI carry the most risk? Why is the use of AI in employment decisions particularly concerning? How can employers mitigate the risks associated with AI tools? What should employers consider when selecting an AI tool? Does the U.S. have any AI regulations comparable to the E.U.'s AI Act?  What U.S. trends should employers be aware of? What advice would you give companies as they roll out AI tools or increase the use of AI to do business? Disclaimer: This episode was recorded prior to the signing of Executive Order 14365, "Ensuring a National Policy Framework for Artificial Intelligence." As a result, some discussions may not reflect the policies or guidance established by this order.   About Melissa Hughes As a senior associate in the Labor and Employment Practice Group in Sheppard Mullin's San Francisco office, Mellissa Hughes defends and counsels employers in a range of disputes, involving harassment, discrimination, retaliation, failure to accommodate, wrongful termination, wage and hour claims, PAGA actions, and class actions. She also has traditional labor law experience, including arbitration, unfair labor practice proceedings, and litigation under the National Labor Relations Act. Melissa represents employers of all sizes in state and federal courts, administrative proceedings, and every phase of litigation, from pre-suit strategy through post-trial motions. She also serves as a trusted advisor on day-to-day workplace issues, including disability accommodations, leaves of absence, performance management, workplace investigations, and compliance with California's complex wage and hour laws. As a member of Sheppard Mullin's French Desk, Melissa advises French companies and groups operating in or expanding to the U.S. on a full range of employment and personnel matters in both French and English.   About Inès Briand Inès Briand is an associate in Sheppard Mullin's Corporate Practice Group and French Desk Team in the firm's Brussels office, where her practice primarily focuses on domestic and cross-border mergers and acquisition transactions (with special emphasis on operations involving French companies). She also has significant experience in general corporate matters and compliance for foreign companies settled in the United States. As a member of the firm's French Desk, Inès has advised companies and private equity funds in both the United States and Europe on mergers and acquisitions, commercial contracts, and general corporate matters, including the expansion of French companies in the United States.   Contact Information Mellissa Hughes Inès Briand    Thank you for listening! Don't forget to SUBSCRIBE to the show to receive every new episode delivered straight to your podcast player every week. If you enjoyed this episode, please help us get the word out about this podcast. Rate and Review this show in Apple Podcasts, Deezer, Amazon Music, or Spotify. It helps other listeners find this show. This podcast is for informational and educational purposes only. It is not to be construed as legal advice specific to your circumstances. If you need help with any legal matter, be sure to consult with an attorney regarding your specific needs.

"Healthcare has had no star score, there's been no reliable pricing. It's all been just a black box." - Will BruhnAn estimated 21% of all medical care in the U.S. is completely unnecessary. My guest this week is Will Bruhn, CEO and Co-Founder of GAM (Global Appropriateness Measures), and he joins the show to explain exactly where that waste comes from and how we can fix it.Will argues that we've been measuring quality wrong. Instead of just looking at outcomes (did the surgery go well?), we need to measure appropriateness (should the surgery have happened at all?). We discuss the perverse financial incentives driving this waste, using real-world examples like unnecessary spinal fusions and the statistical spike in C-sections on Friday afternoons.But this episode isn't just about the problem; it's about the fix. Will breaks down how GAM uses data to identify high-value providers and how employers can redesign plans to guide members away from wasteful care, potentially saving millions while protecting patients from unnecessary risks.Tune in for a blueprint on eliminating the 21% of healthcare spending that shouldn't exist.Chapters:(00:00:00) 21% Of Medical Care Is Wasteful. Here's How We Prevent It.(00:00:34) Why 21% of Medical Care is Wasteful (00:01:57) The Missing Metric: Appropriateness of Care (00:08:29) How Financial Incentives Drive Bad Medicine (00:17:02) Case Study: Unnecessary Spine Surgeries (00:24:06) How We Fix It: Identifying the Right Doctors (00:31:22) Changing Plan Design to Stop the Waste (00:33:52) The ROI of Steering to High-Value Care (00:48:44) The Debate on Government Regulation & Drug Prices (00:53:31) The Future of Healthcare TransparencyKey Links for Social:@SelfFunded on YouTube for video versions of the podcast and much more - https://www.youtube.com/@SelfFundedListen/watch on Spotify - https://open.spotify.com/show/1TjmrMrkIj0qSmlwAIevKA?si=068a389925474f02Listen on Apple Podcasts - https://podcasts.apple.com/us/podcast/self-funded-with-spencer/id1566182286Follow Spencer on LinkedIn - https://www.linkedin.com/in/spencer-smith-self-funded/Follow Spencer on Instagram - https://www.instagram.com/selffundedwithspencer/

On this episode of The Movie Podcast, we're joined by Director Joseph Kosinski to discuss Apple Original Films' “F1” which makes its global streaming debut today on Apple TV. Dubbed “the greatest that never was,” Sonny Hayes (Brad Pitt) was FORMULA 1's most promising phenom of the 1990s until an accident on the track nearly ended his career. Thirty years later, he's a nomadic racer-for-hire when he's approached by his former teammate Ruben Cervantes (Javier Bardem), owner of a struggling FORMULA 1 team that is on the verge of collapse. Ruben convinces Sonny to come back to FORMULA 1 for one last shot at saving the team and being the best in the world. He'll drive alongside Joshua Pearce (Damson Idris), the team's hotshot rookie intent on setting his own pace. But as the engines roar, Sonny's past catches up with him and he finds that in FORMULA 1, your teammate is your fiercest competition — and the road to redemption is not something you can travel alone. APXGP Team Race Car from F1® The Movie Watch and listen to The Movie Podcast now on all podcast platforms, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TheMoviePodcast.ca⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Contact: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠hello@themoviepodcast.ca⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠FOLLOW US⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Daniel on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Letterboxd⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Shahbaz on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Letterboxd⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Anthony on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Letterboxd⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ The Movie Podcast on ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Discord⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rotten Tomatoes Learn more about your ad choices. Visit megaphone.fm/adchoices

Send us a textReah Smith joins me on Dishin' Dirt to discuss the ongoing issue of commission steering in real estate, its implications following the Sitzer/Burnett settlement, and the ethical considerations surrounding compensation structures. We explore the importance of buyer agency agreements, the role of bonuses in transactions, and the need for a cultural shift within the industry. Our conversation emphasizes the necessity for training and education for real estate agents to navigate these changes effectively and the potential future of compensation models in real estate.Don't forget to like us and share us!Gary* Gary serves on the South Carolina Real Estate Commission as a Commissioner. The opinions expressed herein are his opinions and are not necessarily the opinions of the SC Real Estate Commission. This podcast is not to be considered legal advice. Please consult an attorney in your area.

Welcome back to Everyone Racers, the podcast for weekend warriors, budget-built race cars, wrench-turning heroes, and anyone who loves the chaos of amateur motorsports. Episode #415 is a wild ride packed with racing news, wrenching disasters, automotive comedy, and deep-in-the-paddock storytelling every grassroots racer will appreciate.In this Steering wheel Ep 415, Tim's fancy truck makes him do his own driving, like a sucker! Chris hangs pictures, Chrissy got a new helmet while Mental breaks down and buys a Hyundai.Really this week the gang talks about everything from golfing at Cypress Point, DIY sink disasters, buying a 2025 Toyota Tacoma, new helmets, Christmas trees, and the eternal struggle of traffic-assist systems that swear they see ghosts in the fog. We also dive into the hilarious chaos of a Waymo self-driving car wandering into a felony stop, Hyundai recalling only its silver cars, and a Tesla showing “people” in a cemetery at night. Then it's onto real racing:

The holidays aren't just for catching up, they're a strategic opportunity to accelerate your goals. But many conversations stay frustratingly surface-level, leaving us drained rather than energized.Gayle Lantz shares why talking about your aspirations out loud during this season could be the most powerful action you take for the year ahead. As she explains, "What you focus on grows. What you talk about grows even faster."Find the full show notes at:https://workmatters.com/Voice-Your-Vision---Steering-Conversations-to-Achieve-Your-Biggest-Goals

In this episode you will hear: Mark and Hilary answer a question about submitting the science portion of the SAT New Interview-Evan Mandery-How America's Elite Colleges Breed High-Status Careers—and Misery Preview of Part 2 of 3 m Evan distinguishes his scathing criticisms of elite schools, and how he differentiates between the colleges that are steering kids to 3 professions vs the students who end up in these professions m Evan talks about how Ed Blum is now going after legacy preferences, and he briefly talks about class-based legacy preferences m Evan and I go off topic and talk about anti-Semitism on college campuses m We talk about whether donor preferences are valid because they lead to increased generosity, which enables a college to support more under-resourced students m Evan gives advice to parents who are listening to help them to help their student from falling into the trap that they need to either go into a wall street job, a tech job or management consulting m Evan, talks about the value of starting your career with service m Evan and Mark have a debate about how much it matters where you go to college Recommended Resources Colleges that allow self reporting of test scores Colleges that Allow Self-Reporting of SAT and ACT Scores Great source for questions about finances and college Edvisors: Financial Aid, Student Loans, Scholarships and Money Management FAFSA Walkthroughs Mark recommends Complete FAFSA 2026‑2027 Walkthrough | From Start to Submit 2023-2024 FAFSA Walkthrough Video English CSS PROFILE Walkthroughs CSS Profile Walkthrough MEFA Institute: A Deep Dive into the CSS Profile Speakpipe.com/YCBK is our method if you want to ask a question and we will be prioritizing all questions sent in via Speakpipe. Unfortunately, we will NOT answer questions on the podcast anymore that are emailed in. If you want us to answer a question on the podcast, please use speakpipe.com/YCBK. We feel hearing from our listeners in their own voices adds to the community feel of our podcast. You can also use this for many other purposes: 1) Send us constructive criticism about how we can improve our podcast 2) Share an encouraging word about something you like about an episode or the podcast in general 3) Share a topic or an article you would like us to address 4) Share a speaker you want us to interview 5) Leave positive feedback for one of our interviewees. We will send your verbal feedback directly to them and I can almost assure you, your positive feedback will make their day. To sign up to receive Your College-Bound Kid PLUS, our new monthly admissions newsletter, delivered directly to your email once a month, just go to yourcollegeboundkid.com, and you will see the sign-up popup. We will include many of the hot topics being discussed on college campuses. Check out our new blog. We write timely and insightful articles on college admissions: https://yourcollegeboundkid.com/category/blog/ On X for our podcast: https://twitter.com/YCBKpodcast 1. To access our transcripts, click: https://yourcollegeboundkid.com/category/transcripts/ 2. Find the specific episode transcripts for the one you want to search and click the link 3. Find the magnifying glass icon in blue (search feature) and click it 4. Enter whatever word you want to search. I.e. Loans 5. Every word in that episode when the words loans are used, will be highlighted in yellow with a timestamps 6. Click the word highlighted in yellow and the player will play the episode from that starting point 7. You can also download the entire podcast as a transcript We would be honored if you will pass this podcast episode on to others who you feel will benefit from the content in YCBK. Please follow our podcast. It really helps us move up in Spotify and Apple's search feature so others can find our podcast. If you enjoy our podcast, would you please do us a favor and share our podcast both verbally and on social media? We would be most grateful! If you want to help more people find Your College-Bound Kid, please make sure you follow our podcast. You will also get instant notifications as soon as each episode goes live. Check out the college admissions books Mark recommends: https://yourcollegeboundkid.com/recommended-books/ Check out the college websites Mark recommends: https://yourcollegeboundkid.com/recommended-websites/ If you want to have some input about what you like and what you recommend, we change about our podcast, please complete our Podcast survey; here is the link: https://docs.google.com/forms/d/e/1FAIpQLScCauBgityVXVHRQUjvlIRfYrMWWdHarB9DMQGYL0472bNxrw/viewform If you want a college consultation with Mark just text Mark at 404-664-4340 or email at mark@schoolmatch4u.com. All we ask is that you review their services and pricing on their website before the complimentary session; here is link to their services with transparent pricing: https://schoolmatch4u.com/services/compare-packages/

Imagine sitting in the driver’s seat of your parked car. And you start disassembling your steering wheel. You loosen the nut that holds your steering wheel in place. Now, the steering wheel is barely connected to your front wheels. Are you willing to hit the open highway without that control? Only if you like ditches. Today on A NEW BEGINNING, from the series Heroes of the Bible, Pastor Greg Laurie points out some people choose to do that with their very lives . . . and wind up in some kind of ditch. — Become a Harvest Partner today and join us in knowing God and making Him known through media and large-scale evangelism, our mission of over 30 years. Explore more resources from Pastor Greg Laurie, including daily devotionals and blogs, designed to answer your spiritual questions and equip you to walk closely with Christ.Support the show: https://bit.ly/anbsupportSee omnystudio.com/listener for privacy information.

Imagine sitting in the driver’s seat of your parked car. And you start disassembling your steering wheel. You loosen the nut that holds your steering wheel in place. Now, the steering wheel is barely connected to your front wheels. Are you willing to hit the open highway without that control? Only if you like ditches. Today on A NEW BEGINNING, from the series Heroes of the Bible, Pastor Greg Laurie points out some people choose to do that with their very lives . . . and wind up in some kind of ditch. — Become a Harvest Partner today and join us in knowing God and making Him known through media and large-scale evangelism, our mission of over 30 years. Explore more resources from Pastor Greg Laurie, including daily devotionals and blogs, designed to answer your spiritual questions and equip you to walk closely with Christ.Support the show: https://bit.ly/anbsupportSee omnystudio.com/listener for privacy information.

Welcome back to the Everyone Racers Podcast, where low-buck motorsports, strange car culture, & a questionable amount of mechanical judgment collide at high speed. Whether you wrench, race, break stuff, or just laugh at people who do… you're home.In this Steering wheel Ep 412, Tim depletes California's wine supply, Chris has a tiny hammer, Chrissy can't organize her tools , Mental shorts oil futures & drools over ‘Renolls.'Really we talk about all of the great gifts to get & give this holiday season.Also, • Mental's cross-country bread van disaster story — including turbo failure, oil consumption measured in gallons, & the realization mid-drive that the heater doubles as a carbon monoxide delivery system. • Terrible holiday gifts for car people, & the “Gator Grip” socket from hell. • The crew's Thanksgiving adventures, garage disasters, home repairs, & smoking meats like true race-team dads. • A new Renault auction full of bizarre French creations • Terrible tools, presents & why British cars deserve sympathy when they're actually running.If you're into:

In this episode you will hear: Mark and Vince talk about a range of topics-Part 2 of 2 New Interview-Evan Mandery-How America's Elite Colleges Breed High-Status Careers—and Misery Preview of Part 1 of 3 m Evan gives his background m Evan talks his book Poison Ivy and he tells us how it lead to a non-profit organization, called, "classactionorg" that fights legacy preferences, for great educational equality on elite campuses m Evan gives an overview of the article that will be the basis of our interview; it was written in Mother Jones, and it is entitled, How America's Elite Colleges Breed High Status Careers and Misery m Evan discusses why we don't teach, how to live a happy life Recommended Resources Colleges that allow self reporting of test scores Colleges that Allow Self-Reporting of SAT and ACT Scores Great source for questions about finances and college Edvisors: Financial Aid, Student Loans, Scholarships and Money Management FAFSA Walkthroughs Mark recommends Complete FAFSA 2026‑2027 Walkthrough | From Start to Submit 2023-2024 FAFSA Walkthrough Video English CSS PROFILE Walkthroughs CSS Profile Walkthrough MEFA Institute: A Deep Dive into the CSS Profile Speakpipe.com/YCBK is our method if you want to ask a question and we will be prioritizing all questions sent in via Speakpipe. Unfortunately, we will NOT answer questions on the podcast anymore that are emailed in. If you want us to answer a question on the podcast, please use speakpipe.com/YCBK. We feel hearing from our listeners in their own voices adds to the community feel of our podcast. You can also use this for many other purposes: 1) Send us constructive criticism about how we can improve our podcast 2) Share an encouraging word about something you like about an episode or the podcast in general 3) Share a topic or an article you would like us to address 4) Share a speaker you want us to interview 5) Leave positive feedback for one of our interviewees. We will send your verbal feedback directly to them and I can almost assure you, your positive feedback will make their day. To sign up to receive Your College-Bound Kid PLUS, our new monthly admissions newsletter, delivered directly to your email once a month, just go to yourcollegeboundkid.com, and you will see the sign-up popup. We will include many of the hot topics being discussed on college campuses. Check out our new blog. We write timely and insightful articles on college admissions: https://yourcollegeboundkid.com/category/blog/ On X for our podcast: https://twitter.com/YCBKpodcast 1. To access our transcripts, click: https://yourcollegeboundkid.com/category/transcripts/ 2. Find the specific episode transcripts for the one you want to search and click the link 3. Find the magnifying glass icon in blue (search feature) and click it 4. Enter whatever word you want to search. I.e. Loans 5. Every word in that episode when the words loans are used, will be highlighted in yellow with a timestamps 6. Click the word highlighted in yellow and the player will play the episode from that starting point 7. You can also download the entire podcast as a transcript We would be honored if you will pass this podcast episode on to others who you feel will benefit from the content in YCBK. Please follow our podcast. It really helps us move up in Spotify and Apple's search feature so others can find our podcast. If you enjoy our podcast, would you please do us a favor and share our podcast both verbally and on social media? We would be most grateful! If you want to help more people find Your College-Bound Kid, please make sure you follow our podcast. You will also get instant notifications as soon as each episode goes live. Check out the college admissions books Mark recommends: https://yourcollegeboundkid.com/recommended-books/ Check out the college websites Mark recommends: https://yourcollegeboundkid.com/recommended-websites/ If you want to have some input about what you like and what you recommend, we change about our podcast, please complete our Podcast survey; here is the link: https://docs.google.com/forms/d/e/1FAIpQLScCauBgityVXVHRQUjvlIRfYrMWWdHarB9DMQGYL0472bNxrw/viewform If you want a college consultation with Mark just text Mark at 404-664-4340 or email Lisa at lisa@schoolmatch4u.com. All we ask is that you review their services and pricing on their website before the complimentary session; here is link to their services with transparent pricing: https://schoolmatch4u.com/services/compare-packages/

Tyler tells everyone about all the issues he had with the Mule's power steering. Jimmy even got a chance to drive the Mule and experience how bad the steering and handling were. Jimmy wasn't able to do any additional work on Samatha since Tuesday. However, he did manage to get some much-needed yard work done. MORRFlate Giveaway at 900 Reviews on Apple Podcast. But our next giveaway is when we reach 800 reviews; we are giving away an OnX Elite Membership. We will also give away an OnX Elite membership when we get to 850. However, when we reach 900 Reviews, we are teaming up with MORRFlate for a $1000 MF Product Giveaway. Go over to Apple Podcasts to leave your review now and become eligible to win. Congratulations to A13XMONT, who won a set of tires from Yokohama Tire! Call us and leave us a VOICEMAIL!!! We want to hear from you even more!!! You can call and say whatever you like! Ask a question, leave feedback, correct some information about welding, say how much you hate your Jeep, and wish you had a Toyota! We will air them all, live, on the podcast! +01-916-345-4744. If you have any negative feedback, you can call our negative feedback hotline, 408-800-5169. 4Wheel Underground has all the suspension parts you need to take your off-road rig from leaf springs to a performance suspension system. We just ordered our kits for Kermit and Samantha and are looking forward to getting them. The ordering process was quite simple, and after answering the questionnaire, we ensured we got the correct and best-fitting kits for our vehicles. If you want to level up your suspension game, check out 4Wheel Underground. SnailTrail4x4 Podcast is brought to you by all of our peeps over at irate4x4! Make sure to stop by and see all of the great perks you get for supporting SnailTrail4x4! Discount Codes, Monthly Give-Always, Gift Boxes, the SnailTrail4x4 Community, and the ST4x4 Treasure Hunt! Thank you to all of those who support us! We couldn't do it without you guys (and gals!)! SnailSquad Monthly Giveaway This month's Giveaway is from someone very close to us, MORRFlate! MORRFlate is giving away one of its new Xtreme Duty Braided Hose Kits. These kits are an upgrade from their standard quad kits. These have a 1200 D nylon weave on top of the upgraded hose. Making them more heat-resistant and having a higher burst rate. One of these can be yours; all you need to do is sign up for the Giveaway Tier on Irate4x4. Congrats to our two winners Evan Cook and Ryan Crutchfield, for winning the October Giveaway. You each won a Devos800 Light Ranger from our wonderful Gift Box. The Gift Boxes are a fun time that happens two times a year in April and October, and this month's Gift Box is one you don't want to miss. Listener Discount Codes: SnailTrail4x4 -SnailTrail15 for 15% off SnailTrail4x4 MerchMORRFlate - snailtraill4x4 to get 10% off MORRFlate Multi Tire Inflation Deflation™ Kits4WheelUnderground - snailtrail 10% offIronman 4x4 - snailtrail20 to get 20% off all Ironman 4x4 branded equipment!Sidetracked Offroad - snailtrail4x4 (lowercase) to get 15% off lights and recovery gearSpartan Rope - snailtrail4x4 to get 10% off sitewideShock Surplus - SNAILTRAIL4x4 to get $25 off any order!Mob Armor - SNAILTRAIL4X4 for 15% offSummerShine Supply - ST4x4 for 10% offBackpacker's Pantry - Affiliate LinkLaminx Protective Films – Use the Link to get 20% off all products (Affiliate Link) Show Music: Outroll Music - Meizong Kumbang Midroll Music - ComaStudio