Podcasts about Sort

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For the resources and links mentioned, go to:  https://simplysquaredaway.com/158 Do you ever lay in bed mentally rehearsing 47 things you “should” be doing… and then wake up already behind? Yep. Same. In this episode, I'm walking you through exactly how to organize your mind using the same five-step system I use to organize closets, calendars, businesses, and entire lives. Because here's the truth: Overwhelm is not a personality trait. It's a signal. And when your brain feels like 47 tabs are open and one of them is playing music, but you can't find it… It's time to organize. I break down my SPACE method, Sort, Purge, Assign Homes, Contain, Energize,  and show you how to apply it to your thoughts, worries, projects, errands, ideas, and even those sneaky “I'm not doing enough” stories running in the background. This is the exact process I used before heading out of town when my brain was spinning with taxes, birthdays, collaborations, podcast ideas, errands, and random worries. If you're overwhelmed right now, this episode is your reset button. What You'll Learn: Why overwhelm is a signal, not a failure How to “sort” your brain without creating more chaos The 4 D's that instantly reduce mental clutter How to assign homes to thoughts, tasks, and even worries The maintenance habit that keeps your mind clear long-term How to refill your brain with thoughts that energize you instead of drain you

304: Declutter Your Digital Chaos: Organizing Files for Productivity On the podcast today is Tracy Hoth, a life coach and professional organizer with over 16 years of experience. Tracy helps coaches and business owners transform their busy, cluttered operations into organized, CEO-level businesses—so they can calmly make more money while working fewer hours. Her specialty is stepping in to organize digital files, systems, and workflows so you can finally focus on the work that matters most. I'm excited for you to hear this conversation and learn how organization can create both clarity and profitability in your business. Topics Mentioned: Digital Organization Structured Systems for Executive Function Overcoming Resistance to Change Key Thoughts:  Personal identity and past experiences can influence one's ability to stay organized. Structured systems support individuals with executive function challenges. Consistent systems build organizational skills. Simple file structures and organization of bookmarks streamlines operations. Understanding the root of resistance, such as lack of time or energy, is crucial for change. Sort and purge before making decisions.     Contact Michele: Email: Team@ScarletThreadConsulting.com Facebook: Scarlet Thread Consulting Instagram: @ScarletThreadATL Website: ScarletThreadConsulting.com LinkedIn: Michele Williams   Contact Tracy: Email: tracy@simplysquaredaway.com Instagram: @tracyhoth LinkedIn: linkedin.com/in/tracyhoth/ Website: simplysquaredaway.com   References and Resources: Work with Me The Designers' Inner Circle - Become a Member Today    CFO2Go Metrique Solutions

This week Micah and Jason join Brad to discuss some hidden gems including Romeo is a Dead Man, Brigandine: The Legend of Runersia, Crisol: Theater of Idols, and more! Please keep in mind that our timestamps are approximate, and will often be slightly off due to dynamic ad placement. 0:00:00 - Intro0:10:43 - Romeo is a Dead Man0:34:44 - Brigandine: The Legend of Runersia0:45:59 - Crisol: Theater of Idols0:56:34 - Sort it Out/Keep it Up1:29:31 - Game Recommendation1:32:35 - Looking for Group1:36:46 - Cairn1:52:18 - Dragon Quest VII Reimagined2:03:03 - Dynasty Warriors: Origins2:17:19 - Closing Questions To watch the podcast on YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/LastStandMediaYouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Don't forget to subscribe to the podcast for free wherever you're listening or by using this link: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/SummonSign⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ If you like the show, telling a friend about it would be amazing! You can text, email, Tweet, or send this link to a friend: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/SummonSign⁠⁠ Learn more about your ad choices. Visit podcastchoices.com/adchoices

What do you do when your parents have 40 years of stuff in a 4,000 square foot house and they need to move to a 1,200 square foot assisted living apartment? Most families are paralyzed by this question—and Ernise Beckel has spent her career solving it.Ernise is a registered nurse with 20 years of experience who kept walking into seniors' homes and seeing the same problem: people discharged from hospitals into houses filled with clutter, creating fall hazards and overwhelming situations that nobody was addressing. Now she co-owns Caring Transitions of Eden Prairie with her mom, serving families throughout Carver County and the southwest metro.In this conversation, we get into the actual mechanics of how this works:THE PROCESS• Free consultation: 30-60 minutes, includes walkthrough and density assessment• They measure the new space and tell you exactly what will fit• SOD method: Sort, Organize, Donate, Dispose• They photograph everything and recreate familiar arrangements in the new homeWHERE THE STUFF GOES• CT Bids online auction platform reaches 300,000+ registered shoppers nationwide• About 75% of household items can be sold• Revenue split: 65% to homeowner, 35% to Caring Transitions• Items ship nationwide—not limited to local buyers like estate salesTIMELINE & PRICING• Full liquidation (selling everything): 3-4 weeks• Cleanout only (donate/dispose): approximately 1 week• Cost: $3-5 per square foot depending on density• Recommended lead time: call at least one month before you need completionWHY IT'S DIFFERENT FROM ESTATE SALES• No strangers walking through your house• No cars parked on curbs (HOA friendly)• Online bidding reaches national market• They handle ALL remaining items—nothing left behind• Items are shipped to buyers, not picked up on-siteTHE EMOTIONAL SIDEErnise talks about why she insists on meeting mom (not just the adult children), how to handle situations when the senior doesn't want to move, and why patience is essential. Her nursing background shapes everything about how she approaches these transitions.PRACTICAL ADVICEFor families avoiding the conversation: bring it up when multiple family members are present (holidays can work), focus on benefits like being closer to family or having built-in social connections, and give them time to process—this usually takes months, not days.SERVICE AREA: Eden Prairie, Chanhassen, Chaska, Waconia, Victoria, and surrounding Carver County communitiesCONNECT WITH CARING TRANSITIONS: Website: caringtransitions.comABOUT THE HOST: Greg Anderson has been selling real estate in Carver County since 1985 with over 3,000 homes sold. Living IN Carver County is his podcast connecting friends and building community through conversations with local business owners, nonprofit leaders, elected officials, and community members.Substack: HelloIamGregAnderson.substack.comLinkedIn: linkedin.com/in/gregoryranderson

Original Release Date: Feb 6, 2026Our Global Head of Fixed Income Research Andrew Sheets and Global Chief Economist Seth Carpenter unpack the inner workings of the Federal Reserve to illustrate the challenges that Fed chair nominee Kevin Warsh may face.Read more insights from Morgan Stanley.----- Transcript -----Andrew Sheets: Welcome to Thoughts on the Market. I'm Andrew Sheets, Global Head of Fixed Income Research at Morgan Stanley. Seth Carpenter: And I'm Seth Carpenter, Morgan Stanley's Global Chief Economist and Head of Macro Research. Andrew Sheets: And today on the podcast, a further discussion of a new Fed chair and the challenges they may face. It's Friday, February 6th at 1 pm in New York. Seth, it's great to be here talking with you, and I really want to continue a conversation that listeners have been hearing on this podcast over this week about a new nominee to chair the Federal Reserve: Kevin Warsh. And you are the perfect person to talk about this, not just because you lead our economic research and our macro research, but you've also worked at the Fed. You've seen the inner workings of this organization and what a new Fed chair is going to have to deal with. So, maybe just for some broad framing, when you saw this announcement come out, what were some of the first things to go through your mind? Seth Carpenter: I will say first and foremost, Kevin Warsh's name was one of the names that had regularly come up when the White House was providing names of people they were considering in lots of news cycles. So, I think the first thing that's critically important from my perspective, is – not a shock, right? Sort of a known quantity. Second, when we think about these really important positions, there's a whole range of possible outcomes. And I would've said that of the four names that were in the final set of four that we kept hearing about in the news a lot. You know, some differences here and there across them, but none of them was substantially outside of what I would think of as mainstream sort of thinking. Nothing excessively unorthodox at all like that. So, in that regard as well, I think it should keep anybody from jumping to any big conclusions that there's a huge change that's imminent. I think the other thing that's really important is the monetary policy of the Federal Reserve really is made by a committee. The Federal Open Market Committee and committee matters in these cases. The Fed has been under lots of scrutiny, under lots of pressure, depending on how you want to put it. And so, as a result, there's a lot of discussion within the institution about their independence, making sure they stick very scrupulously to their congressionally given mandate of stable prices, full employment. And so, what does that mean in practice? That means in practice, to get a substantially different outcome from what the committee would've done otherwise… So, the market is pricing; what's the market pricing for the funds rate at the end of this year? About 3.2 percent. Andrew Sheets: Something like that. Yeah. Seth Carpenter: Yeah. So that's a reasonable forecast. It's not too far away from our house view. For us to end up with a policy rate that's substantially away from that – call it 1 percentage, 2 percentage points away from that. I just don't see that as likely to happen. Because the committee can be led, can be swayed by the chair, but not to the tune of 1 or 2 percentage points. And so, I think for all those reasons, there wasn't that much surprise and there wasn't, for me, a big reason to fully reevaluate where we think the Fed's going. Andrew Sheets: So let me actually dig into that a little bit more because I know our listeners tune in every day to hear a lot about government meetings. But this is a case where that really matters because I think there can sometimes be a misperception around the power of this position. And it's both one of the most public important positions in the world of finance. And yet, as you mentioned, it is overseeing a committee where the majority matters. And so, can you take us just a little bit inside those discussions? I mean, how does the Fed Chair interact with their colleagues? How do they try to convince them and persuade them to take a particular course of action? Seth Carpenter: Great question. And you're right, I sort of spent a bunch of time there at the Fed. I started when Greenspan was chair. I worked under the Bernanke Fed. And of course, for the end of that, Janet Yellen was the vice chair. So, I've worked with her. Jay Powell was on the committee the whole time. So, the cast of characters quite familiar and the process is important. So, I would say a few things. The chair convenes the meetings; the chair creates the agenda for the meeting. The chair directs the staff on what the policy documents are that the committee is going to get. So, there's a huge amount of influence, let's say, there. But in order to actually get a specific outcome, there really is a vote. And we only have to look back a couple weeks to the last FOMC meeting when there were two dissents against the policy decision. So, dissents are not super common. They don't happen at every single meeting, but they're not unheard of by any stretch of the imagination either. And if we go back over the past few years, lots going on with inflation and how the economy was going was uncertain. Chair Powell took some dissents. If we go back to the financial crisis Chair Bernanke took a bunch of dissents. If we go back even further through time, Paul Volcker, when he was there trying to staunch the flow of the high inflation of the 1970s, faced a lot of resistance within his committee. And reportedly threatened to quit if he couldn't get his way. And had to be very aggressive in trying to bring the committee along. So, the chair has to find a way to bring the committee along with the plan that the chair wants to execute. Lots of tools at their disposal, but not endless power or influence. Does that make sense? Andrew Sheets: That makes complete sense. So, maybe my final question, Seth, is this is a tough job. This is a tough job in… Seth Carpenter: You mean your job and my job, or… Andrew Sheets: [Laughs] Not at all. The chair of the Fed. And it seems especially tricky now. You know, inflation is above the Fed's target. Interest rates are still elevated. You know, certainly mortgage rates are still higher than a lot of Americans are used to over the last several years. And asset prices are high. You know, the valuation of the equity market is high. The level of credit spreads is tight. So, you could say, well, financial conditions are already quite easy, which can create some complications. I am sure Kevin Warsh is receiving lots of advice from lots of different angles. But, you know, if you think about what you've seen from the Fed over the years, what would be your advice to a new Fed chair – and to navigate some of these challenges? Seth Carpenter: I think first and foremost, you are absolutely right. This is a tough job in the best of times, and we are in some of the most difficult and difficult to understand macroeconomic times right now. So, you noted interest rates being high, mortgage rates being high. There's very much an eye of the beholder phenomenon going on here. Now you're younger than I am. The first mortgage I had. It was eight and a half percent. Andrew Sheets: Hmm. Seth Carpenter: I bought a house in 2000 or something like that. So, by those standards, mortgage rates are actually quite low. So, it really comes down to a little bit of what you're used to. And I think that fact translates into lots of other places. So, inflation is now much higher than the committee's target. Call it 3 percent inflation instead core inflation on PCE, rather than 2 percent inflation target. Now, on the one hand that's clearly missing their target and the Fed has been missing their target for years. And we know that tariffs are pushing up inflation, at least for consumer goods. And Chair Powell and this committee have said they get that. They think that inflation will be temporary, and so they're going to look through that inflation. So again, there's a lot of judgment going on here. The labor market is quite weak. Andrew Sheets: Hmm. Seth Carpenter: We don't have the latest months worth of job market data because of the government shutdown; that'll be delayed by a few days. But we know that at the end of last year, non-farm payrolls were running well below 50,000. Under most circumstances, you would say that is a clear indication of a super weak economy. But! But if we look at aggregate spending data, GDP, private-domestic final purchases, consumer spending, CapEx spending. It's actually pretty solid right now. And so again, that sense of judgment; what's the signal you're going to look for? That's very, very difficult right now, and that's part of what the chair is going to have to do to try to bring the committee together, in order to come to a decision. So, one intellectually coherent argument is – the main way you could get strong aggregate demand, strong spending numbers, strong GDP numbers, but with pretty tepid labor force growth is if productivity is running higher and if productivity is going higher because of AI, for example, over time you could easily expect that to be disinflationary. And if it's disinflationary, then you can cut it. Interest rates now. Not worry as much as you would normally about high inflation. And so, the result could be a lower path for policy rates. So that's one version of the argument that I suspect you're going to hear. On the other hand, inflation is high and it's been high for years. So what does that mean? Well. History suggests that if inflation stays too high for too long, inflation psychology starts to change the way businesses start to set. Andrew Sheets: Mm-hmm. Seth Carpenter: Their own prices can get a little bit loosey-goosey. They might not have to worry as much about consumers being as picky because everybody's got used to these price changes. Consumers might be become less picky because, well, they're kind of sick of shopping around. They might be more willing to accept those higher prices, and that's how things snowball. So, I do think that the new chair is going to face a particularly difficult situation in leading a committee in particularly challenging times. But I've gone on for a long, long time there. And one of the things that I love about getting to talk to you, Andrew, is the fact that you also talked to lots of investors all around the world. You're based in London. And so when the topic of the new Fed chair comes up, what are the questions that you're getting from clients? Andrew Sheets: So, I think that there are a few questions that stand out. I mean, I think a dominant question among investors was around the stability of the U.S. dollar. And so, you could say a good development on the back of Kevin Warsh's nomination is that the market response to that has been the price action you would associate with more stability. You've seen the dollar rise; you've seen precious metals prices fall. You've seen equity markets and credit spreads be very stable. So, I think so far everything in the market reaction is to your; to the point that you raised, you know, consistent with this still being orthodox policy. Every Fed chair is different, but still more similar than different now. I think where it gets more divergent in client opinions is just – what are we going to see from the Fed? Are we going to see a real big change in policy? And I think that this is where there are very different views of Kevin Warsh from investors. Some who say, ‘Well, he's in the past talked about fighting inflation more aggressively, which would imply tighter policy.' And he's also talked more recently about the productivity gains from AI and how that might support lower interest rates. So, I think that there's going to be a lot of interest when he starts to speak publicly, when we see testimony in front of the Senate. I think the other, the final piece, which I think again, people do not have as fully formed an opinion on yet is – how does he lead the Fed if the data is unexpected? And you know, you mentioned inflation and, you know, Morgan Stanley has this forecast that: Well, owner's equivalent rent, a really key part of inflation, might be a little bit higher than expected, which might be a distortion coming off of the government shutdown and impacts on data. But there's some real uncertainty about the inflation path over the near term. And so, in short, I think investors are going to give the benefit of the doubt. For now, I think they're going to lean more into this idea that it will be generally consistent with the Fed easing policy over time, for now. Generally consistent with a steeper curve for now. But I think there's a lot we're going to find out over the next couple of weeks and months. Seth Carpenter: Yeah. No, I agree with you. Andrew, I have to say, I'm glad you're here in New York. It's always great to sit down and talk to you. Let's do it again before too long. Andrew Sheets: Absolutely, Seth. Thanks for taking the time to talk. And to our audience, thank you as always for your time. If you find Thoughts the Market useful, let us know by leaving a review wherever you listen. And also tell a friend or colleague about us today.

Chris Cieslak, CEO of BladeBug, joins the show to discuss how their walking robot is making ultrasonic blade inspections faster and more accessible. They cover new horizontal scanning capabilities for lay down yards, blade root inspections for bushing defects, and plans to expand into North America in 2026. Sign up now for Uptime Tech News, our weekly newsletter on all things wind technology. This episode is sponsored by Weather Guard Lightning Tech. Learn more about Weather Guard’s StrikeTape Wind Turbine LPS retrofit. Follow the show on YouTube, Linkedin and visit Weather Guard on the web. And subscribe to Rosemary’s “Engineering with Rosie” YouTube channel here. Have a question we can answer on the show? Email us! Welcome to Uptime Spotlight, shining Light on Wind. Energy’s brightest innovators. This is the Progress Powering Tomorrow. Allen Hall: Chris, welcome back to the show.  Chris Cieslak: It’s great to be back. Thank you very much for having me on again.  Allen Hall: It’s great to see you in person, and a lot has been happening at Blade Bugs since the last time I saw Blade Bug in person. Yeah, the robot. It looks a lot different and it has really new capabilities.  Chris Cieslak: So we’ve continued to develop our ultrasonic, non-destructive testing capabilities of the blade bug robot. Um, but what we’ve now added to its capabilities is to do horizontal blade scans as well. So we’re able to do blades that are in lay down yards or blades that have come down for inspections as well as up tower. So we can do up tower, down tower inspections. We’re trying to capture. I guess the opportunity to inspect blades after transportation when they get delivered to site, to look [00:01:00] for any transport damage or anything that might have been missed in the factory inspections. And then we can do subsequent installation inspections as well to make sure there’s no mishandling damage on those blades. So yeah, we’ve been just refining what we can do with the NDT side of things and improving its capabilities  Joel Saxum: was that need driven from like market response and people say, Hey, we need, we need. We like the blade blood product. We like what you’re doing, but we need it here. Or do you guys just say like, Hey, this is the next, this is the next thing we can do. Why not?  Chris Cieslak: It was very much market response. We had a lot of inquiries this year from, um, OEMs, blade manufacturers across the board with issues within their blades that need to be inspected on the ground, up the tap, any which way they can. There there was no, um, rhyme or reason, which was better, but the fact that he wanted to improve the ability of it horizontally has led the. Sort of modifications that you’ve seen and now we’re doing like down tower, right? Blade scans. Yeah. A really fast breed. So  Joel Saxum: I think the, the important thing there is too is that because of the way the robot is built [00:02:00] now, when you see NDT in a factory, it’s this robot rolls along this perfectly flat concrete floor and it does this and it does that. But the way the robot is built, if a blade is sitting in a chair trailing edge up, or if it’s flap wise, any which way the robot can adapt to, right? And the idea is. We, we looked at it today and kind of the new cage and the new things you have around it with all the different encoders and for the heads and everything is you can collect data however is needed. If it’s rasterized, if there’s a vector, if there’s a line, if we go down a bond line, if we need to scan a two foot wide path down the middle of the top of the spa cap, we can do all those different things and all kinds of orientations. That’s a fantastic capability.  Chris Cieslak: Yeah, absolutely. And it, that’s again for the market needs. So we are able to scan maybe a meter wide in one sort of cord wise. Pass of that probe whilst walking in the span-wise direction. So we’re able to do that raster scan at various spacing. So if you’ve got a defect that you wanna find that maximum 20 mil, we’ll just have a 20 mil step [00:03:00] size between each scan. If you’ve got a bigger tolerance, we can have 50 mil, a hundred mil it, it’s so tuneable and it removes any of the variability that you get from a human to human operator doing that scanning. And this is all about. Repeatable, consistent high quality data that you can then use to make real informed decisions about the state of those blades and act upon it. So this is not about, um, an alternative to humans. It’s just a better, it’s just an evolution of how humans do it. We can just do it really quick and it’s probably, we, we say it’s like six times faster than a human, but actually we’re 10 times faster. We don’t need to do any of the mapping out of the blade, but it’s all encoded all that data. We know where the robot is as we walk. That’s all captured. And then you end up with really. Consistent data. It doesn’t matter who’s operating a robot, the robot will have those settings preset and you just walk down the blade, get that data, and then our subject matter experts, they’re offline, you know, they are in their offices, warm, cozy offices, reviewing data from multiple sources of robots. And it’s about, you know, improving that [00:04:00] efficiency of getting that report out to the customer and letting ’em know what’s wrong with their blades, actually,  Allen Hall: because that’s always been the drawback of, with NDT. Is that I think the engineers have always wanted to go do it. There’s been crush core transportation damage, which is sometimes hard to see. You can maybe see a little bit of a wobble on the blade service, but you’re not sure what’s underneath. Bond line’s always an issue for engineering, but the cost to take a person, fly them out to look at a spot on a blade is really expensive, especially someone who is qualified. Yeah, so the, the difference now with play bug is you can have the technology to do the scan. Much faster and do a lot of blades, which is what the de market demand is right now to do a lot of blades simultaneously and get the same level of data by the review, by the same expert just sitting somewhere else.  Chris Cieslak: Absolutely.  Joel Saxum: I think that the quality of data is a, it’s something to touch on here because when you send someone out to the field, it’s like if, if, if I go, if I go to the wall here and you go to the wall here and we both take a paintbrush, we paint a little bit [00:05:00] different, you’re probably gonna be better. You’re gonna be able to reach higher spots than I can.  Allen Hall: This is true.  Joel Saxum: That’s true. It’s the same thing with like an NDT process. Now you’re taking the variability of the technician out of it as well. So the data quality collection at the source, that’s what played bug ducts.  Allen Hall: Yeah,  Joel Saxum: that’s the robotic processes. That is making sure that if I scan this, whatever it may be, LM 48.7 and I do another one and another one and another one, I’m gonna get a consistent set of quality data and then it’s goes to analysis. We can make real decisions off.  Allen Hall: Well, I, I think in today’s world now, especially with transportation damage and warranties, that they’re trying to pick up a lot of things at two years in that they could have picked up free installation. Yeah. Or lifting of the blades. That world is changing very rapidly. I think a lot of operators are getting smarter about this, but they haven’t thought about where do we go find the tool.  Speaker: Yeah.  Allen Hall: And, and I know Joel knows that, Hey, it, it’s Chris at Blade Bug. You need to call him and get to the technology. But I think for a lot of [00:06:00] operators around the world, they haven’t thought about the cost They’re paying the warranty costs, they’re paying the insurance costs they’re paying because they don’t have the set of data. And it’s not tremendously expensive to go do. But now the capability is here. What is the market saying? Is it, is it coming back to you now and saying, okay, let’s go. We gotta, we gotta mobilize. We need 10 of these blade bugs out here to go, go take a scan. Where, where, where are we at today?  Chris Cieslak: We’ve hads. Validation this year that this is needed. And it’s a case of we just need to be around for when they come back round for that because the, the issues that we’re looking for, you know, it solves the problem of these new big 80 a hundred meter plus blades that have issues, which shouldn’t. Frankly exist like process manufacturer issues, but they are there. They need to be investigated. If you’re an asset only, you wanna know that. Do I have a blade that’s likely to fail compared to one which is, which is okay? And sort of focus on that and not essentially remove any uncertainty or worry that you have about your assets. ’cause you can see other [00:07:00] turbine blades falling. Um, so we are trying to solve that problem. But at the same time, end of warranty claims, if you’re gonna be taken over these blades and doing the maintenance yourself, you wanna know that what you are being given. It hasn’t gotten any nasties lurking inside that’s gonna bite you. Joel Saxum: Yeah.  Chris Cieslak: Very expensively in a few years down the line. And so you wanna be able to, you know, tick a box, go, actually these are fine. Well actually these are problems. I, you need to give me some money so I can perform remedial work on these blades. And then you end of life, you know, how hard have they lived? Can you do an assessment to go, actually you can sweat these assets for longer. So we, we kind of see ourselves being, you know, useful right now for the new blades, but actually throughout the value chain of a life of a blade. People need to start seeing that NDT ultrasonic being one of them. We are working on other forms of NDT as well, but there are ways of using it to just really remove a lot of uncertainty and potential risk for that. You’re gonna end up paying through the, you know, through the, the roof wall because you’ve underestimated something or you’ve missed something, which you could have captured with a, with a quick inspection.  Joel Saxum: To [00:08:00] me, NDT has been floating around there, but it just hasn’t been as accessible or easy. The knowledge hasn’t been there about it, but the what it can do for an operator. In de-risking their fleet is amazing. They just need to understand it and know it. But you guys with the robotic technology to me, are bringing NDT to the masses  Chris Cieslak: Yeah.  Joel Saxum: In a way that hasn’t been able to be done, done before  Chris Cieslak: that. And that that’s, we, we are trying to really just be able to roll it out at a way that you’re not limited to those limited experts in the composite NDT world. So we wanna work with them, with the C-N-C-C-I-C NDTs of this world because they are the expertise in composite. So being able to interpret those, those scams. Is not a quick thing to become proficient at. So we are like, okay, let’s work with these people, but let’s give them the best quality data, consistent data that we possibly can and let’s remove those barriers of those limited people so we can roll it out to the masses. Yeah, and we are that sort of next level of information where it isn’t just seen as like a nice to have, it’s like an essential to have, but just how [00:09:00] we see it now. It’s not NDT is no longer like, it’s the last thing that we would look at. It should be just part of the drones. It should inspection, be part of the internal crawlers regimes. Yeah, it’s just part of it. ’cause there isn’t one type of inspection that ticks all the boxes. There isn’t silver bullet of NDT. And so it’s just making sure that you use the right system for the right inspection type. And so it’s complementary to drones, it’s complimentary to the internal drones, uh, crawlers. It’s just the next level to give you certainty. Remove any, you know, if you see something indicated on a a on a photograph. That doesn’t tell you the true picture of what’s going on with the structure. So this is really about, okay, I’ve got an indication of something there. Let’s find out what that really is. And then with that information you can go, right, I know a repair schedule is gonna take this long. The downtime of that turbine’s gonna be this long and you can plan it in. ’cause everyone’s already got limited budgets, which I think why NDT hasn’t taken off as it should have done because nobody’s got money for more inspections. Right. Even though there is a money saving to be had long term, everyone is fighting [00:10:00] fires and you know, they’ve really got a limited inspection budget. Drone prices or drone inspections have come down. It’s sort, sort of rise to the bottom. But with that next value add to really add certainty to what you’re trying to inspect without, you know, you go to do a day repair and it ends up being three months or something like, well  Allen Hall: that’s the lightning,  Joel Saxum: right? Allen Hall: Yeah. Lightning is the, the one case where every time you start to scarf. The exterior of the blade, you’re not sure how deep that’s going and how expensive it is. Yeah, and it always amazes me when we talk to a customer and they’re started like, well, you know, it’s gonna be a foot wide scarf, and now we’re into 10 meters and now we’re on the inside. Yeah. And the outside. Why did you not do an NDT? It seems like money well spent Yeah. To do, especially if you have a, a quantity of them. And I think the quantity is a key now because in the US there’s 75,000 turbines worldwide, several hundred thousand turbines. The number of turbines is there. The number of problems is there. It makes more financial sense today than ever because drone [00:11:00]information has come down on cost. And the internal rovers though expensive has also come down on cost. NDT has also come down where it’s now available to the masses. Yeah. But it has been such a mental barrier. That barrier has to go away. If we’re going going to keep blades in operation for 25, 30 years, I  Joel Saxum: mean, we’re seeing no  Allen Hall: way you can do it  Joel Saxum: otherwise. We’re seeing serial defects. But the only way that you can inspect and or control them is with NDT now.  Allen Hall: Sure.  Joel Saxum: And if we would’ve been on this years ago, we wouldn’t have so many, what is our term? Blade liberations liberating  Chris Cieslak: blades.  Joel Saxum: Right, right.  Allen Hall: What about blade route? Can the robot get around the blade route and see for the bushings and the insert issues? Chris Cieslak: Yeah, so the robot can, we can walk circumferentially around that blade route and we can look for issues which are affecting thousands of blades. Especially in North America. Yeah.  Allen Hall: Oh yeah.  Chris Cieslak: So that is an area that is. You know, we are lucky that we’ve got, um, a warehouse full of blade samples or route down to tip, and we were able to sort of calibrate, verify, prove everything in our facility to [00:12:00] then take out to the field because that is just, you know, NDT of bushings is great, whether it’s ultrasonic or whether we’re using like CMS, uh, type systems as well. But we can really just say, okay, this is the area where the problem is. This needs to be resolved. And then, you know, we go to some of the companies that can resolve those issues with it. And this is really about played by being part of a group of technologies working together to give overall solutions  Allen Hall: because the robot’s not that big. It could be taken up tower relatively easily, put on the root of the blade, told to walk around it. You gotta scan now, you know. It’s a lot easier than trying to put a technician on ropes out there for sure.  Chris Cieslak: Yeah.  Allen Hall: And the speed up it.  Joel Saxum: So let’s talk about execution then for a second. When that goes to the field from you, someone says, Chris needs some help, what does it look like? How does it work?  Chris Cieslak: Once we get a call out, um, we’ll do a site assessment. We’ve got all our rams, everything in place. You know, we’ve been on turbines. We know the process of getting out there. We’re all GWO qualified and go to site and do their work. Um, for us, we can [00:13:00] turn up on site, unload the van, the robot is on a blade in less than an hour. Ready to inspect? Yep. Typically half an hour. You know, if we’ve been on that same turbine a number of times, it’s somewhere just like clockwork. You know, muscle memory comes in, you’ve got all those processes down, um, and then it’s just scanning. Our robot operator just presses a button and we just watch it perform scans. And as I said, you know, we are not necessarily the NDT experts. We obviously are very mindful of NDT and know what scans look like. But if there’s any issues, we have a styling, we dial in remote to our supplement expert, they can actually remotely take control, change the settings, parameters.  Allen Hall: Wow.  Chris Cieslak: And so they’re virtually present and that’s one of the beauties, you know, you don’t need to have people on site. You can have our general, um, robot techs to do the work, but you still have that comfort of knowing that the data is being overlooked if need be by those experts.  Joel Saxum: The next level, um, commercial evolution would be being able to lease the kit to someone and or have ISPs do it for [00:14:00] you guys kinda globally, or what is the thought  Chris Cieslak: there? Absolutely. So. Yeah, so we to, to really roll this out, we just wanna have people operate in the robots as if it’s like a drone. So drone inspection companies are a classic company that we see perfectly aligned with. You’ve got the sky specs of this world, you know, you’ve got drone operator, they do a scan, they can find something, put the robot up there and get that next level of information always straight away and feed that into their systems to give that insight into that customer. Um, you know, be it an OEM who’s got a small service team, they can all be trained up. You’ve got general turbine technicians. They’ve all got G We working at height. That’s all you need to operate the bay by road, but you don’t need to have the RAA level qualified people, which are in short supply anyway. Let them do the jobs that we are not gonna solve. They can do the big repairs we are taking away, you know, another problem for them, but giving them insights that make their job easier and more successful by removing any of those surprises when they’re gonna do that work.  Allen Hall: So what’s the plans for 2026 then? Chris Cieslak: 2026 for us is to pick up where 2025 should have ended. [00:15:00] So we were, we were meant to be in the States. Yeah. On some projects that got postponed until 26. So it’s really, for us North America is, um, what we’re really, as you said, there’s seven, 5,000 turbines there, but there’s also a lot of, um, turbines with known issues that we can help determine which blades are affected. And that involves blades on the ground, that involves blades, uh, that are flying. So. For us, we wanna get out to the states as soon as possible, so we’re working with some of the OEMs and, and essentially some of the asset owners.  Allen Hall: Chris, it’s so great to meet you in person and talk about the latest that’s happening. Thank you. With Blade Bug, if people need to get ahold of you or Blade Bug, how do they do that?  Chris Cieslak: I, I would say LinkedIn is probably the best place to find myself and also Blade Bug and contact us, um, through that.  Allen Hall: Alright, great. Thanks Chris for joining us and we will see you at the next. So hopefully in America, come to America sometime. We’d love to see you there.  Chris Cieslak: Thank you very [00:16:00] much.

The unlikely crew of Alien returns this week after a short hiatus. Our party proceeds to get geared up, and commit to a bit. Find out this and much more this week on WLP Tries Alien the TTRPG. If you want more content from us consider supporting us on Patreon! Warp Lords is a product of Bandit Gang Entertainment, and the game is used with their permission. Buy the game, take the ride! Buy/Download Warp Lords Here Follow our Sosh-Meds! Warp Lords Podcast Tweeter: @WarpLordsPod Warp Lords Tweeter: @Warplords Warp Lords Facebook: Warp Lords Warp Lords Podcast Patreon: Demand an apology Warp Lords Podcast Tik Tok: @warplordspodcast Credits: GM (Alien The TTRPG): Graham Banas Birger Hedenstrom: Mike Danger Vautour Sezja IDontRememberYourLastNameBaby: Jared Cryan Noor Sajad: Dillon Morin Music: Jared Cryan Editing: Devin Malinowski Art: Mike "Danger" Vautour If you like what you heard, then please spread the word. Any characters, items, animals, blob monsters, trees, instruments, bad voices, manic lawyers, power tools, pocket pickles or shitty jokes that bear resemblance to another intellectual property or otherwise non-original content are used in parody or satire or other harmless ways and are in no way related to or a depiction of another subject in or around reality. This is a silly podcast with silly people, and is not intended to be taken seriously by anyone in any way.

For the resources and links mentioned, go to:  https://simplysquaredaway.com/157

Seth and Sean discuss how the Astros hope to sort out their lineup issues and react to Greg Amsinger calling the 2019 Astros the 7th best regular season team of all time.

On this Frontier flight, a lady gets into it with passengers and flight crew even before the doors were closed...----Yeeessss... The first of the 2026 live MFB 'Status: Arrested' Tour tickets are now on sale.Keep an eye out for more dates to be announced real soon.MELBOURNE - APRIL 11 - 1:30PM - BASEMENT COMEDY CLUB MICFMELBOURNE - APRIL 18 - 1:30PM - BASEMENT COMEDY CLUB MICFCody's show CRU$HER is hitting all major centres and more in 2026. He's back. It's red hot. Fuckin' do it. Stop going to shit comedians who charge double and deliver half.-----------------------------------YOUR STUPID has arrived. It's a book. It's a similar vibe to last year's one, but better. If you want a copy, head over to lukeheggie.com and stump up, and it will arrive via Australia Post. Any First Class Patrons, yours have been posted, (including the seppos - at great personal expense) but excluding the three bastards who have not provided an address, and seem to refuse to reply to emails. Sort it out. I'll bring some to live shows too. That is all.Heggie's 2026 show I WON'T SAY IT AGAIN is on sale now too. It's a hand-selected crack team of bits from the last five years. Get on it here.-----------------------------------Heggie dropped a FOURTH YouTube special, GROT, but still left the comments closed like a coward. Watch it here.Cody's new stand-up special "LIVE AT THE CORNER HOTEL" is OUT NOW on YouTubeHave a squizz and leave comments before he takes Heggie's cowardly route and turns off the comments. Hosted on Acast. See acast.com/privacy for more information.

Sorry about the audio, I promise we will figure it out!  Hit us up at morrisonicpod at gmail Here is the Soccerwise pod that gives a better Colorado preview than we could do https://overcast.fm/+ABJGTFYORMs      

Avec Malou Mallerin, Melvin Schlemer et David Bola pour les nouveautés !

Howdy!The Big 60s Sort Out is back! And this time we are looking across the pond to the USA to rank all the US Number 1 singles from the 1960s for lyrics, music and production.In this pre-series episode, we catch-up with some Beatles news and look back at some of the very first US charts, from sheet music sales in the 1910s, to the first consolidated pop music chart in 1958, and we talk about what we might expect over the coming series!Thanks for joining us as we continue to try and sort, the 60s!

It's back to the archives of Dragon magazine for DMs Rob and Matt. When is giving characters too much information too much of a good thing? Tune in to find out! Dragon Magazine #161: https://archive.org/details/DragonMagazine260_201801/DragonMagazine161/page/20/mode/2up  Music: Pac Div - Roll the Dice Follow Dungeon Master of None on Blue Sky: https://bsky.app/profile/dmofnone.bsky.social  More socials Join our Patreon for bonus episodes: https://www.patreon.com/DungeonMasterOfNone   Join the DMofNone Discord!

YELLOW DIAMONDS LOOK LIKE PEE PEE Slap City picks: "Momma I'm So Sorry" by Clipse, "Naatu Naatu" by Rahul Sipligunj & Kaala Bhairava .Listen to our playlist here Join us in 2 weeks when we'll discuss our next pick, Hellogoodbye's Zombies! Aliens! Vampires! Dinosaurs!

This is the audio version of the Naavik Digest newsletter published on February 22nd, 2026. We explore how niche subgenres – Block, Sort, and Screw – are reshaping the mobile puzzle market.You can read the newsletter (with even more sections and visual detail) here: https://www.naavik.co/digest/how-niche-subgenres-are-reshaping-the-mobile-puzzle-marketMeet us at GDC 2026 by filling out this short form: https://naavik.typeform.com/to/gVDtj4UO Let us know what you think by sending us a note at podcast@naavik.co.Watch our episodes: YouTube ChannelFor more episodes and details: Podcast WebsiteFree newsletter: Naavik DigestFollow us: Twitter | LinkedIn | WebsiteSound design by Gavin Mc Cabe.

Jeff Ross Preaching

This week Colin joins Brad to discuss various topics including God of War: Sons of Sparta, Lovish, the recent State of Play, and more! Please keep in mind that our timestamps are approximate, and will often be slightly off due to dynamic ad placement. 0:00:00 - Intro0:23:18 - State of Play thoughts0:54:41 - God of War Sons of Sparta1:07:18 - Front Mission 1st1:23:22 - Turok 1+21:39:49 - Sort it Out/Keep it Up2:06:55 - Game Recommendation2:09:12 - Lovish2:17:54 - The Legend of Zelda: Majora's Mask 3D2:48:02 - Astalon: Tears of the Earth2:53:16 - Closing Questions To watch the podcast on YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/LastStandMediaYouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Don't forget to subscribe to the podcast for free wherever you're listening or by using this link: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/SummonSign⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ If you like the show, telling a friend about it would be amazing! You can text, email, Tweet, or send this link to a friend: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/SummonSign⁠⁠ Learn more about your ad choices. Visit podcastchoices.com/adchoices

Direction la Syrie où le mystère règne autour du sort des familles de djihadistes qui étaient détenues dans un immense camp. Étaient car elles ont presque toutes disparues. En fait, depuis fin janvier, le camp n'est plus gardé par les Kurdes mais par l'armée syrienne et on les a laissé partir comme ça.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Chaque jour, en quelques minutes, un résumé de l'actualité culturelle. Rapide, facile, accessible.Notre compte InstagramDES LIENS POUR EN SAVOIR PLUSU2 : Radio France, HuffPost, France Info, NBC News, NRJMe at the Zoo : Euronews, Victoria and Albert Museum, BBCBad Bunny rôle “Porto Rico” : 20 Minutes, Numero, Vogue FranceMark Zuckerberg : Courrier International, Le Monde, RTL, Actus PopFélix Lebrun Marty Supreme : Le HuffPost, Vanity Fair, France BleuGemini Lyria 3 : Google, Numerama, ClubicJacob Elordi James Bond : Mandatory, USA Today, HuffPost UKÉcriture : Eden AyachIncarnation : Eden Ayach Hébergé par Acast. Visitez acast.com/privacy pour plus d'informations.

Le 21 février 1966, le général Charles de Gaulle surprend la communauté internationale en annonçant le retrait de la France du commandement militaire intégré de l'OTAN. Si la France reste membre de l'Alliance, elle affirme ainsi son indépendance stratégique et le contrôle de sa défense. Dans le contexte tendu de la guerre froide et des relations complexes avec les États-Unis, cette décision marque un tournant majeur de la diplomatie française.

Thu, 19 Feb 2026 22:15:00 GMT http://relay.fm/connected/591 http://relay.fm/connected/591 Find Your Lost Stephen 591 Federico Viticci, Stephen Hackett, and Myke Hurley The guys look ahead to Apple's March 4 event, talk through iOS 26.4 Beta 1, and ponder a world where Apple makes an AI-powered pendant. The guys look ahead to Apple's March 4 event, talk through iOS 26.4 Beta 1, and ponder a world where Apple makes an AI-powered pendant. clean 4657 The guys look ahead to Apple's March 4 event, talk through iOS 26.4 Beta 1, and ponder a world where Apple makes an AI-powered pendant. This episode of Connected is sponsored by: Sentry: Mobile crash reporting and app monitoring. New users get $100 in Sentry credits with code connected26. Insta360: Introducing the Insta360 Wave and the Link 2 Pro. Fundera, powered by NerdWallet: Compare real financing offers from trusted lenders — all in one place. Get VIP treatment using this link. Links and Show Notes: Get Connected Pro: Preshow, postshow, no ads. Submit Feedback OpenClaw Creator Peter Steinberger Joins OpenAI - MacStories GameSir is making a GameHub app for Mac. | The Verge NPC: Next Portable Console and NPC XL - MacStories Foveated Streaming | Apple Developer Documentation Apple Announces Special Event in New York, London, and Shanghai on March 4 - MacRumors Apple Announces a March 4th Press Event - MacStories Apple's March launch may include multiple days of press releases with no keynote, per rumor - 9to5Mac Someone Tell John Ternus This Would Be a Terrible Crime - 512 Pixels Upgrade #603: Recalibrate the Quality Bar - Relay iOS 26.4 beta 1: Here are the new iPhone features - 9to5Mac The Sentence Returns with iOS 26.4, Sort of - MacStories iOS 26.4 Beta Tidbits: Hidden Features You May Have Missed - MacRumors Apple Ramps Up Work on Glasses, Pendant and Camera

What does a “streamlined” UK border look like? And if you vote for the MP of one party, should they be allowed to defect to another? These are just two questions Jen and Mick pose as they rootle around this week's news. What did they find? Well, there's a new immigration policy bothering British dual-citizens and, upsettingly as ever, more from the man who'd like to bother many more besides: Nigel *groans forever* Farage. There's better news from the EU about companies discarding textiles, as well as a sexism Olympics alongside a JOTB report on the regular one. Learn more about your ad choices. Visit megaphone.fm/adchoices

Thu, 19 Feb 2026 22:15:00 GMT http://relay.fm/connected/591 http://relay.fm/connected/591 Federico Viticci, Stephen Hackett, and Myke Hurley The guys look ahead to Apple's March 4 event, talk through iOS 26.4 Beta 1, and ponder a world where Apple makes an AI-powered pendant. The guys look ahead to Apple's March 4 event, talk through iOS 26.4 Beta 1, and ponder a world where Apple makes an AI-powered pendant. clean 4657 The guys look ahead to Apple's March 4 event, talk through iOS 26.4 Beta 1, and ponder a world where Apple makes an AI-powered pendant. This episode of Connected is sponsored by: Sentry: Mobile crash reporting and app monitoring. New users get $100 in Sentry credits with code connected26. Insta360: Introducing the Insta360 Wave and the Link 2 Pro. Fundera, powered by NerdWallet: Compare real financing offers from trusted lenders — all in one place. Get VIP treatment using this link. Links and Show Notes: Get Connected Pro: Preshow, postshow, no ads. Submit Feedback OpenClaw Creator Peter Steinberger Joins OpenAI - MacStories GameSir is making a GameHub app for Mac. | The Verge NPC: Next Portable Console and NPC XL - MacStories Foveated Streaming | Apple Developer Documentation Apple Announces Special Event in New York, London, and Shanghai on March 4 - MacRumors Apple Announces a March 4th Press Event - MacStories Apple's March launch may include multiple days of press releases with no keynote, per rumor - 9to5Mac Someone Tell John Ternus This Would Be a Terrible Crime - 512 Pixels Upgrade #603: Recalibrate the Quality Bar - Relay iOS 26.4 beta 1: Here are the new iPhone features - 9to5Mac The Sentence Returns with iOS 26.4, Sort of - MacStories iOS 26.4 Beta Tidbits: Hidden Features You May Have Missed - MacRumors Apple Ramps Up Work on Glasses, Pendant

This week on SolFul Connections, we interrupt our regularly scheduled deep conversations to discuss:• Celebrity family drama• Public text message chaos• High School Musical friendships• And the ongoing debate over the proper way to stapleI gathered a panel of thoughtful, intelligent women and asked them to care deeply.They did not.But they showed up anyway.Sometimes the conversations that “don't matter” reveal more than we expect... about how we think, how we judge, how we laugh, and how we connect.This one is semi ridiculous.It's honest.It's slightly unhinged.And yes… it's still SolFul. SolFul Living Meditation BundleIf you're looking for guided meditations to help you navigate fear, overwhelm, or simply return to yourself, you can explore the SolFul Living Meditation Bundle here:https://solfuliving.com/10-minute-meditations/

Nick is joined by Mirror man David Yates to discuss the issues of the day from around the racing world. Acknowledging Constitution Hill making the cut at Southwell, they move quickly on to the latest developments in the sport's own battle with itself in establishing a new governance model. ROA Council member Sam Hoskins joins the debate. Plus, with the pubclication of the Grand National weights, we hear from Panic Attack's owner Bryan Drew (also has news on Final Demand) and Henry de Bromhead, who has 5 in the National and also goes through key members of his Festival squad. JA McGrath has the latest from Hong Kong.

In Episode 23 of Season 3, the Slow Burn Cast Pat (NintenTalk), Min (Min's Meadow), and Payton (Payton'sCorner) are joined with Rogersbase to discuss several topics including Next Fest demos, First Impressions of Pokopia, Listener Emails + MORE!!Join the OFFICIAL Slow Burn Discord: https://discord.gg/6wxYg3S7BMSlow Burn Podcast on Youtube: https://www.youtube.com/@SlowBurnCast

Nick is joined by Mirror man David Yates to discuss the issues of the day from around the racing world. Acknowledging Constitution Hill making the cut at Southwell, they move quickly on to the latest developments in the sport's own battle with itself in establishing a new governance model. ROA Council member Sam Hoskins joins the debate. Plus, with the pubclication of the Grand National weights, we hear from Panic Attack's owner Bryan Drew (also has news on Final Demand) and Henry de Bromhead, who has 5 in the National and also goes through key members of his Festival squad. JA McGrath has the latest from Hong Kong.

Another away day, another win. Archie, Alex and Chris are here for some midweek JOY.With all that said… this was not as glorious on the eye as some other away wins. A boxing analogy from Michael Savage sums things up nicely – scrappy, relentless and a clash of styles, but still much to like. The loss of another midfielder to another hamstring injury did not impact as many feared, and Argyle worked through the gears to grind out a convincing win. Pepple scoring again, Curtis looking increasingly like a signing for the ages, solid performances all round and mental fortitude in the face of an equaliser all feature in a satisfying, grinding three points.Part 2 takes time to pause and reflect on quite how far this team has come. On and off the field, there remains much to fix, but lots to like. Can we dream of the play-offs? Maybe we can, but more likely next year – time to play the kids or just stick with a winning formula? Thoughts turn to a huge game – and a huge crowd – for the women's team and a free hit for the men against the best team in the division.All in all, a doom-free zone. Sort of.COYG Hosted on Acast. See acast.com/privacy for more information.

Bryan and Nick are starting a new adventure! Deep dives into the stand-up comedian world. You remember how Bryan and Nick are Toastmasters? (of course you do, they only mention it every chance they get) Well, as Toastmasters they have gotten in the habit of evaluating people speaking. It was only a short step to get to their new podcast, "Laughs Per Minute". They will be doing a deep dive with a guest into stand up comedians by evaluating them up and down, backwards and forwards. We will be releasing episodes every 2nd and 4th Tuesday of each month. So if you even slightly enjoyed listening to Help Yourself, then you'll for sure slightly enjoy Laughs Per Minute. 

Lynette is back from a mild concussion and she tells us the story plus Stef got a dog named Babalu and she explains the saga plus asks for advice. Sort of.Sleep soundly with Boll & Branch. Get 15% off your first order plus free shipping at BollAndBranch.com/fcol with code fcolFor a limited time, Nutrafol is offering our listeners $10 off your first month's subscription and free shipping when you visit Nutrafol.com and enter promo code FCOLFor a limited time only, our listeners are getting an insane deal. Get up to 57% Off and a Free Gift with code FCOL at FirstDay.com.

Sana Starros occupies many roles in the Star Wars universe - academic, smuggler, hero, and also Han Solo's first wife. Sort of. We unpack her origins, her accomplishments and some chaotic romance.See omnystudio.com/listener for privacy information.

https://www.mlssoccer.com/news/mls-jersey-week-see-every-new-kit-for-the-2026-season    Check out: Portland Vanity Soccer Podcast Outer Roses Timbers Review Stuptown Footy  Cascadia FC Conifers & Citrus

Air Date 10/15/02 Thank you so much for listening to NYPD Blue Balls! If you enjoyed this episode, we encourage you to rate and review the show on whatever podcast platform you happen to be listening on. Make sure to follow us on Instagram @asherbrotherspodcast For more content like this, follow Kirk on X (Twitter), Instagram & TikTok: @kirkhasglasses. Watch Kirk play video games and watch old movies: Youtube: https://www.youtube.com/channel/UCFgQ5XvqltSyP5a2UpPvUyQ Twitch: https://www.twitch.tv/kirkhasglasses Join the Discord: discord.gg/GKPU6

AI is the new Google? Sort of! In this video, I break down how AI search tools like ChatGPT, Gemini, and Perplexity are changing the way people find content. I'll show you how to optimize your content for both traditional SEO and the new AI-driven search. Learn why long form content, schema, and your unique expertise matter more than ever for getting found in AI chatbots.

Your friends Trey Mitchell, Austin Guttery and Connor Risenhoover talk Video Games 00:00:33 - Open00:03:04 - We Have Clips00:05:25 - News00:06:24 - Nintendo Partner Direct00:13:10 - New Microsoft Console? 00:18:00 - AI in Games00:18:40 - What's Your Favorite Pokemon Commercial00:22:36 - Horizon Zero Dawn 3?00:25:18 - Discord Discourse00:29:30 - Ten 2000's Retro games that aren't as good as you remember00:35:50 - Pokémon Pokopia00:36:58 - Playstation State of Play predicts (That already happened)00:38:52 - Austin likes Kingdom Hearts now00:43:23 - What You Been Playin -  Hades 200:47:00 - Lego Party00:50:10 - Kingdom Come Deliverance 2 00:51:52 - High on Life00:58:33 - Games Coming Out This Week01:04:10 - This Week in Gaming01:09:15 - Video Game Draft Talk01:12:00 - Game of the Week

Your friends Trey Mitchell, Austin Guttery and Connor Risenhoover talk Video Games 00:00:33 - Open00:03:04 - We Have Clips00:05:25 - News00:06:24 - Nintendo Partner Direct00:13:10 - New Microsoft Console? 00:18:00 - AI in Games00:18:40 - What's Your Favorite Pokemon Commercial00:22:36 - Horizon Zero Dawn 3?00:25:18 - Discord Discourse00:29:30 - Ten 2000's Retro games that aren't as good as you remember00:35:50 - Pokémon Pokopia00:36:58 - Playstation State of Play predicts (That already happened)00:38:52 - Austin likes Kingdom Hearts now00:43:23 - What You Been Playin -  Hades 200:47:00 - Lego Party00:50:10 - Kingdom Come Deliverance 2 00:51:52 - High on Life00:58:33 - Games Coming Out This Week01:04:10 - This Week in Gaming01:09:15 - Video Game Draft Talk01:12:00 - Game of the Week

The start to many slick shows here in Texas and it's a great way to start. We are extremely grateful for the opportunity to sit down with Mr. Callis and dive through all the classes. It's awesome to hear his perspective on the differences between types and kinds of cattle. We can't wait for the rest of Texas major show season but we are thrilled to be able to discuss cattle with Mr. Callis. Empowerment Is Here.

This week, Matty and Lock join Brad to discuss several of the Resident Evil remakes, Nioh 3, The Outer Worlds 2, and more! Please keep in mind that our timestamps are approximate, and will often be slightly off due to dynamic ad placement. 0:00:00 - Intro0:03:04 - Resident Evil 2 + 3 Remake0:49:57 - Nioh 31:06:38 - Sort it Out/ Keep It Up1:37:57 - Game Recommendation1:38:32 - Resident Evil 4 Remake1:47:35 - The Outer Worlds 22:02:31 - Closing Questions Learn more about your ad choices. Visit podcastchoices.com/adchoices

“How should I sort through everything the Church teaches?” This question opens a discussion on navigating the complexities of Church doctrine. Additionally, the episode addresses intriguing topics such as why St. Michael is recognized as a saint despite being an angel, and whether a Protestant can participate in Confession, providing a well-rounded exploration of faith. Join the Catholic Answers Live Club Newsletter Invite our apologists to speak at your parish! Visit Catholicanswersspeakers.com Questions Covered: 05:58 – How should I sort through everything the Church teaches? 13:20 – How come St. Michael is a saint if he is an angel? 20:28 – What’s the response to the claim that Mary and Joseph did not have very much faith because they were afraid and didn’t know where to look when they lost Jesus? 33:10 – Can a Protestant go to Confession? 42:50 – How is the Eucharist a propitiatory sacrifice and how is it applied?

The Bastards, the robbers, and their hitchhiking friend Yorp are trapped in the Big Momma Fishing shop. Fortunately they've taken the shopkeeper hostage and discover a mysterious powder, which diffuses the situation. (Not.) As negotiations unravel, Yorp calls in a favor from the strange vessel he's been mumbling to, and a whole swarm of his friends arrive to smooth things over. (Sort of.) At the end of the day, all that really matters is that everybody ate, Cleo's got clean pants, Yorp's mission is intact, and The Bastards have a ship full of apocalyptic bioweapons and a renewed dream of a free MidSpace… For everyone. Starring: DM Dick Dynamite the Dungeon Master -- ⁠⁠⁠⁠⁠⁠⁠Richard Kimber-Bell⁠⁠⁠⁠⁠⁠⁠ Cleo deCap / M8 -- ⁠⁠⁠⁠⁠⁠⁠Taylor van Biljon⁠⁠⁠⁠⁠⁠⁠ Dr Ze/Doctor Zafrey Elektra --⁠⁠⁠⁠⁠⁠ ⁠Daniel Matthews⁠⁠⁠⁠⁠⁠⁠ Yorp -- ⁠Bodie⁠ Episode art by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bodie⁠ Ambiance sound support by ⁠⁠⁠⁠⁠⁠⁠Jamie Nord⁠⁠⁠⁠⁠⁠⁠ and ⁠⁠⁠⁠⁠⁠⁠Michaël Ghelfi⁠⁠⁠⁠⁠⁠⁠ Synth Music Karl Casey @ ⁠⁠⁠⁠⁠⁠⁠White Bat Audio⁠⁠⁠⁠⁠⁠⁠ Episode Edit / Sound design by Daniel Matthews Distributed by ⁠⁠⁠⁠⁠⁠⁠Realm⁠⁠⁠⁠⁠⁠⁠ - Send inquiries and fanart to backwaterbastards@gmail.com Support the show and gain access to extra content by joining our ⁠⁠⁠⁠⁠⁠⁠Patreon⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/Backwaterbastards⁠⁠⁠⁠⁠⁠⁠ If you love what you hear, share us with a friend! Find everything else on our website at ⁠⁠⁠⁠⁠⁠⁠www.backwaterbastards.com⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠Join our Discord⁠⁠⁠⁠⁠⁠⁠! Learn more about your ad choices. Visit megaphone.fm/adchoices

Ben Solak of ESPN joins Afternoon Drive on The Fan. He talks about quarterback scarcity this offseason, why he projected Malik Willis to the Browns, how much better they can be in 2026, and more.

What does it mean when Human Design doesn't just interest you… but deeply pulls you? In this final episode of the series, we explore the difference between casually learning Human Design and feeling genuinely, spiritually called to understand it at a deeper level. We know that knowing yourself is powerful. But understanding the system well enough to guide others? That's a different level of commitment. In this episode, we cover:→ What it actually means to feel “called” to Human Design→ Why nuance matters — and what gets lost when Human Design is oversimplified→ The responsibility that comes with reading charts and guiding others→ Why reducing people to labels (“I'm this type”) misses the depth of the system→ How mastering Human Design helps you see people more clearly — not judge them→ Why our collective future depends on people who understand this system with integrity Let's get into it! PS – This episode is Part 4/4 of YOUR FUTURE DEPENDS ON KNOWING WHO YOU ARE (and Human Design Shows You How)

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

durée : 00:58:20 - Cultures Monde - par : Julie Gacon, Mélanie Chalandon - Alors que les représentants européens et kazakhstanais se sont retrouvés à deux reprises en décembre 2025, Bruxelles tente de se rapprocher d'Astana en jouant la carte d'un partenariat "d'égal à égal", à rebours des impérialismes russe et chinois. - réalisation : Vivian Lecuivre - invités : Catherine Poujol Professeure d'histoire de l'Asie centrale à l'INALCO et ancienne directrice de l'IFEAC (Institut Français d'Études sur l'Asie Centrale); Ikboljon Qoraboyev Professeur de relations internationales et directeur fondateur du Centre d'études de la gouvernance globale et régionale de l'université Maqsut Narikbayev à Astana; Yéléna Mac-Glandières Doctorante à l'Institut Français de Géopolitique (IFG), thèse en cours intitulée "Géopolitique de l'émancipation en mer Caspienne : de la concurrence énergétique à la coopération logistique en Azerbaïdjan - Kazakhstan - Turkménistan".

Still guessing on Amazon listing images? Today's guest shares a simple AI image workflow that makes decisions easy—what to fix first, what to test, and how to know it'll win.   If your AI-generated Amazon images look “technically perfect” but still don't convert, you're not alone, and you're not missing more prompts. In this AIM (AI Monthly) session, our Amazon creatives expert guest breaks down the real issue. AI and designers can execute, but they can't decide strategy for you. That's why sellers often spend thousands on creatives that look good, yet still fail to drive more clicks and sales.   Hannah Lyss Tampioc is the Founder and CEO of Mad Cat Creatives, and her team has worked with more than 300 brands. She walks through how shoppers actually buy on Amazon and explains why each image serves a different purpose. Your main image needs to stand out in mobile search results. Images two and three should help shoppers quickly understand what they're getting. The rest of your images and A+ Content should build confidence by answering objections and removing hesitation. The key is figuring out whether you have a click problem or a conversion problem, then fixing the right part of your image stack instead of randomly “refreshing” everything.   The centerpiece of the episode is Hannah's SORT framework. First, you spot the priority so you know what to fix first. Next, you gather the right context by pulling mobile search screenshots, competitor pages, reviews, and Rufus questions. Then you use that information to reason through the data, so your AI outputs are based on real buyer language instead of guesses. Finally, you test before committing by validating your image ideas with polling tools like the Helium 10 Audience tool, powered by PickFu, before you publish. You'll also see a real example using Bradley Sutton's Project X Coffin Shelf listing, where small changes like aspect ratio, mobile-first sizing, and packaging callouts helped the main image stand out more when it mattered most. By the end, you'll know exactly what to fix first and how to follow a repeatable AI Amazon image workflow that confirms your next update will win before you publish. In episode 495 of the AM/PM Podcast, Bradley and Hannah discuss: 00:00 – Introduction 01:27 – The Missing Ingredient To Your Amazon Listing Images 02:31 – Meet Hannah & The “Named My Son Helium” Story 06:05 – The Real Issue: Strategy Beats Design + AI 09:02 – The Job Of Each Amazon Image Stack 11:17 – The SORT Framework Overview 11:41 – Click Problem vs Conversion Problem (What To Fix First) 12:23 – What To Gather Before You Design Anything 23:17 – Coffin Shelf Case Study: Mobile Size Wins 30:46 – ChatGPT Prompts & Gemini Image Generation Workflow 35:05 – “Main Image In 10 Minutes” & Gemini Tip 37:03 – Secondary Images: Use GPT For Briefs + Prompting 39:50 – Helium 10 Discount Code SSP20 + Q&A 42:04 – Where To Find Hannah (LinkedIn) & Mad Cat Creatives

Whitney White is a theatrical powerhouse. A director, writer, actor, and musician, White's work has been seen on Broadway, Off Broadway, and at major institutions including The Public Theater, the Brooklyn Academy of Music, and, most recently, the Royal Shakespeare Company. Her projects include Jaja's African Hair Braiding, The Last Five Years, Macbeth in Stride, and By The Queen, which was featured in the Folger's 2025 Reading Room Festival. In this episode, White discusses All Is But Fantasy, her four-play musical cycle created for the RSC, where it's now receiving its world premiere. The high-energy, gig-theater show investigates Shakespeare's women and ambition, focusing on Lady Macbeth, Emilia, Juliet, and Richard III. Each piece combines performance with original music, using sound and rhythm as a way into the text and as a tool for rethinking these characters whose inner lives are often cut short or overlooked. White reflects on why Shakespeare's women so often meet tragic ends, how those stories continue to feel familiar, and what it means to keep staging them now. She considers the ways that music, performance, and adaptation can help us better understand Shakespeare today. From the Shakespeare Unlimited podcast. Published February 10, 2026. © Folger Shakespeare Library. All rights reserved. This episode was produced by Matt Frassica, with Garland Scott serving as executive producer. It was edited by Gail Kern Paster. Technical support was provided by Melvin Rickarby in Stratford, England, and Voice Trax West in Studio City, California. Web production was handled by Paola García Acuña. Transcripts are edited by Leonor Fernandez. Final mixing services were provided by Clean Cuts at Three Seas, Inc. Whitney White is an Obie and Lily Award-winning and Tony Award-nominated director, actor, and musician, celebrated for her bold, innovative storytelling across both Broadway and off-Broadway. She recently received the Drama League's 2025 Founders Award for Excellence in Directing and an Obie Award for Sustained Achievement in Directing. All Is But Fantasy, White's four-part musical exploration of Shakespeare's women and ambition, commissioned by the Royal Shakespeare Company, marks her RSC debut as a writer, director, and actor. The two-part high-energy gig theater show is receiving its world premiere at The Other Place in Stratford-upon-Avon in January and February 2026. White's other directing credits on Broadway include The Last Five Years and Jaja's African Hair Braiding, off-Broadway credits include Liberation, Walden, Jordan's, Soft, On Sugarland, What to Send Up When It Goes Down, Our Dear Drug Lord, and For All the Women Who Thought They Were Mad. She recently opened Saturday Church, a new musical featuring songs by Sia and Honey Dijon at New York Theatre Workshop. She also created Macbeth In Stride at Brooklyn Academy of Music, writing the book, music and lyrics. Additional directing work includes The Secret Life of Bees, By The Queen, The Spectacularly Lamentable Trial of Miz Martha Washington, A Human Being of a Sort, An Iliad, The Amen Corner, Othello, Canyon, and Jump. On screen, White has appeared in Ocean's Eight, Single Drunk Female, Louie, and The Playboy Club, and she contributed as a writer to Boots Riley's acclaimed series I'm A Virgo for Prime Video.

Love to hear from you; “Send us a Text Message”Feeling whiplash from a culture that calls chaos freedom and opinion truth? We step back and ask the question that reframes the noise: what sort of tale have we fallen into? From the first lines of Genesis to the streets of our cities, we trace how order leads to freedom, how evil only distorts what is good, and why the human heart is the primary battleground. Along the way, we confront the early wounds of porn, the pull of relativism, and the emptiness of use, then chart a concrete path toward healing and purpose.We lean into the wisdom of St. John Paul II's Theology of the Body, exploring why life is a love story set in a real conflict between good and evil. YIf you're tired of drifting with the spirit of the age and ready to live on purpose, this conversation offers a map: order over chaos, communion over isolation, self-gift over self-grasping. Grab the Claymore battle plan and start the Claymore 10-minute morning ritual! Share this episode with a friend who needs hope. Subscribe, leave a review, and tell us: what tale do you think we've fallen into?Discussion Questions• How does the “dictatorship of moral relativism” appear in your own life, and how can Saint John Paul II's Theology of the Body help you recover meaning and direction?• What experiences of awe or beauty have stirred your heart, and how might they be leading you toward God's love story? (John 1:38–39)• In what concrete ways can you step back from the world's noise, including social media and pornography, in order to hear Christ's invitation, “Come and see”? (See the Claymore Battle Plan Outline and Catechism of the Catholic Church, no. 2709)  Support the show

Our Global Head of Fixed Income Research Andrew Sheets and Global Chief Economist Seth Carpenter unpack the inner workings of the Federal Reserve to illustrate the challenges that Fed chair nominee Kevin Warsh may face.Read more insights from Morgan Stanley.----- Transcript ----- Andrew Sheets: Welcome to Thoughts on the Market. I'm Andrew Sheets, Global Head of Fixed Income Research at Morgan Stanley. Seth Carpenter: And I'm Seth Carpenter, Morgan Stanley's Global Chief Economist and Head of Macro Research. Andrew Sheets: And today on the podcast, a further discussion of a new Fed chair and the challenges they may face. It's Friday, February 6th at 1 pm in New York. Seth, it's great to be here talking with you, and I really want to continue a conversation that listeners have been hearing on this podcast over this week about a new nominee to chair the Federal Reserve: Kevin Warsh. And you are the perfect person to talk about this, not just because you lead our economic research and our macro research, but you've also worked at the Fed. You've seen the inner workings of this organization and what a new Fed chair is going to have to deal with. So, maybe just for some broad framing, when you saw this announcement come out, what were some of the first things to go through your mind? Seth Carpenter: I will say first and foremost, Kevin Warsh's name was one of the names that had regularly come up when the White House was providing names of people they were considering in lots of news cycles. So, I think the first thing that's critically important from my perspective, is – not a shock, right? Sort of a known quantity. Second, when we think about these really important positions, there's a whole range of possible outcomes. And I would've said that of the four names that were in the final set of four that we kept hearing about in the news a lot. You know, some differences here and there across them, but none of them was substantially outside of what I would think of as mainstream sort of thinking. Nothing excessively unorthodox at all like that. So, in that regard as well, I think it should keep anybody from jumping to any big conclusions that there's a huge change that's imminent. I think the other thing that's really important is the monetary policy of the Federal Reserve really is made by a committee. The Federal Open Market Committee and committee matters in these cases. The Fed has been under lots of scrutiny, under lots of pressure, depending on how you want to put it. And so, as a result, there's a lot of discussion within the institution about their independence, making sure they stick very scrupulously to their congressionally given mandate of stable prices, full employment. And so, what does that mean in practice? That means in practice, to get a substantially different outcome from what the committee would've done otherwise… So, the market is pricing; what's the market pricing for the funds rate at the end of this year? About 3.2 percent. Andrew Sheets: Something like that. Yeah. Seth Carpenter: Yeah. So that's a reasonable forecast. It's not too far away from our house view. For us to end up with a policy rate that's substantially away from that – call it 1 percentage, 2 percentage points away from that. I just don't see that as likely to happen. Because the committee can be led, can be swayed by the chair, but not to the tune of 1 or 2 percentage points. And so, I think for all those reasons, there wasn't that much surprise and there wasn't, for me, a big reason to fully reevaluate where we think the Fed's going. Andrew Sheets: So let me actually dig into that a little bit more because I know our listeners tune in every day to hear a lot about government meetings. But this is a case where that really matters because I think there can sometimes be a misperception around the power of this position. And it's both one of the most public important positions in the world of finance. And yet, as you mentioned, it is overseeing a committee where the majority matters. And so, can you take us just a little bit inside those discussions? I mean, how does the Fed Chair interact with their colleagues? How do they try to convince them and persuade them to take a particular course of action? Seth Carpenter: Great question. And you're right, I sort of spent a bunch of time there at the Fed. I started when Greenspan was chair. I worked under the Bernanke Fed. And of course, for the end of that, Janet Yellen was the vice chair. So, I've worked with her. Jay Powell was on the committee the whole time. So, the cast of characters quite familiar and the process is important. So, I would say a few things. The chair convenes the meetings; the chair creates the agenda for the meeting. The chair directs the staff on what the policy documents are that the committee is going to get. So, there's a huge amount of influence, let's say, there. But in order to actually get a specific outcome, there really is a vote. And we only have to look back a couple weeks to the last FOMC meeting when there were two dissents against the policy decision. So, dissents are not super common. They don't happen at every single meeting, but they're not unheard of by any stretch of the imagination either. And if we go back over the past few years, lots going on with inflation and how the economy was going was uncertain. Chair Powell took some dissents. If we go back to the financial crisis Chair Bernanke took a bunch of dissents. If we go back even further through time, Paul Volcker, when he was there trying to staunch the flow of the high inflation of the 1970s, faced a lot of resistance within his committee. And reportedly threatened to quit if he couldn't get his way. And had to be very aggressive in trying to bring the committee along. So, the chair has to find a way to bring the committee along with the plan that the chair wants to execute. Lots of tools at their disposal, but not endless power or influence. Does that make sense? Andrew Sheets: That makes complete sense. So, maybe my final question, Seth, is this is a tough job. This is a tough job in… Seth Carpenter: You mean your job and my job, or… Andrew Sheets: [Laughs] Not at all. The chair of the Fed. And it seems especially tricky now. You know, inflation is above the Fed's target. Interest rates are still elevated. You know, certainly mortgage rates are still higher than a lot of Americans are used to over the last several years. And asset prices are high. You know, the valuation of the equity market is high. The level of credit spreads is tight. So, you could say, well, financial conditions are already quite easy, which can create some complications. I am sure Kevin Warsh is receiving lots of advice from lots of different angles. But, you know, if you think about what you've seen from the Fed over the years, what would be your advice to a new Fed chair – and to navigate some of these challenges? Seth Carpenter: I think first and foremost, you are absolutely right. This is a tough job in the best of times, and we are in some of the most difficult and difficult to understand macroeconomic times right now. So, you noted interest rates being high, mortgage rates being high. There's very much an eye of the beholder phenomenon going on here. Now you're younger than I am. The first mortgage I had. It was eight and a half percent. Andrew Sheets: Hmm. Seth Carpenter: I bought a house in 2000 or something like that. So, by those standards, mortgage rates are actually quite low. So, it really comes down to a little bit of what you're used to. And I think that fact translates into lots of other places. So, inflation is now much higher than the committee's target. Call it 3 percent inflation instead core inflation on PCE, rather than 2 percent inflation target. Now, on the one hand that's clearly missing their target and the Fed has been missing their target for years. And we know that tariffs are pushing up inflation, at least for consumer goods. And Chair Powell and this committee have said they get that. They think that inflation will be temporary, and so they're going to look through that inflation. So again, there's a lot of judgment going on here. The labor market is quite weak. Andrew Sheets: Hmm. Seth Carpenter: We don't have the latest months worth of job market data because of the government shutdown; that'll be delayed by a few days. But we know that at the end of last year, non-farm payrolls were running well below 50,000. Under most circumstances, you would say that is a clear indication of a super weak economy. But! But if we look at aggregate spending data, GDP, private-domestic final purchases, consumer spending, CapEx spending. It's actually pretty solid right now. And so again, that sense of judgment; what's the signal you're going to look for? That's very, very difficult right now, and that's part of what the chair is going to have to do to try to bring the committee together, in order to come to a decision. So, one intellectually coherent argument is – the main way you could get strong aggregate demand, strong spending numbers, strong GDP numbers, but with pretty tepid labor force growth is if productivity is running higher and if productivity is going higher because of AI, for example, over time you could easily expect that to be disinflationary. And if it's disinflationary, then you can cut it. Interest rates now. Not worry as much as you would normally about high inflation. And so, the result could be a lower path for policy rates. So that's one version of the argument that I suspect you're going to hear. On the other hand, inflation is high and it's been high for years. So what does that mean? Well. History suggests that if inflation stays too high for too long, inflation psychology starts to change the way businesses start to set. Andrew Sheets: Mm-hmm. Seth Carpenter: Their own prices can get a little bit loosey-goosey. They might not have to worry as much about consumers being as picky because everybody's got used to these price changes. Consumers might be become less picky because, well, they're kind of sick of shopping around. They might be more willing to accept those higher prices, and that's how things snowball. So, I do think that the new chair is going to face a particularly difficult situation in leading a committee in particularly challenging times. But I've gone on for a long, long time there. And one of the things that I love about getting to talk to you, Andrew, is the fact that you also talked to lots of investors all around the world. You're based in London. And so when the topic of the new Fed chair comes up, what are the questions that you're getting from clients? Andrew Sheets: So, I think that there are a few questions that stand out. I mean, I think a dominant question among investors was around the stability of the U.S. dollar. And so, you could say a good development on the back of Kevin Warsh's nomination is that the market response to that has been the price action you would associate with more stability. You've seen the dollar rise; you've seen precious metals prices fall. You've seen equity markets and credit spreads be very stable. So, I think so far everything in the market reaction is to your; to the point that you raised, you know, consistent with this still being orthodox policy. Every Fed chair is different, but still more similar than different now. I think where it gets more divergent in client opinions is just – what are we going to see from the Fed? Are we going to see a real big change in policy? And I think that this is where there are very different views of Kevin Warsh from investors. Some who say, ‘Well, he's in the past talked about fighting inflation more aggressively, which would imply tighter policy.' And he's also talked more recently about the productivity gains from AI and how that might support lower interest rates. So, I think that there's going to be a lot of interest when he starts to speak publicly, when we see testimony in front of the Senate. I think the other, the final piece, which I think again, people do not have as fully formed an opinion on yet is – how does he lead the Fed if the data is unexpected? And you know, you mentioned inflation and, you know, Morgan Stanley has this forecast that: Well, owner's equivalent rent, a really key part of inflation, might be a little bit higher than expected, which might be a distortion coming off of the government shutdown and impacts on data. But there's some real uncertainty about the inflation path over the near term. And so, in short, I think investors are going to give the benefit of the doubt. For now, I think they're going to lean more into this idea that it will be generally consistent with the Fed easing policy over time, for now. Generally consistent with a steeper curve for now. But I think there's a lot we're going to find out over the next couple of weeks and months. Seth Carpenter: Yeah. No, I agree with you. Andrew, I have to say, I'm glad you're here in New York. It's always great to sit down and talk to you. Let's do it again before too long. Andrew Sheets: Absolutely, Seth. Thanks for taking the time to talk. And to our audience, thank you as always for your time. If you find Thoughts the Market useful, let us know by leaving a review wherever you listen. And also tell a friend or colleague about us today.