Podcasts about Weak

BUY CAST BREW COFFEE TO SUPPORT THE SHOW - https://castbrew.com/ Become A Member And Protect Our Work at http://www.timcast.com Host: Tate Brown @realTateBrown (everywhere) Guest: Madison Cawthorn @realcawthorn (X) My Second Channel - https://www.youtube.com/timcastnews Podcast Channel - https://www.youtube.com/TimcastIRL

-- On the Show -- Pam Bondi erupts during a House hearing, redirects questions about Jeffrey Epstein to stock market numbers, clashes with Ted Lieu, and refuses to engage directly with Democratic members -- Jim Jordan says protesters cannot enter the Capitol and disrupt Congress, unintentionally describing the criminal conduct of January 6 rioters whom Donald Trump later pardons -- Donald Trump delivers rambling remarks about wind turbines in China and Europe, praises tariffs as his favorite word, and abruptly ends an event without taking questions after scrutiny of Pam Bondi intensifies -- Donald Trump accepts a “Champion of Beautiful, Clean Coal” trophy amid exaggerated praise, highlighting how flattery and branding around clean coal substitute for substantive energy policy debate -- Six House Republicans including Thomas Massie and Brian Fitzpatrick join Democrats in a 219 to 211 vote to end tariffs on Canadian imports, prompting Donald Trump to threaten primary consequences -- Trump's central promises on immigration, trade, and the economy are facing broad resistance and failing to deliver the stability he promised -- Speaker Mike Johnson dismisses the Pam Bondi hearing as a circus while projecting midterm confidence, reinforcing party loyalty messaging as scrutiny and internal pressure grow -- Joe Rogan and Andrew Schulz criticize the Trump administration's handling of Jeffrey Epstein narratives, signaling potential erosion within culturally influential podcast audiences -- On the Bonus Show: The House passes new voting rules, free speech lawsuits grow over Charlie Kirk commentary, the US military temporarily closed the El Paso airport, and much more...

Today’s Bible Verse: “Love the Lord your God with all your heart and with all your soul and with all your strength.” — Deuteronomy 6:5 Deuteronomy 6:5 calls us to a faith that is not divided. God doesn’t ask for a portion of our lives—He invites our whole heart, soul, and strength. This command reaches beyond feelings into daily choices, priorities, and actions. Meet Today’s Host: Reverend Jessica Van Roekel

What is consciousness — and how should biology explain it?In this second conversation with Professor Kevin Mitchell, we examine whether consciousness can be fully accounted for within physics alone — or whether biological organization introduces new levels of explanation.Mitchell develops a non-reductive naturalist framework in which organisms are genuine agents, higher-level causal structures matter, and subjectivity cannot be ignored in any adequate theory of mind.We explore:• What needs explaining when we talk about consciousness• The limits and strengths of physicalist reduction• Weak vs strong emergence• Biological organization as a causal framework• Downward causation and levels of explanation• Organisms as agents rather than passive mechanisms• The role of the conscious subject• Mental causation and explanatory gaps• Teleology in evolutionary systems• Whether artificial systems could instantiate subjectivityTIMESTAMPS:(0:00) – Introduction(0:32) – Kevin's Approach to Consciousness(1:12) – Consciousness and the Requirement of a Subject(3:59) – AI, Functionalism, & Biological Naturalism(7:37) – Embodiment, In-Mindedness & Experiential Bedrock(11:19) – Control Architectures, Attention, and Illusionism(15:21) – Selfhood Perspectives: Jennings, Graziano & Humphrey(19:08) – Temporal Continuity & Brains as Semantic Engines(23:03) – Top-Down Causation and Dynamical Self(27:00) – Levels of Selfhood & Autobiographical Continuity(30:43) – Neuroscience, Psychiatry & Emergent Mental Phenomena(38:15) – Altered Subjectivity & Embodiment in Injury(44:06) – Life, Consciousness, and AI Agents(50:23) – Philosophy, Science & Indeterminacy(56:28) – Neural Noise, Decision-Making & Agency(1:10:48) – Reasons, Choices & Moral Development(1:20:43) – Emergence, Transcendence & First-Person Neuroscience(1:26:50) – Kantian Structures & Perception(1:30:35) – Defining Mind & Relational Perspectives(1:34:52) – Final ThoughtsEPISODE LINKS:- Kevin's Round 1: https://youtu.be/UdlkYGbuD7Q- Kevin's Website: https://www.kjmitchell.com/- Kevin's Blog: http://www.wiringthebrain.com- Kevin's Books: https://tinyurl.com/2p9yjzxr- Kevin's Publications: https://tinyurl.com/mskdpvce- Kevin's Twitter: https://twitter.com/wiringthebrain- Consciousness needs a subject:https://philpapers.org/rec/MITCNA-2- Reframing the free will debate: the universe is not deterministic:https://link.springer.com/article/10.1007/s11229-026-05455-7- Beyond Mechanism—Extending Our Concepts of Causation in Neuroscience:https://onlinelibrary.wiley.com/doi/abs/10.1111/ejn.70064- Undetermined: Free will in real time and through time:https://dialnet.unirioja.es/servlet/articulo?codigo=10358095- The origins of meaning - from pragmatic control signals to semantic representations:https://osf.io/preprints/psyarxiv/dfkrvCONNECT:- Website: https://mindbodysolution.org - YouTube: https://youtube.com/@mindbodysolution- Podcast: https://creators.spotify.com/pod/show/mindbodysolution- Twitter: https://twitter.com/drtevinnaidu- Facebook: https://facebook.com/drtevinnaidu - Instagram: https://instagram.com/drtevinnaidu- LinkedIn: https://linkedin.com/in/drtevinnaidu- Website: https://tevinnaidu.com=============================Disclaimer: The information provided on this channel is for educational purposes only. The content is shared in the spirit of open discourse and does not constitute, nor does it substitute, professional or medical advice. We do not accept any liability for any loss or damage incurred from you acting or not acting as a result of listening/watching any of our contents. You acknowledge that you use the information provided at your own risk. Listeners/viewers are advised to conduct their own research and consult with their own experts in the respective fields.

Is the lumber market quiet… or is it weak? That's the debate this week on The Lumber Word Podcast. Charles breaks down how SYP is trading — some items are moving, but others are dug in tight. Matt walks through what he's seeing in Texas as activity starts to open up, and why low-grade SPF is creeping closer to #2 in ways that matter. Gregg zooms out to the macro, unpacking monetary supply and asking the big question: what kind of price move would hurt the most people the most?  Ashley connects the dots on what he knows is happening in real-time supply and demand. Did we leave the studio sucking our thumbs… or bulled up and ready?  Crush the follow button and find out. Advertiser Fastmarkets RISI Dustin Jalbert Senior Economist Wood Products djalbert@fastmarkets.com www.fastmarkets.com Show Contacts: Gregg Riley: Gregg@sitkainc.com Charles DeLaTorre: cdelatorre@ifpwood.com Matt Beymer: mattbeymer@hamptonlumber.com Ashley Boeckholt: ashley@sitkainc.com  

Guest: Elizabeth Peek. Peek critiques potential 2028 Democratic candidates, arguing Gavin Newsom's California record and Kamala Harris's past campaign failures make them weak contenders for the presidency.NORMA SHEARER

Today we're joined by the incredible Lysa TerKeurst as she gets real about healing from betrayal, rebuilding trust, and finding hope in dating again after an unwanted divorce! Need 1:1 support for your specific dating situation? Book a coaching call today! https://www.heartofdating.com/coaching  Find out your Dating Personality Type for free by taking our QUIZ here! https://www.heartofdating.com/quiz Join Basics of Dating! The 6-Week Program for the Christian single feeling stuck, anxious, or healing from heartbreak. https://www.heartofdating.com/basics-of-dating   Love Heart of Dating Podcast? Want to support us AND be a part of the fam? Join us on Patreon! https://www.patreon.com/heartofdating Subscribe to our YouTube channel here! https://www.youtube.com/channel/UCJ1PswEXEyeSddMmOSiRKGw Crushing on a cutie? Download this FREE Resource on how to show interest: https://www.heartofdating.com/resource/how-to-show-interest  Want to further your dating knowledge? Check out our ultimate dating library! https://www.heartofdating.com/resource/ultimate-dating-library  Kait wrote a book! Snag Thank You For Rejecting Me on Amazon: https://amzn.to/3E59cLQ Want to meet some epic Christian Singles? Join our huge HOD Family on FB! https://www.facebook.com/groups/heartofdatingpodcast  Come hang with us on the gram: http://instagram.com/heartofdating http://instagram.com/kaitness https://www.instagram.com/jjtomlin/?hl=en Interested in advertising on this show? Learn more here! https://docs.google.com/forms/d/16V_c91F1iIYNZOVvrEinrB9h2dsZq-kZFqYYEDQ4A60/viewform?edit_requested=true  . . . . .  Learn more about your ad choices. Visit megaphone.fm/adchoices

Plus, why did they steer clear of Rhamondre Stevenson? And an all-new Arcand Fire, right here.

I often hear phrases such as "God is bigger than the Bible." Today I'm going to explain why I think we should not say this. There is a sense in which it is true, but typically it is used to subtly lower the authority of the Bible. I hope to convince you today! ► Buy John's book, Wimpy, Weak, And Woke https://a.co/d/j2JNRCV ► Subscribe to the podcast: www.johnlcooper.com  Apple: http://bit.ly/cooperstuff  Spotify: http://bit.ly/cooperstuffspotify  CastBox: http://bit.ly/cooperstuffcast ► Connect with John L. Cooper on Social Media:  https://www.facebook.com/johnlcooperstuff https://www.instagram.com/johnlcooper  https://www.twitter.com/johnlcooper ► Cooper Stuff Merchandise:  https://cooper-stuff.myshopify.com/collections/all

   We use the word "history" way too much in sports media.  But Monday night was historic at Allen Field House over the most dramatic regular season three hours in years.  We got a sad little whiny superstar sitting out sick, a staffer on press row texting that Darryn Peterson said, "Eff it" to playing and a wildly enthusiastic Bill Self after knocking off the number one team in the country.  Oh, this made up for all the lack of fun at Super Bowl 60.   Big 12 commissioner Brett Yormark was at the game and says big changes are coming to the Big 12 tourney.  Are they about to pay players for performance?    Bad Bunny's halftime show would NEVER have been allowed in English, that's for sure.  So why doesn't the FCC  go after the NFL/NBC hard for all the violations in Spanish.  It could be happening.    175,000 KC residents are now eligible to get their Amazon packages delivered by drones from KCK.  Americans gobbled up a record amount of been in 2025.  Kid Rock hits #1 on the charts while a lesser known artist records an anti-Sprinsteen song for the ages.   The Royals do the cheapest thing ever and a small plane lands on traffic filled road in Georgia and the pilot must have been very hungry.

Watch The Full Episode https://youtu.be/5xmHnIlF13cBecome a Member and Give Us Some DAMN GOOD Support :https://www.youtube.com/channel/UCX8lCshQmMN0dUc0JmQYDdg/joinGet your Twins merch and have a chance to win our Damn Good Giveaways! - https://officialhodgetwins.com/Get Optimal Human, your all in one daily nutritional supplement - https://optimalhuman.com/Want to be a guest on the Twins Pod? Contact us at bookings@twinspod.comDownload Free Twins Pod Content - https://drive.google.com/drive/folders/1_iNb2RYwHUisypEjkrbZ3nFoBK8k60COFollow Hodgwtins Podcast Everywhere -X - https://x.com/hodgetwinspodInstagram - https://www.instagram.com/hodgetwinspodcast/Facebook - https://www.facebook.com/thehodgetwinsYouTube - https://www.youtube.com/@HodgetwinsPodcastRumble - https://rumble.com/c/HodgetwinsPodcast?e9s=src_v1_cmdSpotify - https://open.spotify.com/show/79BWPxHPWnijyl4lf8vWVuApple - https://podcasts.apple.com/us/podcast/hodgetwins-podcast/id1731232810

Weak retail numbers sent stocks drifting.

LISTEN and SUBSCRIBE on:Apple Podcasts: https://podcasts.apple.com/us/podcast/watchdog-on-wall-street-with-chris-markowski/id570687608 Spotify: https://open.spotify.com/show/2PtgPvJvqc2gkpGIkNMR5i WATCH and SUBSCRIBE on:https://www.youtube.com/@WatchdogOnWallstreet/featured  Ahead of the January jobs report, officials are already moving the goalposts—arguing that weak job growth isn't really weak and that 50,000 jobs a month should now be considered “great,” supposedly due to deportations.That explanation doesn't hold up. If millions left the workforce, job openings should surge. Instead, hiring is frozen, small businesses are squeezed by tariffs, young grads can't find work, and companies are investing in tech over people.Weak jobs numbers are a real problem—and redefining them doesn't fix the economy.

Some are haunted with the hideous nature of their imaginations and with wicked and unworthy thoughts of God, Christ, and the Word, which, as busy flies, disquiet and assault their peace. These are cast in a wildfire by Satan, which can be seen in the strangeness, the strength and violence, and the horrible nature of these imaginations, even to those who are corrupt. A virtuous soul is no guiltier of them than Benjamin was when Joseph's cup was put into his sack.

Weak did not mean done   All the links, tools & resources I personally share:https://linktr.ee/cfreecancerfree

Weak grip strength is not a normal part of aging. It is often an early warning sign of metabolic dysfunction. In this episode, Ben explains how issues like insulin resistance, chronic inflammation, mitochondrial decline, and mineral depletion can weaken nerve signaling and muscle function, especially in the hands. Grip strength is one of the strongest predictors of longevity, independence, and overall metabolic health. You'll learn why people can look fit yet still struggle with weak hands, cramping, or fatigue, and how these symptoms often appear years before serious disease. Ben breaks down the four main root causes behind grip weakness and shares a simple daily protocol to restore strength. The episode covers insulin resets, nutrition strategies, mineral support, protein intake, grip training, and the role of creatine in improving strength and muscle signaling. Ben also explains why chronic stress and inflammation accelerate metabolic decline and how gratitude can positively impact metabolism. This episode offers practical, actionable steps you can start today to rebuild grip strength, improve energy, and protect long-term metabolic health. CLICK To Get Your Creatine - MYOXCIENCE Use Coupon Code "FREEDOM" for 25% off - https://bit.ly/4qTgABP 

"Do you need a non-stop music mix to get you through your day, week, workout, or commute? Do you enjoy listening to House Music from Around the World? Do you appreciate a variety of musical styles, flavors, and one-of-a-kind edits/remixes? Press play and enjoy!" - DJ MIDIMACKEp 263 MIDI's FUNKY FAVs #9 (Pt. 1 of 3) Playlist:Disco Down 2019 by House of Glass (Ep 86)Weak by Maverick Sabre/Tom Breu/Vintage Culture (Ep 249)Easy by David Morales/Joe Roberts (Ep 255)Mauritius by Jazz Mango (Ep 249)Sun Rising Up by Deux feat. Rebeka Brown (Ep 250)1990 by Jack Truant (Ep 252)It's All Right by Promise Land feat. Enlery (Ep 249)I Love You So by Sugar Hill (Ep 250)Beautiful Day by The Shapeshifters feat. Liisi Fontaine (Ep 255)Is It You by Angelo Ferreri/Danmic's (Ep 252)Feet To The Floor by Ivan Pica/SRCS feat. Richard Farrell (Ep 254)To The Bass by Adriano Pepe (Ep 246)Da Phunky by Foo Funkers (Ep 250)So What by Vacuii (Ep 252)Music Saved My Life by Mr. Jay/Joey SLVR (Ep 254)Don't Go by Paco Caniza (Ep 252)Down The Road by Earth N Days (Ep 254)Live My Life by IDA fLO/Ted Funke/Dino DZ (Ep 255)Nightmoves by FDF (Ep 249)Starlightway by Josh Butler/Flashmob (Ep 251)Bullerengue by Low Steppa/Crusy (Ep 250)El Mariachi by Max Komodo (Ep 241)Ride by Ride by Gabriele Ranucci (Ep 253)Get On The Floor by Yvvan Back (Ep 248)Take It To The Rhythm by Eden Prince (Ep 255)Heartquake by L'Imperatrice feat. Cuco (Ep 246)Day By Day by DJ MIDIMACK (Ep 250)Is It A Sin by John Duff (Ep 219)Fall In Love by Toscana (Ep 254)Love Is No Game by HP Vince (Ep...

This week felt like a free fall at times, the kind of tape that makes you question everything, and then the market turned around and finished Friday on a much better note. In this episode, James breaks down what happened, why weeks like this are exactly when investor behavior matters most, and how to keep your decision making grounded when headlines and intraday swings get loud.One of the biggest takeaways is a classic reminder from legendary investor Peter Lynch: the most important organ for an investor is not your brain, it is your stomach. Anyone can find a great company on a green day. The real test is whether you can stick with your plan when markets drop fast, sentiment turns negative, and fear starts writing the narrative.James also asks a tough question: did you sell this week? If the market ended the week barely in the red, or even close to flat, but you reacted like it was the end of the world, it might be worth stepping back and reassessing your approach. Investing is not about guessing the next headline. It is about building conviction in what you own, understanding why you own it, and having a process you can follow when volatility spikes.A key lesson James reinforces is that a company's share price does not tell you how good the company is. Price is simply what buyers and sellers agree on right now. Great businesses can have brutal weeks. Weak businesses can have strong weeks. The job is to separate business quality from market mood.We also talk about a major milestone: the Dow Jones closed above 50,000 for the first time ever, despite an extremely volatile week. That matters because the Dow is not dominated by the same high growth names as the Nasdaq. It can be a signal that money is rotating into more risk aware, steady parts of the market when investors get uneasy.Volatility is part of the story too. Since COVID, big intraday moves, even 2% swings, have become a lot less rare, and James explains why that changes how investors should think about risk, position sizing, and conviction.Then we zoom out to the fear gauge assets: gold and silver, which often get attention when investors are anxious, plus oil prices and what a healthy range can look like for the broader economy.Finally, James shares a potential opportunity in the space theme: the Procure Space ETF, ticker UFO, a basket of companies tied to the space economy. Think satellites, launch providers, and the infrastructure around space based tech. We touch on why this theme is worth watching, and how ETFs like UFO can provide exposure to names people associate with the space race, including holdings like Planet Labs, Rocket Labs, Viasat, Garmin, etc. All Information is educational in its intent and distribution! Please do not consider this personal financial advice. We believe all clients have unique situations and thus require unique advice.

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

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Watch The X22 Report On Video No videos found (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:17532056201798502,size:[0, 0],id:"ld-9437-3289"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="https://cdn2.decide.dev/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs");pt> Click On Picture To See Larger Picture The US Labor market was destroyed by Biden, Trump is reversing everything he has done. US housing market has more sellers than there are buyers, lower rates and 50 year mortgages will fix this. Gold,Silver and Bitcoin are on sale, the masses tend to panic during this period. Bessent breaks the [CB] independence narrative. The [DS] is losing every step of the way. The people are now longer with the D’s. They are now panicking over the midterms and they are messaging that they have plan to do something during this period. Schiff says the quiet part out loud. Trump is setting the stage for their plan for the insurrection. Trump has let the country know that we will find out who actually won the 2020 election. When it is revealed that Trump won, does he get another term?   Economy (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:18510697282300316,size:[0, 0],id:"ld-8599-9832"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="https://cdn2.decide.dev/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs"); https://twitter.com/GlobalMktObserv/status/2019218921950175742?s=20  since the Financial Crisis. The gap suggests workers are taking 2nd and 3rd jobs not by choice but out of necessity, as hours are cut and primary employment fails to provide sufficient income. The job market is WEAK. https://twitter.com/Barchart/status/2019252512013054316?s=20     Bessent Says the President Can Interfere With the Fed  Treasury Secretary Scott Bessent told lawmakers on Wednesday that the president has the right to interfere with the decision-making of the Federal Reserve. Source: barrons.com  the president has the right to verbally and politically interfere with the Federal Reserve’s decision-making. He made this comment in response to questioning from Rep. Emanuel Cleaver (D-Mo.), saying, “It is his right…It is the right of everyone in here,” referring to members of Congress present at the hearing. Political/Rights  https://twitter.com/alexbruesewitz/status/2019226238720831674?s=20 whately https://twitter.com/PoliticalStacy/status/2019217700841726146?s=20 Human Trafficking Crackdown Nets More than 600 Suspects in Sex Trade Authorities in Los Angeles announced Tuesday the results of a statewide crackdown on human trafficking that resulted in the arrests of more than 600 suspects and the rescue of 170 victims, predominantly in the sex trade. The weeklong “Operation Reclaim and Rebuild” campaign was part of a yearly effort by the Los Angeles Regional Human Trafficking Task Force and 80 local, state, and federal law enforcement agencies. Los Angeles County Sheriff Robert Luna laid out the exact numbers at a news conference, later posted on X. A total of 611 criminal arrests were made and 156 adults rescued as part of the operations, Luna told reporters. In addition, 14 children were rescued from sex trafficking. Officials said 71 suspected traffickers were arrested, and an additional 328 sex buyers were arrested. “This is a multibillion-dollar industry,” Los Angeles County District Attorney Nathan Hochman said. “It is nothing less than modern slavery.” According to the Los Angeles Times' reporting of the announcement: Source: breitbart.com Geopolitical Spain Amnesty: Gov't to Take Illegals' Word That They Don't Have Criminal Record The socialist Spanish government's amnesty scheme will allow illegal migrants to simply declare that they have no criminal record, rather than providing documentation from their native countries, sparking concern over criminals gaming the system. Last month, the left-wing coalition government of Socialist PM Pedro Sánchez agreed to allow upwards of half a million illegals seek amnesty and obtain residence permits to remain in Spain. While the scheme stipulates that amnesty will not apply to migrants with criminal records — other than the crime of entering Spain illegally — the regularisation decree published by the government this week revealed that Madrid will essentially be willing to take the word of illegal migrants about their past. Source: breitbart.com https://twitter.com/MarioBojic/status/2019341799148409099?s=20   this is just another step toward killing our freedoms. The EU is an open-air prison and Ursula von der Leyen is the warden. https://twitter.com/MarioNawfal/status/2019395593345393136?s=20   https://twitter.com/visegrad24/status/2019390275924230638?s=20   Kremlin to purchase Russian weapons. In the 2010s, Russia’s largest oil company, Rosneft, became a key lender to Venezuela in exchange for receiving stakes in the country's oil projects. According to Reuters, between 2006 and 2017, the Kremlin provided a total of $17 billion to the Venezuelan government and the state oil company PDVSA. https://twitter.com/visegrad24/status/2019331875572183318?s=20 https://twitter.com/GlobalDiss/status/2019133827453776172?s=20 https://twitter.com/PM_ViktorOrban/status/2019397051612647711?s=20  Brusselian censorship, Orwellian in nature.  3 US Warships Dispatched to Haiti as Part of Campaign Against Drug Traffickers   Three U.S. warships have been sent to Haiti as part of Operation Southern Spear, a military operation in the Caribbean to counter narcotics trafficking. “At the direction of [Secretary of War Pete Hegseth], the ships USS Stockdale, USCGC Stone, and USCGC Diligence have arrived in the Bay of Port-au-Prince as part of Operation Southern Spear,” the U.S. Embassy in Haiti posted on X on Feb. 3. The embassy said the presence of the warships reflects the United States' “unwavering commitment to Haiti's security, stability, and brighter future.” Source: theepochtimes.com https://twitter.com/TheSCIF/status/2018867826459562070?s=20   This is the beginning of the global operation to install these manipulative, backdoor implemented electronic voting machines worldwide to steal elections and install the candidate of their choice. This is the election fraud cartel and its inception. 866 Q !UW.yye1fxo ID: 2362f9 No.568863 Mar 6 2018 13:06:24 (EST) https://wikileaks.org/clinton-emails/emailid/629 So much is open source. So much left to be connected. Why are the children in Haiti in high demand? How are they smuggled out? ‘Adoption' process. Local ‘staging' ports friendly to CF? Track donations. Cross against location relative to Haiti. Think logically. The choice, to KNOW, will be yours. Q 1233 Q !xowAT4Z3VQ ID: 30e575 No.1133862 Apr 21 2018 14:40:05 (EST) Anonymous ID: 03b5fb No.1133796 Apr 21 2018 14:35:58 (EST) america-has-spoken.png >>1133772 THIS IS WHAT THE NEXT 6 YEARS IS ABOUT – THIS QUESTION >>1133796 They will lose black vote once Haiti revealed. Lost now (awakening). They keep them enslaved. What did Hussein do for the black community? vs POTUS? Q War/Peace Medical/False Flags https://twitter.com/EndWokeness/status/2019149006744490427?s=20 https://twitter.com/TheLastRefuge2/status/2019110609145459184?s=20     [DS] Agenda https://twitter.com/AGPamBondi/status/2019443234728989029?s=20   https://twitter.com/nicksortor/status/2019241676490051624?s=20 https://twitter.com/HillaryClinton/status/2019394858767798349?s=20   Control the narrative and turn defense into offense: In a private session, it’s all about dry facts, sworn statements, and transcripts that could be dissected later without my real-time spin. Publicly, it could be framed  as a partisan witch hunt, rally my base, and pivot to attacking the Republicans (like Comer) for hypocrisy or distractions. It’s theater—I’d get soundbites on TV, memes on social media, and maybe even sympathetic coverage from friendly outlets, diluting any real scrutiny.   Closed depositions often drag on for hours with nitpicky details, no time limits, and less grandstanding. In public, time is constrained, questions are performative, and I could filibuster or redirect more easily.  Anything of National Security cannot be discussed and Clinton could hide behind it.  https://twitter.com/CynicalPublius/status/2019169898799259770?s=20   out the part where the Democrats/Hamas initiated the violence. 3. Children are brought to “protests” as human shields. If a child is harmed as his/her parents are engaged in violence, such child is the focus of social media efforts. 4. Rank and file members (useful idiots) are actively encouraged to illegally engage with armed authorities. These are martyrdom operations, and to the extent martyrs are created out of useful idiots, that was always the unstated intent. (But nobody tells the useful idiots that.) 5. Illegal, violent operations are funded by US tax dollars, money laundered through multiple NGOs and non-profits. 6. Laws are irrelevant when they are inconvenient. Laws are ironclad rules when they are convenient. 7. Opponents are dehumanized such that any atrocity that is inflicted on them is justified. 8. A major goal is to sway public opinion on the international stage and create the story that the aggressors are actually the victims. 9. Neither Hamas nor the Democrats can meme effectively. 10. The ultimate goal of both Democrats and Hamas is to create elaborate deception operations as a path to absolute power. President Trump's Plan https://twitter.com/TonySeruga/status/2019235176363212952?s=20 https://twitter.com/RedLineReportt/status/2019175100386267570?s=20   to get TORCHED. For once, the IRS is being deployed FOR AMERICANS FIRST — not against working families. Follow the money. Audit everything. Prosecute whoever broke the law. Thank you, Sec. Bessent.   Do you firmly support Scott on this? A. Huge Yes B. No IF Yes, Give me a THUMBS-UP !! DHS Secretary Noem Identifies Another Leaker and Refers to DOJ for Prosecution  The good news is the process to identify the subversive agents inside the various offices of the administration continues to yield results.   there's a lot of them to identify and remove. Dept of Homeland Security Secretary Kristi Noem shares another leaker has been identified and removed.  Additionally, she is referring their conduct to the Dept of Justice for criminal prosecution. [SOURCE]  The reason for that removal now seems to come to light with the release of letter former Agent Paul Brown sent to Elections Director Nadine Williams giving her a head's-up on the material the FBI was going to seize. FBI Agent Brown asks Ms Williams to voluntarily hand over the material, which has the result of giving Fulton County a heads-up about the specifics of the material the FBI were going to gather and review in their search warrant. Source: theconservativetreehouse.com https://twitter.com/disclosetv/status/2019203189221065004?s=20 Trump is now setting it all up, the people are going to demand he come into the cities and states when the insurrection is happening. optics are important 4360 May 30, 2020 6:11:47 PM EDT Q !!Hs1Jq13jV6 ID: 63d310 No. 9383164  INSURRECTION Act of 1807. [Determination that the various state and local authorities are not up to the task of responding to the growing unrest] Call the ball. Q  https://twitter.com/ElectionWiz/status/2019378085913653512?s=20   https://twitter.com/Rasmussen_Poll/status/2019394557428019374?s=20 https://twitter.com/StephenM/status/1755562105678266707?s=20 https://twitter.com/Breaking911/status/2019257661657633016?s=20   has to happen.” https://twitter.com/TheStormRedux/status/2019184398831100056?s=20 https://twitter.com/Patri0tContr0l/status/2019452836153581799?s=20   they need to figure out other ways to cheat now that their primary cheating techniques have been blocked. Oh, and Democrats are now threatening a government shutdown in order to prevent ICE from being at polling places. Could it be any more obvious what's going on here? They need illegals to vote or they're screwed. These people are in a full-blown panic over the Trump Administration securing our elections. Enjoy watching them squirm!   https://twitter.com/KanekoaTheGreat/status/2019236736203911681?s=20   Intelligence identified “extremely concerning cybersecurity and operational deployment practices that pose a significant risk to U.S. elections.” ODNI said some vulnerabilities in Puerto Rico's voting machines stemmed from the use of insecure cellular technology, along with software flaws that could allow hackers deep access into critical election systems. “Given ODNI’s broad statutory authority to coordinate, integrate, and analyze intelligence related to election security and our known work on understanding vulnerabilities to foreign and other malign interference, ODNI conducted an examination of electronic voting systems used in Puerto Rico's elections,” an ODNI spokesperson said. In April 2025, Gabbard told a Cabinet meeting that her office had obtained evidence showing U.S. electronic voting systems have long been vulnerable to hacking. “We have evidence of how these electronic voting systems have been vulnerable to hackers and vulnerable to exploitation to manipulate the results of the votes being cast,” she said, adding that this supports the push for nationwide paper ballots so voters can trust the integrity of U.S. elections. https://twitter.com/canncon/status/2019054407954956637?s=20  Bureau of Investigation Vic Reynolds told Senator Perdue, “I’m a team player. If the Governor doesn’t want to investigate, we’re not going to investigate.” “You said that although Mr. Reynolds had received evidence that he felt was compelling enough to open an investigation that he was not going to investigate because the governor had told him not to?” “That’s one of the things he said, yeah.” – Senator Perdue One month before the special grand jury testimony, Vic Reynolds was appointed a Superior Court Judge by……..Governor Brian Kemp. And Reynolds wasn’t the only person who ignored election fraud evidence or maladministration and got appointed to a Superior Court judgeship. He wasn’t even the second one. Reynolds was presented with video evidence, cell phone data, bank records, and testimony of a ballot harvester. Reynolds claimed that the GBI made “repeated requests” to True The Vote for their witness. True The Vote denies this saying that THEY actually reached out to GBI after their one and only meeting and were ignored. From TTV’s Catherine Engelbrecht: “After that meeting, we made repeated attempts to re-engage with the GBI and never received a response.” Why did Brian Kemp order GBI not to investigate an alleged crime, with evidence, that would ultimately lead to a UNPRECEDENTED RICO case against a former President and HIS party’s front-running candidate?? Read my story in the link below. https://twitter.com/amuse/status/2019409257137918096?s=20 https://twitter.com/TrumpWarRoom/status/2019211072755151237?s=20 https://twitter.com/TheStormRedux/status/2019416872727278048?s=20  about Russia interfering in the 2016 election, but now all of a sudden they want nothing to do with that. A solid point. Trump added, “So now they're saying Russia had nothing to do with it, because if I say Russia, it's perfectly fine. But you could add China and about 5 other countries to it.” Is Trump implying they believe there was foreign interference or is he just trolling the deep state? Time will tell. https://twitter.com/EricLDaugh/status/2019198733167260134?s=20 https://twitter.com/Patri0tContr0l/status/2019068648917217511?s=20     https://twitter.com/amuse/status/2019166626260627780?s=20 John Cornyn who are opposed to the bill by not allowing debate. https://twitter.com/nicksortor/status/2019131769665274030?s=20   Any Republican allowing our elections to be filled with fraud needs to be primaried. https://twitter.com/Lancegooden/status/2019126883192049803?s=20 https://twitter.com/EricLDaugh/status/2019414831074271739?s=20   (function(w,d,s,i){w.ldAdInit=w.ldAdInit||[];w.ldAdInit.push({slot:13499335648425062,size:[0, 0],id:"ld-7164-1323"});if(!d.getElementById(i)){var j=d.createElement(s),p=d.getElementsByTagName(s)[0];j.async=true;j.src="//cdn2.customads.co/_js/ajs.js";j.id=i;p.parentNode.insertBefore(j,p);}})(window,document,"script","ld-ajs"); 

Are you looking to save time, make money, and start winning with less risk? Then head to https://www.ovtlyr.com.Alright, let's talk about what's actually happening out there, because if you've been watching the market lately, it probably feels messy, confusing, and honestly a little exhausting.Stocks are getting hit all over the place. Names people love are getting smacked. Futures look shaky one minute, flat the next. And everywhere you turn, someone is screaming that a massive crash is guaranteed. This video slows all of that noise down and looks at what the charts are really saying, not what fear wants them to say.The market is clearly at a turning point. There's heavy selling, weird candles, late-day reversals, and a lot of chop that makes trading feel harder than usual. That doesn't automatically mean everything is about to implode. It means this is one of those periods where patience and discipline matter way more than bold predictions.Instead of trying to call the top or the bottom, the focus here is simple. Follow the trend. Respect the signals. Accept that nobody knows the future. The goal is not to be right on every move. The goal is to stay in the game and protect capital while the market figures itself out.You'll hear why chasing earnings can be brutal, why big gaps can trap traders fast, and why reacting emotionally usually does more damage than waiting things out. There's also a real conversation about inflation, yields, liquidity drying up, and how all of that quietly pressures the market even when headlines sound fine.Quick highlights covered in this session:✅ Why ugly price action does not always mean a crash✅ How to read trends without overthinking every candle✅ Why earnings trades can wreck otherwise good setups✅ What inflation and rates are doing behind the scenes✅ How OVTLYR keeps the focus on process, not predictionsIf trading has felt harder lately, that's because it is. This video is about staying grounded, sticking to a plan, and not letting fear or hype push bad decisions. Watch it with that mindset and it'll click.

In episode 406 of Everything Fast Pitch by Fast Pitch Prep, Coach Tory and Coach Don delve into several listener-driven topics and provide insights into effective softball coaching and team management strategies. The lead-off segment tackles a query from a high school coach looking to improve his pitching coaching skills. The hosts advise taking advantage of online resources, developing a collaborative relationship with players, and maintaining a positive coaching attitude even if expertise isn't fully developed.In the cleanup segment, they address whether it's acceptable for players to skip team practices to play in other tournaments. Both coaches stress the importance of team practices for skill development and team cohesion, highlighting that skipping practices undermines team performance and individual growth. The coaching tip of the week focuses on ensuring players consistently back up plays during games. The coaches share strategies for making this a habit, including the importance of drills, practice, and possibly benching players who fail to fulfill their responsibilities on the field.Support the show

DONATE TO GOONERS V CANCER HERE: https://goonersvcancer.com/Arsenal Eliminate Weak Chelsea, Havertz Haunts Blues, Odegaard Injury Update & Wembley Date set

Join economist Dr. Orphe Divounguy and Chris Krug as they discuss Labor Market, on this episode of Everyday Economics! Everyday Economics is an unrehearsed, free-flow discussion of the economic news shaping the day. The thoughts expressed by the hosts are theirs, unedited, and not necessarily the views of their respective organizations.Support this podcast: https://secure.anedot.com/franklin-news-foundation/ce052532-b1e4-41c4-945c-d7ce2f52c38a?source_code=xxxxxx Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The world is chaotic and people are tired. And when people are tired, they stop rewarding “safe.” They reward clear. In this Black History Month Series episode, I'm joined by Kristina Hall and we're talking about the problem too many Black business owners won't admit out loud: your business isn't stuck because you aren't working hard. It's stuck because your message is weak. If you're posting consistently, seeing other creators pop off, and sitting there thinking “do I need to be more controversial?” you're not crazy. But let's separate truth from coping: the algorithm doesn't “hate” you. The message just isn't landing. And in 2026, with AI making everybody sound the same, blending in is a death sentence.We get into the line between being bold and being messy. Bold is standing on what you believe and being able to back it up. Messy is rage-baiting for attention, copying hot takes, and building a brand that collapses the second a real client walks through the door.Kristina drops a clean framework we build in real time: Stand. Say. Sell. Stand on your business. Say it in plain English. Then sell your service like you're not ashamed to get paid. We also break down a real client example (Pilates School SF) where one bold, clear message brought in the right audience, globally.And we close with a Black History Month question that gets real fast: what Black history means when you're biracial, light-skinned, and still connected to the roots, the pride, and the pain.Watch the full episode on YouTube: https://youtu.be/8_Z3bf7EVEYAs always we ask you to comment, DM, whatever it takes to have a conversation to help you take the next step in your journey, reach out on any platform!Twitter, FaceBook, Instagram, Tiktok, LinkedinDISCLOSURE: Awards and rankings by third parties are not indicative of future performance or client investment success. Past performance does not guarantee future results. All investment strategies carry profit/loss potential and cannot eliminate investment risks. Information discussed may not reflect current positions/recommendations. While believed accurate, Black Mammoth does not guarantee information accuracy. This broadcast is not a solicitation for securities transactions or personalized investment advice. Tax/estate planning information is general - consult professionals for specific situations. Full disclosures at www.blackmammoth.com.

NVIDIA (NVDA) AI chip sales to China are reportedly stalled by a US security review, and Chinese customers are, meanwhile, not placing H200 chip orders.European bourses are broadly firmer, US equity futures are mixed with mild underperformance in the NQ.DXY trades flat ahead of US data, JPY underperforms as focus turns to a landslide LDP victory.Fixed income benchmarks are mixed; USTs are flat whilst Bunds are firmer.Crude-specific newsflow remains light, benchmarks retrace bid following US-Iranian tensions; Precious metals continue to rebound with spot XAU returning above USD 5k/oz.Looking ahead, highlights include US Final Composite/Services PMIs (Jan), US ADP (Jan), ISM Services (Jan), Treasury Refunding Announcement, NBP Policy Announcement, Comments from Fed's Cook, Supply from the US.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

After decades in education, Dr. Peter Liljedahl realized that many classrooms fail to engage the people inside them. Rather than accept that reality, he began challenging every classroom norm he could find, asking a single question of each one: does this increase thinking?What followed was a decades-long effort to redesign learning environments from the ground up, dramatically increasing student engagement and understanding. In this revisited episode, Dart and Peter discuss how rethinking classroom norms can reshape learning, collaboration, and the design of work itself.Dr. Peter Liljedahl is an author, researcher, and professor of mathematics education at Simon Fraser University in Vancouver, Canada. His work focuses on increasing thinking, engagement, and collaboration through classroom design.In this episode, Dart and Peter discuss:- Peter's redesign of the classroom and how it can be applied to work- How to create an environment that cultivates thinking- Transforming norms to achieve better results- The importance of collaboration in work and learning- The best ways to evaluate employee performance- Deconstructing ideas into actionable points- What creates “Aha!” moments- The structure of a good task- And other topics…Dr. Peter Liljedahl is a professor of mathematics education at Simon Fraser University in Vancouver, Canada. His work focuses on increasing thinking, engagement, and collaboration through classroom design. He is the author of Building Thinking Classrooms in Mathematics and works internationally with educators, schools, and education systems. His work has been recognized with the Cmolik Prize for the Enhancement of Public Education and the Fields Institute's Margaret Sinclair Memorial Award for Innovation and Excellence in Mathematics Education.Resources mentioned:Building Thinking Classrooms in Mathematics, Grades K-12, by Peter Liljedahl: https://www.amazon.com/Building-Thinking-Classrooms-Mathematics-Grades/dp/1544374836Weapons of the Weak, by James Scott: https://www.amazon.com/Weapons-Weak-Everyday-Peasant-Resistance/dp/0300036418A Pattern Language, by Christopher Alexander: https://www.amazon.com/Pattern-Language-Buildings-Construction-Environmental/dp/0195019199Connect with Peter:X: https://x.com/pgliljedahlhttps://buildingthinkingclassrooms.com/Work with Dart:Dart is the CEO and co-founder of the work design firm 11fold. Build work that makes employees feel alive, connected to their work, and focused on what's most important to the business. Book a call at 11fold.com.

Become a member at www.blackwhitenetwork.com for just $10 per month with a 7 day FREE TRIAL and get exclusive content and extra discounts on merch!Member stream at 10am CST every Friday UNCENSORED!Locals: https://blackandwhitenetwork.locals.comBecome a monthly subscriber to the podcast: https://podcasters.spotify.com/pod/show/blackandwhitenetwork/subscribeFollow us on Rumble: https://rumble.com/user/BlackandWhiteNewsFollow Black and White Sports on Rumble: https://rumble.com/user/BlackandWhiteSports

Do you feel like living the faith has become needlessly complicated? Are you confused; deconstructing; or just looking for answers? Today's episode is meant to encourage you to live the true gospel, which is a gospel of grace and not works. Time to get back to the basics. ► Buy John's new book, Wimpy, Weak, And Woke https://a.co/d/j2JNRCV ► Subscribe to the podcast: www.johnlcooper.com  Apple: http://bit.ly/cooperstuff  Spotify: http://bit.ly/cooperstuffspotify  CastBox: http://bit.ly/cooperstuffcast ► Connect with John L. Cooper on Social Media:  https://www.facebook.com/johnlcooperstuff https://www.instagram.com/johnlcooper  https://www.twitter.com/johnlcooper ► Cooper Stuff Merchandise:  https://cooper-stuff.myshopify.com/collections/all

Dr. Doug Lucas, a double board-certified orthopedic surgeon and osteoporosis specialist, who dives into the secrets of maintaining optimal bone health. In this episode, you will discover why bone health is vital for women of all ages and learn actionable tips to build and maintain strong bones from your teens to your golden years. Brace yourself for intriguing insights, such as the surprising benefits of prunes as an independent intervention for osteoporosis! This episode is a comprehensive guide to nurturing your bones through diet, exercise, and hormonal balance.Episode Overview [Timestamps are approximate]:(0:00) Introduction(4:00) Why Women Get Osteoporosis in Their 40s(11:00) The Chronic Dieting Problem & Under-Eating Epidemic(17:00) Pregnancy, Breastfeeding & Bone Density(23:00) Hormones Explained(34:00) Nutrition for Bone Health(43:00) The Boron Secret & Prunes for Bone Density(47:00) Exercise That Hurts Your Bones(55:00) How to Lift Weights for Bone Health(1:04:00) Weighted Vests, GLP-1s & Bone Medications(1:23:00) The After-Party with Dr. StephanieResources mentioned in this episode can be found at https://drstephanieestima.com/podcasts/ep454We couldn't do it without our sponsors:TROSCRIPTIONS - There's a completely new way to optimize your health. Give it a try at https://troscriptions.com/BETTER or enter BETTER at checkout for 10% off your first order.PIQUE LIFE - If you want to redefine your evening ritual and still feel like yourself the next day, you can get 10% off for life. Yes, for life at https://piquelife.com/betterYOUNG GOOSE - Youth Serum & Youth Moisturizer from Young Goose is designed to support NAD⁺-dependent cellular pathways in the skin. Go to https://younggoose.com/better and use code BETTER for 10% off your first purchaseLIFT - If you want muscle for longevity, clarity, and confidence—come LIFT with me.  Head over to https://drstephanieestima.com/lift and join today.QUALIA NAD+ - Boost energy, DNA health, and cellular protection. Save 15% at https://qualialife.com/better with code BETTER. P.S. When you're ready, here are a two ways I can help you:Subscribe: The Mini Pause — My weekly newsletter packed with the most actionable, evidence-based tools for women 40+ to thrive in midlife.Build Muscle: LIFT — My progressive strength training program designed for women in midlife. Form-focused, joint-friendly, and built for real results. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Can weakness really become strength? And can ordinary, imperfect people actually become Zion?

APAC stocks pressured with several bearish factors weighing, incl. the partial US shutdown, weak Chinese PMIs & NVIDIA's OpenAI investment stalling.DXY rangebound, EUR firmer but below 1.19. USD/JPY initially benefited from Takaichi's remarks, though subsequent clarification unwound this.Fixed benchmarks mixed, JGBs benefit from the latest election polling.Crude benchmarks hit alongside APAC stocks, OPEC+ maintained the pause as expected. Spot gold continued to falter, base peers hit by the Chinese data.Bitcoin hit a trough just below USD 75k before finding a floor.Looking ahead, highlights include Global Final Manufacturing PMIs (Jan), US ISM Manufacturing PMI (Jan), Speakers including BoE's Breeden & Fed's Bostic, Treasury Refunding Announcement, Earnings from Palantir & NXP Semiconductors.Click for the Newsquawk Week Ahead.Read the full report covering Equities, Forex, Fixed Income, Commodites and more on Newsquawk

Healthy boundaries are essential - but most men don't know what they actually look like. In this episode of Friday Field Notes, Ryan Michler breaks down eight practical boundaries every man must establish in his personal, professional, and romantic relationships to build respect, alignment, and long-term success. These boundaries aren't about control or ultimatums - they're about clarity, self-respect, and creating relationships that truly work. SHOW HIGHLIGHTS 00:00 - Why Boundaries Matter 01:10 - Why Men Struggle with Boundaries 02:38 - Grace, Communication, and Relationships 03:05 - High Fences Make Great Neighbors 05:05 - Alignment in Healthy Relationships 06:15 - Boundary #1: Reciprocity 07:26 - Boundary #2: Initiation 08:50 - Boundary #3: Flow 10:55 - Boundary #4: Capacity 12:08 - Boundary #5: Self-Abandonment 14:45 - Boundary #6: Regulation 17:30 - Boundary #7: Exit Boundary 22:55 - Boundary #8: Potential 24:10 - Identifying Triggered Boundaries 26:05 - Communicating Boundaries Effectively 28:40 - Join Iron Council 30:05 - Final Thoughts & Sign-Off Battle Planners: Pick yours up today! Order Ryan's new book, The Masculinity Manifesto. For more information on the Iron Council brotherhood. Want maximum health, wealth, relationships, and abundance in your life? Sign up for our free course, 30 Days to Battle Ready

Guest: Michael Toth. The segment focuses on California's strategy to empower the Attorney General to sue fossil fuel companies for rising insurance premiums. Toth argues these lawsuits are politically motivated and legally weak, noting that even insurance companies refuse to sue because attributing specific damages or deaths to corporate emissions is factually difficult.UNDATED

Eric Criscuolo, NYSE Market Strategist, highlights a week where mega cap tech snapped back into leadership after small caps' strong run faded. A quiet Fed meeting kept rates steady and shifted attention to earnings, where Meta and IBM surged on AI strength while Microsoft slumped on lofty expectations. Travel, leisure, and select industrial names outperformed as software and healthcare lagged. Metals stole the spotlight with extreme volatility, led by gold's dramatic intraday swing, while crypto struggled to gain traction. With a busy earnings slate and key labor data ahead, markets move into next week with momentum reshuffling once again.

Happy friday!!! Holy cow this week was a DOOZY! Thank you for being here as always!!! Write In Your Questions/Stories: https://docs.google.com/forms/d/1Po-xXACQPyiFYy4UP9ctxg7UAOh1bFoUnG65hAz5GRM/preview  

The experts discussed whether it is more costly to have thin or fat cows going into the calving season. Thin cows are generally more costly because they are at high risk of having calving issues. Determining when to be aggressive with supplementation is important when addressing thin cows. The team also answered a listener’s question regarding weak calves. They discussed the factors that could be causing the calves to be weak. The key takeaway is that weak calves are associated with long or difficult births, weather stress, and poor nutrition. Better monitoring of heifers and calving progress can prevent many issues. 2:35 Thin vs Fat Cows 19:53 Weak Calves For more on BCI Cattle Chat, follow us on X at @ksubci, Facebook, and Instagram at @ksubci. Check out our website, ksubci.org. If you have any comments/questions/topic ideas, please send them to bci@ksu.edu. You can also email us to sign up for our weekly news blast! Don't forget, if you enjoy the show, please go give us a rating!

Joe's Premium Subscription: www.standardgrain.comGrain Markets and Other Stuff Links —Apple PodcastsSpotifyTikTokYouTubeFutures and options trading involves risk of loss and is not suitable for everyone.Welcome back!Grain futures finished higher Wednesday as a weaker US dollar, biofuel headlines, and weather concerns provided support across ag and macro markets. Here's what moved markets today

Bethel's apology; my thoughts; lots of receipts. ► Buy John's new book, Wimpy, Weak, And Woke https://a.co/d/j2JNRCV ► Subscribe to the podcast: www.johnlcooper.com  Apple: http://bit.ly/cooperstuff  Spotify: http://bit.ly/cooperstuffspotify  CastBox: http://bit.ly/cooperstuffcast ► Connect with John L. Cooper on Social Media:  https://www.facebook.com/johnlcooperstuff https://www.instagram.com/johnlcooper  https://www.twitter.com/johnlcooper ► Cooper Stuff Merchandise:  https://cooper-stuff.myshopify.com/collections/all

Bethel's apology; my thoughts; lots of receipts. ► Buy John's new book, Wimpy, Weak, And Woke https://a.co/d/j2JNRCV ► Subscribe to the podcast: www.johnlcooper.com  Apple: http://bit.ly/cooperstuff  Spotify: http://bit.ly/cooperstuffspotify  CastBox: http://bit.ly/cooperstuffcast ► Connect with John L. Cooper on Social Media:  https://www.facebook.com/johnlcooperstuff https://www.instagram.com/johnlcooper  https://www.twitter.com/johnlcooper ► Cooper Stuff Merchandise:  https://cooper-stuff.myshopify.com/collections/all

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Joe's Premium Subscription: www.standardgrain.comGrain Markets and Other Stuff Links —Apple PodcastsSpotifyTikTokYouTubeFutures and options trading involves risk of loss and is not suitable for everyone.Welcome back to the channel!In today's update, we cover Trump's comments on year-round E15, growing weakness in the U.S. dollar, 2026 acreage debates, heat stress in Argentina, fresh USDA flash sales, and China's latest soybean buying behavior.⛽

“We are in the midst of a rupture, not a transition,” Prime Minister Mark Carney of Canada announced last week at the World Economic Forum in Davos, Switzerland.It was one of the most significant foreign policy speeches in years, sending shockwaves through the international community. He was describing a dynamic that's been building for decades — what the scholars Henry Farrell and Abraham Newman call “weaponized interdependence” — that has now reached a tipping point.I asked Farrell on the show to explain this dynamic, why this is a “rupture” moment and how other countries are responding. He is an international-affairs professor at Johns Hopkins University, is an author of the book “Underground Empire: How America Weaponized the World Economy” and writes an excellent Substack, Programmable Mutter.Note: This episode touches on the clashes over immigration enforcement in Minneapolis and the killing of Renee Good, but it was recorded on Friday, before the killing of Alex Pretti.Mentioned:“Davos 2026: Special address by Mark Carney, Prime Minister of Canada”Underground Empire by Henry Farrell and Abraham Newman“Programmable Mutter” by Henry Farrell“The nature and sources of liberal international order” by Daniel Deudney and G. John Ikenberry“The Enshittification of American Power” by Henry Farrell and Abraham L. Newman“Too big to care” by Cory DoctorowWeapons of the Weak by James C. ScottPrivate Truths, Public Lies by Timur Kuran“Further Back to the Future: Neo-Royalism, the Trump Administration, and the Emerging International System” by Stacie E. Goddard and Abraham Newman“The Dynamics of Informational Cascades: The Monday Demonstrations in Leipzig, East Germany, 1989–91” by Susanne LohmannBook Recommendations:Dollars and Dominion by Mary BridgesNonesuch by Francis SpuffordThe Score by C. Thi NguyenThoughts? Guest suggestions? Email us at ezrakleinshow@nytimes.com.You can find transcripts (posted midday) and more episodes of “The Ezra Klein Show” at nytimes.com/ezra-klein-podcast, and you can find Ezra on Twitter @ezraklein. Book recommendations from all our guests are listed at https://www.nytimes.com/article/ezra-klein-show-book-recs.This episode of “The Ezra Klein Show” was produced by Jack McCordick. Fact-checking by Michelle Harris, with Mary Marge Locker, Kate Sinclair Our senior engineer is Jeff Geld, with additional mixing by Aman Sahota and Isaac Jones. Our executive producer is Claire Gordon. The show's production team also includes Marie Cascione, Annie Galvin, Rollin Hu, Kristin Lin, Emma Kehlbeck, Marina King and Jan Kobal. Original music by Pat McCusker and Carole Sabouraud. Audience strategy by Kristina Samulewski and Shannon Busta. The director of New York Times Opinion Audio is Annie-Rose Strasser. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app.

Today we talk about something we've never dabbled into: Bethel controversy. Podcaster Mike Winger released a 6 hour expose on Bethel's knowledge about one of their most platformed "prophets," Shawn Bolz, who allegedly data-mined personal information about people and used it to fraudulently prophesy to them. These and other accusations has caused a firestorm in the evangelical world, especially in its charismatic corners. Today, we briefly respond to the controversy. A church that is truly prophetic would stand up for righteousness and for victims of church abuse.  ► Mike Winger's Video https://www.youtube.com/watch?v=GH05S53QlY0 ► Buy John's new book, Wimpy, Weak, And Woke http://www.johnlcooper.com/wimpyweakwoke https://a.co/d/j2JNRCV ► Subscribe to the podcast: www.johnlcooper.com  Apple: http://bit.ly/cooperstuff  Spotify: http://bit.ly/cooperstuffspotify  CastBox: http://bit.ly/cooperstuffcast ► Connect with John L. Cooper on Social Media:  https://www.facebook.com/johnlcooperstuff https://www.instagram.com/johnlcooper  https://twitter.com/johnlcooper ► Cooper Stuff Merchandise:  www.johnlcooper.com/store

Note: "Act 1" was a separate published audio podcast.*Check out EZ's morning radio show "The InZane Asylum Q100 Michigan with Eric Zane" Click here*Get a FREE 7 day trial to Patreon to "try it out."*Watch the show live, daily at 8AM EST on Twitch! Please click here to follow the page.Email the show on the Shoreliners Striping inbox: eric@ericzaneshow.comTopics:*Local hilljacks again say "no" to new data center*Data centers on the Moon.*Another massive pile-up near EZ's house.*Despite being a huge pile of shit, Shedeur Sanders is a Pro Bowl playerAsshole of the DaySponsors:SkyDive Grand Haven, Merchant Automotive, Impact Power Sports, Kuiper Tree Care, Frank Fuss / My Policy Shop Insurance, Kings Room Barbershop, Shoreliners Striping, Ervines Auto Repair Grand Rapids Hybrid & EV,Interested in advertising? Email eric@ericzaneshow.com and let me design a marketing plan for you.Contact: Shoreliners Striping inbox eric@ericzaneshow.comDiscord LinkEZSP TikTokSubscribe to my YouTube channelHire me on Cameo!Tshirts available herePlease subscribe, rate & write a review on Apple Podcastspatreon.com/ericzaneInstagram: ericzaneshowTwitter:Our Sponsors:* Check out Aura.com: https://aura.com/removeSupport this podcast at — https://redcircle.com/the-eric-zane-show-podcast/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

Bill Roggio and Husain Haqqani examine how Africa remains unprotected from jihadists and plunderers. The discussion explores the continent's vulnerability to extremist expansion and resource exploitation, with weak governance and insufficient international attention allowing terrorist networks and predatory actors to operate with increasing impunity across multiple nations.

Are you struggling with feelings of inferiority? Has your self-esteem been crushed by a soured relationship or job loss? Chip shares how you can rise above those feelings of inferiority and a low self-esteem to experience God's love for you like never before.Distorted mirrors destroy our lives:The Appearance MirrorThe Performance MirrorThe Status MirrorThesis – Until we see ourselves as God sees us, we are destined to FEEL INFERIOR.3 keys to a Biblical self-image:1. Get God's View of You = KNOWLEDGEYou are significant because:You're UNIQUE -Gen 1:27, Ps 139:13-14You're LOVED -1 Sam 16:7, Jer 31:3You're VALUABLE -1 Cor 6:19-20You're SECURE -Eph 1:13You're INDISPENSIBLE -Eph 2:10, 4:15-162. Believe God's View is True = FAITHFaith is built on God's Word -Rom 10:17Faith grows through mind renewal -Rom 12:2Develop a plan to remove the distorted mirrors of the world with the mirror of God's Word -Jam 1:22-243. Discover the “You” that's True = EXPERIENCEUnwrap your SPIRITUAL GIFTS -1 Cor 12, Rom 12Unleash your SPIRITUAL PASSION -Ps 37:4Use your God-given TALENTS -Ex 31:3Embrace your God-given PERSONALITY -1 Cor 2:11Leverage your past EXPERIENCE -Rom 8:28Ministry is how God makes what's true “of” us, true “in” us. Sometimes it's only in the act of “loving others: that we can fathom that we ourselves are loved!Broadcast ResourceDownload MP3Message NotesAdditional Resource MentionsI Choose Love BookDaily Discipleship - Psalms of HopeBOOK: "The Strong and the Weak" by Paul TournierConnect888-333-6003WebsiteChip Ingram AppInstagramFacebookTwitterPartner With UsDonate Online888-333-6003