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Tiff and Dana address one of the most popular topics for Dental A-Team consultants: overhead! They talk about what it entails, where to start when looking to reduce it, critical questions to ask yourself about needs versus wants, and more. Episode resources: Subscribe to The Dental A-Team podcast Schedule a Practice Assessment Leave us a review Transcript: Tiff (00:00) Hello, Dental A Team listeners. Thank you for being here with us today. Thank you for listening. We say this every time, but we love what we do and we love bringing you so much valuable information. And the fact that Kiera can do all the podcasts she does blows my mind. ⁓ but she is a busy bee over there, and the fact that we get to do these as well is just really, really fun for us. It allows all of the consultants here on our team to really feel like we're giving back to you guys. So with that, I have Dana here with me today, and   Dana, gosh, we have been podcasting together for a really long time. I can't even put a number to it. And I remember, I don't know if you remember, but I remember I remember where I was sitting. I remember the thought process. And I remember it was me, you and Britt on a call on a Zoom link. And it was the first time marketing had said we want to do video with the podcast. And I was like, what? And video like was not, it was just like up and coming.   I didn't understand it. It was on Instagram. I was watching I was like, why am I watching you talk? Like the a podcast is to listen. Why am I watching you talk? And now I mean it's very normal and that's how I watch them. And I feel like I feel like it was like YouTube came back around, you know. But anyways, I remember that day vividly. ⁓ I don't remember what we were talking about, but I remember being like, I have to like do my hair. I'm gonna be seen.   DAT-Dana (01:23) Yeah. Yeah. I know it was   funny because we always could see each other, right, in those early days, but it was just like we weren't creating the video content for it. And I remember thinking exactly like who's gonna want to watch   Tiff (01:33) Yes.   DAT-Dana (01:35) us who's gonna want to watch us do this thing but then I see my kids literally like watching people play Minecraft and it's like their favorite thing and I'm like wouldn't it be more fun to actually go play? So I do feel like there is definitely this like niche of people like wanting to watch and like you know get a glimpse in of like the podcast world and just different worlds in general and so I agree with you. I remember the three of us just kind of being like who's gonna want to watch us talk to each other but hey we're so glad you're here.   Tiff (01:37) Yeah.   Yes.   It's true.   Yeah.   DAT-Dana (02:05) Yeah.   Tiff (02:06) Yes, I agree. And the three fur podcasts are hard. So hard when there's so many people virtually. And yeah, I r I remember the shock. I wish I could remember what the ⁓ podcast actually it was probably I bet you it was probably one that we did for Kiera. We probably it bosses day or something, yeah, 'cause if there are multiple of us. Anyways, that was that popped into my head this morning as I I always have to now have like prep for podcast time so I can like   DAT-Dana (02:12) Yeah.   Like Boss's Day or something like that. Yeah.   Tiff (02:35) just tame my hair or get my ring light just right. And I'm like, gosh, I remember the days that we did not have to do this. And then we have c new to Dental A Team consultants come on and I'm like, we're gonna podcast. And they're like stressed and I'm like, I get it. I just I get it. I saw them go talk yourself in the mirror for a bit first. You'll get used to it.   DAT-Dana (02:50) Yeah. Yeah.   I know   I remember in the early days I would always have to reframe my podcast because I'd see podcasting on my schedule and I'm like, ⁓ like I gotta get on. So then I just started reframing it. It was like time with Tiff, time with Britt, time with Kiera. And it's how I like kind of learn get over the like of the podcasting space. So I totally feel it when new consultants are like, I have my first podcast today.   Tiff (03:12) I love that.   Yeah,   yeah, and they all come to you, right? 'Cause I'll all schedule it and then they're like, Dana, what do I do? That's so cute. Yeah. I love the reframe. That actually like goes I think hand in hand with what we're talking about today. ⁓ but I think you can do that with anything and I have to remind myself, even like gosh, when I get up in the morning, I got up this morning and I went from for my walk and I was like, ⁓ this sucks and I was like, No, you get to be in the morning sun.   You get to move your body before anybody else in the house is awake. Like I think that's the part that's the hardest is like everybody else gets to sleep, you know? But you that reframe is so powerful. And we can look at a schedule and think I I look at my schedule and I'm like, shoot. This is so busy. Or gosh, I'm I'm like   So long today, and I have to reframe it often and be like, gosh, no, actually I get to do something really cool. And I get to wake up and go for a walk and I get to do these things or I get to go to an office and I get to be boots on the ground with other people. So I love that you mentioned that reframe, Dana. That was really smart. So today's reframe, which I love, I think this is one of the most popular conversations that we have. We get a couple of things here at Dental A Team. ⁓   We love everything that we get, but the most common, most popular things are systems, which we will help you with systems, I promise you. And there are thousands of podcasts I think that just Dana and I have done on systems and operations manual. So go look them up. We're not doing that today. And the second, which I actually really have grown to truly love, ⁓ is overhead cost reduction and and overhead analysis. And so many practice owners and leaders come to us and they're like, gosh.   what does overhead even mean? I know I had a conversation with a client last week that has been in the dental like consulting world for years and years and years. And w his question was what does that even what does it mean? Like overhead can mean so many different things to so many different people and so many different consulting companies. And for the sake of today's conversation and the sake of forever with Dental A Team know that when we say overhead, we are talking about top of the line   Whatever I always say if someone were to purchase your practice, what are the expenses they'd be taking over? Anything outside of that, your pay, your taxes, your debt, your debt will follow you typically, right? You can lump it into the loan, ⁓ but it's not overhead top of the line expense. So your debt, meaning your scanners, ⁓ your school debt, anything like that is outside of quote unquote overhead. So when we talk about overhead, it's top of the line and that had to that that   explanation, I think it can just vary. It can vary depending on who you're talking to. So today we wanted to reframe that, Dana Go. No, I love it.   DAT-Dana (06:08) and I don't want to interrupt you, but I think too just   just to be clear on overhead too, anything that you run through the business, right? Again, that's not something absolutely with your CPA, you structure it how you want. But understand that that's not an expense that somebody is going to take on when they take over the bracket.   Tiff (06:25) Yes, I love that. Thank you. Good clarification. so with this kind of reframe, every everybody's like reduce overhead, reduce overhead. And I totally agree. And a lot of a lot of companies, a lot of people, ⁓ a lot of strategists will come in and they're like, okay, what can we cut? And we for sure, like, we'll come in and look at what if there's space to make cuts, but our biggest piece is always we're not gonna spend a lot of time on it today because we've got a million other podcasts about it.   I think I just did one actually with Kristy not that long ago, but the first place we're gonna look is your collections. A lot of people will say, I need to over I need to produce. And I love the statement, you can't outproduce your problems. So if you're producing, producing, producing, producing, but you're still feeling like there's an issue. And if you're meeting the financial, like you're meeting your goal, your production goal, but you're still cash flow short, then there's an issue in your collections. And so look at your collections and Dana.   I would love to hear quick snippet, what are the areas that you tackle when it comes to overhead and it comes to collections? And then I want to talk about the reframes and the other pieces.   DAT-Dana (07:33) Yeah, so you're exactly right. The first thing I'm gonna look at is the collections number. I'll look at the total, like what is the total percentage and like what profit point do we need to get to when it comes to collections? And then the very next thing I'm gonna look at is your AR because honestly and truly I've been able to get practices out of cash flow crisis, out of really feeling that pinch simply by going after already produced   ⁓ monies. And so I think that those are usually the things that I look at. Okay, what are we collecting? What does our profit point need to be for healthy AR?   Right. And and obviously we're gonna talk about is that possible? How do we get your schedule to get you there? But then the very next thing I'm gonna look at is AR. Is there money that I can just quickly tackle that's already been produced that's gonna help the collections problem? So I'm looking at the total collections, collections percentage, and then what's sitting in AR, because if I can tackle that and make a really quick difference, ⁓ sure, we can budget things, we can line item your PL, we can we can chop where we need to, but those things are often the fastest, easiest, quickest fixes.   and like you said, you like outproducing the problem. If I can fix AR and then we can create systems that it doesn't happen again, oftentimes we don't even have to really touch production, right? Because we're already producing pretty well in a lot of these cases. So those are that's kind of where I start.   Tiff (08:46) Yeah.   Yeah, I love that. And it's something that makes such a massive difference. Knowing one, knowing your numbers, knowing what your numbers mean. So knowing your overhead, knowing your outgoing expenses is massive. And then looking to see, okay, well, if these are my outgoing expenses, what do I need to collect in order to profit? Right. And then if we're not collecting that, is it because production isn't where it needs to be? So what's our what's our bare minimum?   And is collections meeting that or is production meeting that so that collections can meet our bare minimum. If production is or is way above and our collections is just tanked, like I saw somebody the other day that was like 83% collections. They're like, we gotta produce more. And I Yeah, absolutely. If we want to maintain 83% collections and get your overhead in line, you for sure have to produce more. But also we can tackle your collections and get your collections up to that ninety-eight percent that it should be or above, and really not have to work   you harder as the provider work our numbers harder and get that collections up. It also kind of flows into Dana, I think the capacity that we just recorded a podcast. So probably the podcast ahead of this one I would assume is is about capacity. And I think that capacity conversation flows into this one really, really well. So all right, collections.   Go do it. We will harp on that for days, but go do it. If you need help with it, you're not sure, you don't know how to analyze it, you need help with your numbers, Hello@TheDentalATeam.com. We are honestly and truly here to help you. We will provide you as much information as we possibly can to get you on the right track. Now, something else that we like to do within that, and we talked about this on capacity, we talked about analyzing ⁓ fee schedules, right? But then we also need to analyze expenses. So when we're really looking at things and we're saying, okay.   Great, this is my overhead. I like to think, okay, does it have to be my overhead though? So a lot of people will look at staff cost, the employee cost. I actually I look at it, I kind of glaze that, you guys. I don't, I don't like to touch the staff cost unless it absolutely is extraordinary and there's maybe team members that are taking advantage or you're feeling like there's something culturally wrong in your practice, then I'm gonna say, okay, great.   Let's really take a look at this and make sure that we're being efficient with our time. We're not in overtime. We're not in those spaces. But I'm gonna kind of glaze at that unless there's a red flag somewhere else. And then I'm gonna look at those other expenses as well. And something that I really love to do is to analyze what do we need versus what we have. It reminds me of when Brody was little, we'd go to the store and he'd be like, Mom, is this a want or a need? Is it on your list? Is you have are you getting it because you just want it and it sounds exciting?   Or do we actually need this? And Dana, I love the conversation that you have around. I'm gonna say like analyze your vendors, analyze your contracts with vendors, but I love the conversation around ⁓ the wants versus needs when it comes to scanners, when it comes to mills. And I love I I miss the conversation actually. I miss the conversation of negotiate with your labs. And I miss that conversation because   I think that the mill has become such a bandwagon thing. It's been around for so long and it's such a bandwagon thing that everybody's that jumped into. But I love your your like evaluation of is it necessary? Is it actually going to save us the time and the money and get us the results that we want? And I would love, Dana, for you to talk through some of that and how you help your clients decide. Because I'm not against the mill, I'm not for it. I'm for it for the practices that it works.   And I'm for making sure that it's going to work and it's gonna do its due diligence. So what how is that conversation for you, Dana, when you talk to your practices about it   DAT-Dana (12:44) Yes. I love this conversation too, too. I   think first and foremost, I always want to know when when somebody wants to purchase something big like that. So whether it's a new scanner or whether it's a mill, like why.   Why do we want to purchase it? Is it because we have a scanner that we constantly use and we're constantly pulling and we never have it in the like appointment times that we need? So then we need to talk about adding another scanner. Is it that like we need another tool to show patients, but like could we just do IOPs a little bit more until we've got the budget set for the scanner? I'm not saying no to scanners. I'm not saying no to mills. I'm just saying, why do we want it? Is it the right time and is it going to do what you anticipate it's going to do as far as your budget goes?   Because I think we can talk about scanners and what's going to add so much more to my production. Okay, well, it is, but when are we going to use it? How often are we going to use it? Who's going to use it? How are we mapping it out to make sure that it really is putting more production on your schedule and it really is reducing your lab fees? Right. Scanner is a great tool for negotiating with a lab, but are you going to do that? Are you going to do the negotiations? Are you going to send them enough work to make it worth having the scanner?   Same thing with the mill. I'm always asking like why, right? And I know that kind of the mill is the hot spot or the mill is like the next big thing. And I think sometimes, you know, I hear a lot from doctors, well, it's gonna buy me back a lot of time. Well, it's only gonna buy you back time if you're going to let your assistant, right, help design and do the actual milling. If you're not gonna let that happen, then we're actually using more of your time than and sometimes it's not will you let them, it's do you have the capacity within your assistant team right now to be able to allow them.   Tiff (14:07) Yeah.   Mm-hmm.   DAT-Dana (14:21) to do those things because maybe we're short staffed in that area or maybe assistants are really hard to find. Well then maybe now's not the time to bring on the mill because it's actually going to use more of your time versus less of your time. And then you know all of these purchases typically come with either a large payout, right? Or a decent size loan that we're paying every single month. And so I like to kind of reverse engineer with my practices so they know cold hard facts how many crowns they have to do every single month.   to make that loan payment worth it or make that payout out of their emergency fund or their growth fund or wherever they're pulling that funds from. Hopefully not their emergency funds, but sometimes right, doctors get wild on us and it feels like an emergency to get that.   Mill. So knowing exactly how many crowns you have to do every single month. And then I'm saying, okay, let's go back through the last year. Let's see, did we even do as many? Because if we didn't do as many, then now's not the time. Let's get to that many crowns every single month, then take a look at the mill. Because so often we think, hey, the mill is going to save me on lab fees, but you have to do so many of them for it to save you on lab fees. And again, I'm not pro mill. I'm not like I'm neutral when it comes to mill. I think it's a great tool, but it's not the best tool for every   Tiff (15:25) Yeah.   Mm-hmm.   DAT-Dana (15:35) practice at that exact time. I think you really have to look   At and crunch things when you decide to make those purchases and really look at it as is it truly going to give your time back? Is it truly going to give you your lab fees back? Is it truly going to up your patient experience or up your diagnosis or whatever it is? Because that is when it makes it worth it. So I just like to like have the conversation, review the numbers together, and kind of say, hey, like this is the reality of the purchase. I, you know, I am.   Totally understand the like purchase in the feels, right? I get that. I've done it. I'm human. I think we've all been like, but this is gonna feel so good when I have it. But I think look at the numbers and make sure because these things can really hit your these these debt services can really hit your profit points if it's not set up correctly and you don't know kind of the benchmarks you have to hit to make it help with profit versus hurt.   Tiff (16:11) Yeah.   Yeah.   Absolutely. I think it's so beautiful. And a follow-up to that too is if you already have the mill, you already have the scanner, you already made the purchase or the laser, Dana, as you were talking, I was like, the lasers, the lasers. There's so many there's just so many really cool tools that dentistry has that makes us feel like we've got to jump on it to be the most progressive, to be the most exciting, to stay up with the times, to to not fall behind. And really they're just fun and exciting. It's like   ⁓ Canva and you know we only had Photoshop and then Canva came out and then we had, you know, all of these different opportunities. And it it can be easy to jump on board with them. So if we already have jumped on board, we didn't have this conversation, or maybe we did, and then gosh, we're just falling a little bit short. This is the overhead analysis as well. This all flows into that overhead analysis. So as you're looking at your overhead and you see those   those loans under on you have your bottom you have your top line and you have a bottom line. And at your bottom line, when you see those other loans in there and you're like, gosh, Def, Dana, I just I'm not using the scanner as much as I thought I did. I know both of us have I all of our consultants are really, really fantastic at having conversations like this that say, okay, great, why? Dana, you said something earlier, you said it asking more questions, right? Like I want to know, I want to know why you want it.   what it's gonna do for your practice and then reverse engineer it. And we are really great at pulling out the why for anything. So if you're not, if you bought it and you're not using it, we're gonna say, well, why aren't we using it? Is it because it's not the tool that we needed or we wanted and or we don't have the patient base for it or is it because we're not trained, we're not holding accountabilities. And ultimately, if this thing isn't working for your practice, it's not doing what you wanted it to or gosh, you just hate it. You don't like it. You don't want to use it.   This is a conversation with the company that you can have. You can call the company and say, Hey, what can I do? How can I how can I get out of this? I've had ⁓ I've had doctors that have had this conversation with them and they do have like a smaller buyout, right? They're like, Well, we'll buy it back from you, but you're gonna it's kind of like taking a car in and you you're you know, you're under.   So you you owe a little bit more on your car and then you owe on the car that you're buying. So it kind of sucks because you do have to pay that out, but could getting out of that contract early, sending the equipment back, save you in the long run because you haven't paid that total balance. Or a lot of doctors will call and they're like, yeah, absolutely. I have a doctor actually who's looking for one that might buy it from you.   And so you can you can sell this equipment as well if it's not working for you. So I don't ever want doctors to really just feel so stuck in the decisions that either they've made or that they want to make and you have that kind of decision paralysis. So as we're going through that looking at ⁓ cost control and overhead control.   Part of the conversation as well. So there's the projecting side and really looking at do I do I need this? What can it do? And then there's the evaluation side of is this working for me? And Dana, I think that same conversation when it comes to like marketing. Are is my marketing ROI coming in? Is it getting me what I what I thought it was going to? There's magazines investments, there's all of these like hottie-totty ⁓ marketing efforts that are coming around right now. They're trying to like really reinvent a lot of wheels.   And projecting and seeing, does this fit my avatar? Is this gonna work? Gosh, your telephone company, I know our like cable and internet. We don't even have cable, but it's the same company, right? And I'm like, why are we paying for cable and internet? And it just jumped like $90. And I'm like, what the heck? It's a call and a conversation with your vendors and looking at, okay, am I getting the most value for what I'm spending? And that I think Dana helps us to calm the storm.   Because what happens typically is we're like, okay, I gotta produce more in order to afford my life. And it's just like personal, right? I gotta work more in order to afford the lifestyle that I want. Well, maybe the lifestyle that you want can be had with less debt or less stuff, you know, and really evaluating your quote unquote lifestyle in the practice and out.   DAT-Dana (20:43) Yeah, I agree with you because like dental offices, do we have to spend money? Do we have expenses? Yes, absolutely. Let's make sure those expenses are doing what we need them to do and and we have an ROI on those expenses. And I do feel like just doctors highlighting like, don't forget those bottom of the line things because oftentimes it's like, hey, my payroll's in line, my rent's in line, my marketing is in line, everything's in line, but I don't have any profit at the end of the month. And I think don't forget to take a look at oftentimes I think there's an impression of doctors that like those below the   aligned things are like fixed expenses and oftentimes they are variable expenses that we can do something about it. We can make changes like you said, sell it or start using it, right? Or incorporating a way for it to help us produce or collect more. I think just don't forget those bottom of the line things and don't look at them as hey, those are fixed things, right? A lot of times those items aren't. We can either move the needle as far as using them or move the needle as far as offloading them.   Tiff (21:15) Uh-huh.   Yes.   DAT-Dana (21:42) Right. I just had a conversation with the practice. Like, why do we have two scanners? Right. Like, why do we need them? Walk me through it. If if you can walk me through why and it makes sense, totally keep your scanners, utilize them, have it help you. Right. But if we don't need them, then let's not have that sit there every month and pull from that profit that you so desperately need.   Tiff (21:45) Mm-hmm.   Yeah, I love that conversation and I think it's something that's a piece of value that the consulting team brings to our clients that I think is totally undervalued. I know I have clients that are like, Teff, I wanna buy this thing. And I'm like, Okay, cool. Like, tell me why. How are we gonna afford it? Great. I have a doctor that was like, I like this scanner better, but I bought this scanner before I knew that this scanner was better. And I was like, Awesome. Well it sounds you want that scanner. He's like, Yeah, I'm gonna get it. And I said, Cool, what are you gonna do with that scanner that you don't like?   Because that one is still being paid on. It's still in your office. And he's like, okay. So it's like we have this innate ability, right, to see things very, very cleanly. I had a conversation just last week with a client that was like, Tiff, what do I do? And it was like a personnel thing, right? I said, Listen, my job and the and the superpower that I have for you is to be very black and white in business. I'm not emotionally attached to what's going on in the practice. I I love you, I love the practice, I love the team.   And I I have emotions towards you, but I'm able to separate it out and say, hey, do this, don't do this, or these are the black and white opinions that I see. These are the pros and the cons that I can see. I'm not emotionally attached to one scanner is better than the other. I'm emotional, I'm not emotionally attached to the money that's coming in or going out. I am neutral and I'm able to say it is or it isn't. And so that value, that ROI is not always really easy to see.   in the numbers until you look backwards and say, gosh, actually I sold that scanner because of or I didn't buy that and gosh, I'm so happy. Or I was able to invest in my team because I could see my shortcomings or my accountability faults or the accountability that Dana was able to give me so that I could give my team like those spaces are just so   valuable in this overhead analysis is huge. And I know you and I do it often. I know the rest of the consulting team does. Gosh, Kristy, Kiera likes to say she's like a truffle hunting ⁓ little, you know, little piggy out there finding the dollars. And that's how she does it as well. And Nikki and Pam and all of you know, Diana, every one of us are out there looking for those dollars from that black and white kind of business mindset because it's easier for us as a pulled out   Peace, right? And Dana, I just think that is a space that doctors, I can't imagine making those kinds of decisions by myself, right? Even just as simple as purchasing a mill. Like because it's so it's like walk walking into Louis Vuitton with a credit card with no limits and expecting me to not leave with a purse, right? Because in my head it's paid for, it's done, it's it's good.   But then on the flip side, I've got expenses and other things and they've always got just gotta have that person who can be that sound mind.   DAT-Dana (24:58) Yeah. Yep. I agree with you.   Tiff (25:00) All right, Dana, so overhead cost analysis. ⁓ I would say, and I think Dana, add anything you can think of. My pro thought process is figure out your bottom line first of all. Figure out what are your costs, your fixed costs that aren't changing. If someone were to purchase your practice, then then look at what's left over. How much debt do you have? what do you want to be making? Are you paying yourself and are you paying yourself what you want to be making?   And are you saving money? So what do those buckets look like? That to me is your is your bare minimum. You have your bare minimum of this is what it takes to keep my practice open and my employees paid. And then you have your bare minimum of this is what I want my practice to look like. So I like to add that fluff in there. I know Dana does as well. We have our bare minimum and then we have our bare minimum. And our our second bare minimum is the number that I work from ⁓ and tack on a little bit extra. So overhead analysis, look at what your numbers are, look at what your   DAT-Dana (25:46) How many? Yeah.   Tiff (25:55) Collecting, always look at collections and then look at what your debt looks like and look at what your spending is. Is there anywhere in there that can be negotiated? Is there anywhere in there that maybe we need to start using a tool a little bit more to get it paid, paying for itself? Just like you want your team to pay for themselves, you want your equipment to pay for themselves as well. Dana, is there anything you can think of that I missed that I didn't add in there as an action item that they can scurry on home to do?   DAT-Dana (26:24) No, I think I think that those are great tools for them to really be able to slice and dice and look at those pieces.   Tiff (26:31) Awesome. All right, guys, go do the thing. Pull up your PLs, pull up month by month, pull up year to date, pull up last year's, and look at what your expenses truly are. And when you get to the point that you want some third-party perspective, some eyes on it, if you're a current client, you should be doing this with your consultant too. So do it. I want you to know how to do it and I want you to do it with your consultant as well. If you're not yet a consultant, you're ⁓ someone who is a listener and you want you're not a consultant, you're not a client.   You're a listener and you want help with this, please reach out. Hello@TheDentalATeam.com There's also a link on our website, TheDentalATeam.com, that you can schedule a consult with us and they'll help you run through a lot of that information as well. We are here to help. So let us know how we can best serve you and how we can help you in the short and the long run. Hello@TheDentalATeam.com. All right, guys, and we will catch you next time. Thanks so much.  

What happens when a creative mind, a startup operator, a cancer survivor, and a humanitarian all live inside the same person?In this episode of Clover, I sit down with Melissa Wood, founder of Formis and Curate, to explore the winding path that led her from a small town in North Carolina to the startup ecosystem in Austin. Melissa's story spans early tech startups, design and photography, turning down life-changing opportunities, surviving cancer, humanitarian work in Ethiopia, and building companies rooted in solving real-world problems.What stood out most was Melissa's ability to notice gaps others accept as normal;  and then build solutions around them. Whether helping homeowners navigate renovations through Formis or simplifying conference experiences through Curate, her work is driven by a simple question: “Why doesn't something better exist?”This conversation is about resilience, leadership, community, and the unexpected ways life experiences shape the companies we build.In this episode, we discuss:How an accidental discovery of Photoshop in the mid-1990s launched Melissa's career in technology and design.Why she walked away from opportunities—including an early chance to join the team behind Me.com—and how she evaluates big decisions.The life-changing impact of surviving cancer and how that experience influenced her approach to work, family, and entrepreneurship.What she learned from living and working in Ethiopia, including lessons about community, presence, loyalty, and leadership.How she built Curate, an AI-powered event discovery platform, in just days using no-code and AI tools after identifying a problem she'd personally experienced for years.Why trust breaks down in industries like home renovation and how technology can create transparency, alignment, and accountability.Notable Quotes“Trust isn't built in one big moment. It's built through patterns.”“The best products are born from real experiences.”“If the system reflects that someone is being heard and understood, it creates a feeling of partnership.”Resources & Links MentionedFormis – AI-powered platform designed to improve transparency and communication in home renovation projects.Curate – Event discovery and scheduling platform helping people navigate conferences, tech events, and community gatherings.Lovable – AI-powered development platform Melissa used to build an early version of Curate.Claude – AI assistant Melissa uses for ongoing product development and iteration.FoundHers – Austin-based organization supporting women founders and entrepreneurs.South by Southwest (SXSW) – The event experience that inspired Curate.Austin Tech WeekLA Tech WeekAustin TV FestivalMelissa's story is a reminder that entrepreneurship doesn't always begin with a grand vision. Sometimes it starts with a frustration, a life experience, or a problem you simply can't stop thinking about and the courage to do something about it.

Qué tal, queridos Curiosinautas. Bienvenidos a un nuevo CuriosiMartes, el resumen semanal de noticias tecno del tío Fabián.Esta semana viene cargada: el regreso inesperado de Commodore con un teléfono retro pensado para comunicarse sin caer en redes sociales, los problemas de Android 17 en los propios Pixel, la nueva guerra tecnológica entre China, Estados Unidos y la Unión Europea, y un cambio clave en Apple con la posible llegada de una etapa más enfocada en diseño y hardware.Además, hablamos de inteligencia artificial en serio: la salida de figuras clave de Google DeepMind y Meta, las advertencias de Sam Altman sobre una IA que podría superar intelectualmente a los humanos, el concepto de “rendición cognitiva” y el riesgo de dejar de pensar por depender demasiado de los chatbots.También exploramos el futuro de la robótica versátil junto a DEEPRobotics, con avances en robots cuadrúpedos, embodied AI, automatización y nuevos formatos como el M20 y su pequeño compañero robótico.La robótica ya no es solo industrial: empieza a mezclarse con asistencia, autonomía, seguridad, compañía y nuevas formas de interacción con el mundo físico.Y para cerrar, una noticia que parece ciencia ficción: Midjourney Medical trabaja en un sistema de escaneo corporal con medio millón de sensores ultrasónicos, pensado para analizar el cuerpo completo en menos de 60 segundos y ayudar en la detección temprana de enfermedades.

The company is also previewing an upgraded creative AI studio experience. Learn more about your ad choices. Visit podcastchoices.com/adchoices

What happens when someone who grew up in the Lucasfilm Games golden era decides that today's AI tools are failing creatives? Mike Levine has spent more than 30 years building at the intersection of games, XR, VFX, and interactive storytelling—and his verdict is clear: the current AI stack is a fragmented, overcomplicated mess that turns directors into prompt engineers.Mike started as a tester at Lucasfilm Games (later LucasArts), working his way into the art department on titles like Sam & Max and The Dig before helping ship live-action Star Wars games such as Rebel Assault and Jedi Knight II. He later built rotoscoping tools used across the VFX industry, collaborated with ILM and Pixar, experimented with mobile AR games for Hasbro and HoloLens, and dipped into crypto gaming—before finally co-founding MovieFlow (now FilmSpark), an AI-native production platform designed so that filmmakers, agencies, and showrunners can move from script to screen without needing a computer science degree.The AI XR news you should know: Apple taps Google Gemini to power Siri, acknowledging that building world-class LLMs in-house makes little financial sense. Meta cuts 10% of Reality Labs, right-sizing its VR bets while pivoting toward wearables. Xreal raises another $100M amid questions about Chinese state influence and data flows. Higgs Field lands $80M at a $1.3B valuation for AI cinematography tools that many filmmakers still find unreliable. Wikipedia signs licensing deals with major AI companies after years of being scraped for free. OpenAI invests $252M in Sam Altman–backed Merge Labs, raising fresh conflict-of-interest questions.Key Moments Timestamps:[00:23:02] From Boston journalist-to-be to accidental hire at Lucasfilm Games[00:26:24] The “test pit” culture at Lucas and how Nintendo experience got Mike in the door[00:28:45] Moving into the art department, learning Photoshop from early legends, and shipping Sam & Max[00:31:15] Live-action Star Wars games: Rebel Assault, Jedi Knight II, and convincing George Lucas[00:34:38] Visiting Pixar with new VFX tools and recognizing the same creative “magic” as LucasArts[00:36:24] Doug Trumbull's influence on Mike's sense of cinematic possibility and immersion[00:43:27] The urinal meeting at Magic Leap and what early spatial computing got right (and wrong)[00:49:00] Why most AI tools are “dark ages” for filmmakers: node graphs, 10+ subscriptions, no story view[00:51:00] Building MovieFlow/FilmSpark: story-first, timeline-based AI production for long-form and vertical shows[00:53:00] The Neighborhood Podcast: a 90-second vertical murder mystery as proof-of-concept for AI-native seriesWhen humans can generate shots, scenes, and even entire episodes in minutes, the bottleneck shifts from production to vision. Mike argues that the winning AI tools will be the ones that let directors see their whole story, maintain continuity, and iterate fast—without ever feeling like they left the edit bay for a dev console. His vertical drama collaboration with Charlie, The Neighborhood Podcast, is an early look at what happens when narrative craft meets AI-native pipelines instead of fighting them.This episode is brought to you by Zapar creators of Mattercraft—the leading visual development environment for building immersive 3D web experiences. Build smarter at mattercraft.io.Watch the full episode on YouTube and subscribe to the AI XR Podcast for weekly conversations with the people building the future of AI, XR, and interactive media. Hosted on Acast. See acast.com/privacy for more information.

Herkese merhaba! Bu hafta yapay zeka dünyası kelimenin tam anlamıyla alev alev... Beyaz Saray'ın nükleer silah alarmına geçer gibi kısıtlamalar getirmesinden girdik , Çin'in Alibaba ve DeepSeek gibi devlerle bu duruma verdiği hızlı cevaplardan çıktık. Claude'un yeni MCP entegrasyonu sayesinde Photoshop ve çeşitli araçlarla bağlantı kurarak grafik tasarımcıları nasıl ihya ettiğini detaylıca anlattık. Bununla da kalmadık; Midjourney'nin sadece görsel üretmekle kalmayıp, ultrasonik ses dalgalarıyla çalışan ve MR çekimini bir "spa" keyfine dönüştüren yepyeni bir tıbbi tarama cihazı projesiyle Tıp dünyasına nasıl bomba gibi düştüğünü inceledik. Elon Musk'ın Grok hamleleri , Avrupa Birliği'nin 2 Ağustos'ta yürürlüğe girecek katı Yapay Zeka Yasası ve Meta'nın içeride yaşadığı büyük motivasyon krizi de masamızdaydı. Ayrıca yerli yapay zeka modelimiz TÜBİTAK Bilge'nin altyapısını ve Türk Telekom'un görme engelliler için geliştirdiği stadyum projesini de değerlendirdik. Peki sizce içeriklerde insan dokunuşu mu olmalı, yoksa yapay zeka da aynı tadı verebilir mi? Gerçekle yapay zeka arası sizin için fark eder mi? Yorumlarda kendi görüşlerinizi paylaşmayı unutmayın! Videoyu beğenmeyi, sevdiklerinizle paylaşmayı ve kanalımıza abone olmayı unutmayın, iyi seyirler! 00:00 - Giriş ve ABD'nin Nükleer Silah Statüsünde Yapay Zeka Kısıtlamaları 00:36 - Çin'in Hızlı Atağı: DeepSeek, Qwen ve Amerika'yı Tokatlamaya Hazır Veri Merkezleri 06:01 - Claude'dan Tasarımcılara Kıyak: MCP ile Photoshop Entegrasyonu 07:33 - Midjourney Tıp Dünyasında: MR Kalitesinde Ultrasonik Tarayıcı Spa Cihazı 12:22 - Grok 1.5 Video Modeli, Elon Musk'ın Destekleri ve Görme İmplantları 14:52 - Microsoft'un AWS'ye Geçişi ve Goldman Sachs'tan 7.6 Trilyon Dolarlık Yatırım Beklentisi 16:30 - Mistral "Le Chat" Yapay Zeka Memleri ve Test Tabloları 17:58 - Avrupa Birliği Yapay Zeka Yasası Geliyor: Şeffaflık Zorunluluğu ve Dev Cezalar 19:48 - Soyma Uygulamalarına ve İstismara Karşı Katı Avrupa Önlemleri 21:46 - Güney Kore'nin Endişeleri ve Yerli Yapay Zeka TÜBİTAK Bilge Tartışmaları 25:27 - Meta'nın Çöküşü: İşten Çıkarmalar ve "Cenaze Evi" Gibi Çalışma Ortamı 26:30 - Türk Telekom'un Görme Engelliler İçin Geliştirdiği Özel Stadyum Projesi 28:50 - Yapay Zekaya Karşı İnsanı Üstün Kılan Şey: Kusurlarımız ve Nüanslar 29:33 - Kapanış ve Yorumlarınızı Bekliyoruz #fable5 #claudemythos #yapayzeka

L’IA va-t-elle révolutionner la conception de sites web, ou les promesses dépassent-elles la réalité ? Olivier Sauvage, consultant et stratège du web, invité du live Visionary Marketing du 18 juin 2026, a apporté une réponse nuancée, documentée et parfois à contre-courant des discours dominants. Tour d’horizon des promesses réelles, des limites concrètes et des impacts sur les métiers. Sites Web, l’IA à toutes les étapes mais pas (encore) de miracles Sites Web conçus avec l’IA : Si Olivier Sauvage confirme que l’IA est présente à tous les instants, il pense néanmoins elle n’est cependant pas magique. Image d’Olivier Sauvage réalisée par lui-même sur son générateur d’images (oliviersauvage.com) La première question méritait d’être posée franchement : allons-nous continuer à faire des sites web ? La réponse d’Olivier Sauvage est catégorique : oui, et même davantage qu’avant. Les sites web ne disparaissent pas. Ils changent de rôle. L’IA est aujourd’hui présente à tous les stades de la chaîne de production web : outils de design (Figma intègre l’IA depuis longtemps), outils de prototypage, outils de retouche graphique (Photoshop), outils de test, outils de réflexion et de génération de contenu. On ne peut plus y échapper. La question n’est plus de savoir si l’on va intégrer l’IA, mais comment, à quel moment, et à quelles fins. En trois minutes, Stitch va te sortir des pages web là où il faudrait une journée ou deux pour un UX designer. Très impressionnant. Mais en réalité, ce n’est pas un outil qui a cette compréhension des choses que peut avoir un être humain quand il crée des maquettes. — Olivier Sauvage L’IA est un outil qui ouvre des horizons, explore des pistes qu’on n’aurait pas eu le temps d’explorer, accélère certaines phases de production. Ce n’est pas un substitut au métier. IA pour les sites web : les usages les plus solides aujourd’hui Le prototypage : un vrai gain C’est probablement l’usage le plus solide identifié par Olivier Sauvage. Le prototypage, notamment sur des applications mobiles ou des fonctionnalités complexes, était autrefois laborieux. Aujourd’hui, un outil comme Google Stitch permet de générer en quelques minutes des maquettes multi-supports (desktop, tablette, mobile) d’un niveau de réalisation crédible. L’avantage n’est pas seulement la vitesse : c’est la capacité à tester 4 ou 5 variantes là où l’on n’en produisait qu’une seule. On peut explorer des parcours utilisateurs différents, tester des architectures de navigation, obtenir un premier retour client sur quelque chose de visuellement représentatif, et ce bien avant d’engager un budget de développement. La génération d’arborescences et de tree-testing (test de tri de cartes) Autre usage robuste : la définition d’arborescences et le card sorting (tri de cartes, technique qui consiste à demander aux utilisateurs de classer des contenus pour identifier la structure de navigation la plus intuitive). L’IA fait gagner un temps considérable sur ces tâches de structuration de l’information, à condition d’alimenter l’outil avec des données suffisamment riches et spécifiques. Des personas génériques, sortis de nulle part, n’ont que peu de valeur. Des personas connectés à de vraies données de terrain, c’est une autre affaire. Avec l’IA, la conception des sites web n’a jamais été aussi rapide ni gratifiante, mais les itérations et les tests humains restent nécessaires nous dit Olivier Sauvage.Il ne faut pas rêver aux miracles et les web designers ne seront pas remplacés par Merlin l’enchanteur. Image réalisée avec Midjourney. La production d’interfaces : utile, avec supervision La production et la création d’interfaces bénéficient clairement de l’IA. Générer des composants, des variantes graphiques, des systèmes de design : tout cela est désormais accessible plus rapidement. Mais la supervision humaine reste indispensable pour valider que ce qui est produit correspond à la réalité de l’expérience utilisateur attendue. Ce qui ne fonctionne pas (encore) Simuler un comportement humain : une limite fondamentale Olivier Sauvage est catégorique sur un point : l’IA ne peut pas simuler un comportement utilisateur réel. Des personnages artificiels censés tester un site web à la place d’utilisateurs humains ? Je pense que ça ne marchera vraiment jamais. Il y a trop d’inconnues, trop de paramètres. Une IA se nourrit de ce qui existe. Elle ne sait pas ce qui est bon ou pas bon. Elle définit statistiquement ce qui est majoritaire, ce qui n’est pas un gage suffisant de qualité. — Olivier Sauvage Ce point est crucial : le web regorge d’interfaces médiocres. Une IA entraînée sur ce corpus va reproduire ces médiocrités avec une belle régularité statistique. Les sites 100 % IA : pour quels usages ? La question des sites entièrement générés par IA a été soulevée par un participant au live. Le verdict d’Olivier Sauvage est mesuré. Pour un site vitrine informatif d’une TPE locale, un site e-commerce B2C classique, c’est jouable, à condition d’une vérification humaine minimale. Pour un site B2B avec de la vente complexe, des parcours privés, une expérience riche, des animations : la limite est atteinte rapidement. « Les données nécessaires pour recréer des parcours UX valables sur du B2B complexe n’existent tout simplement pas en quantité suffisante. » Et le contenu ? C’est là que le bât blesse le plus. Le « slop » (contenu IA générique, interchangeable et sans valeur ajoutée) est déjà un problème visible. Générer des milliers d’articles en quelques minutes ne crée pas de valeur. Les moteurs de recherche et les utilisateurs s’en aperçoivent. Ce mouvement a un temps limité. La maintenabilité : le problème qu’on ne voit pas tout de suite J’ai cité un exemple vécu : un site d’association refait en 4 heures avec Claude, fonctionnellement supérieur à l’ancien, design convenable. Mais « le jour où la personne qui a développé ça s’en va, on fait quoi ? Qui va le retoucher ? Où est la base de données ? Quels sont les mots de passe ? » La dette technique invisible est l’un des vrais risques du vibe-coding (développement par description en langage naturel, sans écrire de code ligne par ligne) appliqué à des projets réels. Olivier Sauvage va plus loin en suggérant que les solutions no-code (outils permettant de créer des applications sans programmer, via des interfaces visuelles), moins spectaculaires mais structurellement plus solides, méritent d’être reconsidérées dans ce contexte. Des outils comme Airtable, Bubble ou TimeTonic offrent des garanties de maintenabilité que le code généré par IA ne peut pas toujours assurer. L’agent IA et l’avenir du e-commerce Un échange particulièrement intéressant a porté sur le protocole MCP (Model Context Protocol) et l’IA agentique appliquée au e-commerce. L’hypothèse est la suivante : demain, un agent IA pourra conduire une recherche produit, comparer des offres, poser des questions complémentaires, et passer à la transaction en ne donnant la main à l’utilisateur humain qu’au moment du paiement. Cela existe déjà partiellement : Shopify a adopté MCP, et ChatGPT intègre des fonctions marchandes dans certaines géographies. Ce qui change, c’est le rôle du site web : il reste indispensable, non plus comme destination première de navigation humaine, mais comme source de données structurées pour les agents IA. « Le site web va avoir encore une grande fonction : alimenter les IA par ses contenus. » Et Olivier Sauvage ajoute un point prospectif important : les marchands ont de plus en plus intérêt à produire des contenus spécifiques, propriétaires, qu’on ne peut trouver que sur leur site, et qui constituent une vraie barrière à l’imitation par l’IA générique. Premier signal concret de cette évolution : lors de ce live, j’ai mentionné la réservation d’une session photo dans mon studio par un client dont la recommandation initiale provenait de ChatGPT. Le trafic issu des LLM reste marginal, mais sa qualité est notable. Selon le rapport Adobe Digital Insights d’avril 2026, basé sur plus d’un milliard de visites e-commerce, le trafic provenant des LLMs convertit 42 % mieux que le trafic non-IA chez les retailers américains. Semrush va plus loin et mesure un ratio de 4,4× sur certains segments B2B logiciel, avec des taux de conversion de 15,9 % pour ChatGPT contre 1,76 % pour Google organique. Ces chiffres restent toutefois à nuancer : une étude Amsive portant sur 54 sites (septembre 2025) indique que 41 % des sites de l’échantillon convertissaient moins bien via LLM que via l’organique classique. Le résultat dépend du secteur et de la maturité du site. Impacts sur les métiers du design web Une transformation plus qu’une accélération Olivier Sauvage formule ici une thèse importante : l’IA transforme le métier de designer plus qu’elle ne l’accélère. Les gains de productivité purs ne sont pas aussi évidents qu’annoncés. On fait un prompt, on voit le résultat, on se dit c’est révolutionnaire. Puis en réalité, avant d’arriver à quelque chose de vraiment utilisable, on a fait 50 prompts, ce n’est jamais parfait, il faut mettre les mains dans le cambouis. — Olivier Sauvage La comparaison avec le développement est éclairante. Côté demande globale, les données TalentNeuron montrent que les offres d’emploi pour développeurs de logiciels ont progressé de 22 % entre 2023 et 2024, éclipsées toutefois par une hausse de 148 % sur les profils ingénieurs IA et machine learning. Mais côté marché français, la note de conjoncture de l’INSEE de mars 2026 dresse un tableau plus nuancé : l’emploi des moins de 30 ans dans l’informatique et les services d’information a reculé de 3 % entre 2023 et 2025, avec ‑7,4 % d’emploi des 15‑29 ans sur un an au T4 2025. Les entreprises produisent davantage, mais avec moins de juniors, remplacés en partie par l’IA sur les tâches répétitives. Les seniors, eux, passent plus de temps à corriger, structurer et documenter le code généré automatiquement. Ce n’est pas forcément moins de travail : c’est un travail différent. Une étude METR de juillet 2025 a même mesuré que des développeurs expérimentés étaient en réalité ralentis de 19 % avec Cursor Pro et Claude, alors qu’ils estimaient avoir gagné 20 % de productivité. L’écart entre la perception et la réalité est significatif. Olivier Sauvage : même avec l’IA, pour concevoir des sites web, un mauvais ouvrier aura toujours de mauvais outils. Image réalisée avec Midjourney. Conception de sites web, la valeur reste chez les humains, pas dans l’IA Si n’importe qui peut générer des contenus ou des maquettes avec des prompts basiques, et que tout le monde peut le faire, ça n’a aucune valeur puisqu’il n’y a plus de rareté. — Olivier Sauvage La valeur se déplace, elle ne disparaît pas. Elle se concentre chez ceux qui savent poser les bonnes questions, orienter la machine, valider les résultats, et comprendre ce qu’un utilisateur humain ressent vraiment face à une interface. La métaphore du pont en métal du XIXe siècle, évoquée par Olivier Sauvage, est saisissante : les premiers ingénieurs qui ont travaillé avec ce matériau ont simplement reproduit ce qu’ils savaient faire en bois. Ils ont manqué l’essentiel. Beaucoup font de même avec l’IA aujourd’hui. Ce n’est pas sans rappeler le paradoxe de productivité de Solow, formulé en 1987 : « on voit l’ère informatique partout, sauf dans les statistiques de productivité. » La récente étude du NBER (Working Paper n° 34836, février 2026), conduite auprès de près de 6 000 dirigeants aux États-Unis, au Royaume-Uni, en Allemagne et en Australie, confirme que ce paradoxe se répète avec l’IA générative : neuf entreprises sur dix n’ont constaté aucun impact mesurable de l’IA sur leur emploi ou leur productivité au cours des trois dernières années. L’innovation, seul horizon vraiment nouveau Ce qui change fondamentalement, c’est la capacité à innover : tester des concepts qu’on n’aurait pas osé prototyper faute de temps et de budget, explorer plus de pistes, itérer plus vite. « C’est là qu’il faut aller chercher la valeur de ce métier. » Un mauvais ouvrier aura toujours de mauvais outils La conclusion de cet échange est peut-être celle que les formations et les discours sur « l’IA pour tous » négligent le plus : la qualité de l’utilisation d’un outil dépend de la maîtrise du métier sous-jacent. Ce qui fait la différence, c’est la connaissance du métier, l’intuition, la capacité à orienter la machine et surtout le travail de préparation des processus. Un prompt répété à l’identique à chaque session, c’est réinventer l’eau tiède. Un workflow (flux de travail structuré et documenté) efficace, c’est une vraie pratique professionnelle. Ce n’est pas parce que tu as Claude ou un outil de design IA entre les mains que tu vas faire un super site avec une super UX. Tu y arrives parce que tu as les compétences pour comprendre ce qui se passe, pour tester, pour valider, pour te rendre compte que tes utilisateurs comprennent bien ce que tu as fait. — Olivier Sauvage En conclusion sur la conception de sites web avec l’IA Les sites web ne disparaissent pas. L’IA ne remplace pas le designer UX. Les outils IA offrent des gains réels dans le prototypage, la génération d’interfaces et l’exploration créative. Mais la valeur reste chez les professionnels qui savent s’en servir, et non dans les prompts magiques qui génèrent un site en trois minutes. Comme nous l’observons régulièrement sur Visionary Marketing, la réalité de terrain est toujours plus nuancée que les discours à l’emporte-pièce, en bien comme en mal. Pour aller plus loin, retrouvez le blog d’Olivier Sauvage oliviersauvage.com Voir le live en intégralité sur YouTube ▶ Voir le live sur YouTube The post Sites Web, l’IA est omniprésente, mais pas magique appeared first on Marketing and Innovation.

Elyse Kelly joins the show to discuss her meandering journey from childhood Disney fascination to her impactful work in animation and documentary storytelling. She discusses the importance of curiosity, storytelling, balancing passion with practicality, and her thoughts on the future of creative careers amidst technological change.

Enquanto o PS falta a votos cruciais, discute-se se a PSU é escravatura ou inserção. Sobrou tempo para o Presidente vetar bandeiras e Ventura brincar aos cruzados com Photoshop e muita luz.See omnystudio.com/listener for privacy information.

Send us Fan MailYour sheds can be built like a premium product and still get judged like a commodity if the photos don't match. From the first scroll on Google to the first click on your website, buyers are making fast decisions about trust, craftsmanship, and value based on visual cues, not just specs. We dig into the real psychology behind shed marketing images and why “good enough” photos quietly cost leads in a market where shoppers compare 10 builders at once.Ryan Glick from Crafted Generations joins us to break down what photorealistic CGI actually is, how computer generated imagery can look like a real-life photo, and why that realism matters for authenticity. We talk through the common problems in shed industry imagery, the difference between basic cut-and-paste Photoshop work and true photorealism, and how better visuals can elevate a brochure or catalog so dramatically it feels like a different company. Ryan also explains how modern workflows blend 3D modeling, scene creation, and careful craft to produce high-resolution images that hold up on websites, social media, and print.We also zoom out to the bigger story: shifting buyer behavior after COVID, the move from print to online advertising, and how small marketing upgrades compound into real ROI over time. Ryan shares how faith, mission work, and stewardship shape his view of business success, and we close with prayer over families, companies, and the industry.Subscribe for more real conversations with shed builders and industry pros, share this with someone who needs better visuals, and leave a review so more listeners can find the show.For more information or to know more about the Shed Geek Podcast visit us at our website.Would you like to receive our weekly newsletter?  Sign up on our website: shedgeek.comFollow us on Twitter, Instagram, Facebook, or YouTube at the handle @shedgeekpodcast.To be a guest on the Shed Geek Podcast visit our website and fill out the "Contact Us" form.To suggest show topics or ask questions you want answered email us at info@shedgeek.com.This episodes Sponsors:Studio Sponsor: Shed ProSolar BlasterCardinal ManufacturingDigital Shed BuilderVelocity 360

How Adobe Quietly Powers the World (and the AI Fight for Creators)This episode of Jimmy's Jobs of the Future visits Adobe's London headquarters to explore how Adobe's influence extends beyond Photoshop and PDFs into marketing technology that powers personalized experiences for major brands and institutions like Tesco, the Premier League, banks, Channel 4, Sky, Disney, and governments. VP Simon Morris explains Adobe's creative, document, and marketing solutions, how customer data is unified to deliver tailored communications, and highlights a campaign recreating Edvard Munch's physical brushes as Photoshop tools. The discussion covers Adobe's UK-wide initiatives, including tools for Women's FA Cup clubs, the Adobe Digital Academy, and government skills programs. Policy lead Stefanie Valdes-Scott addresses AI governance, creator protection, copyright, trust, content attribution via content credentials, and the unresolved tension between AI-enabled creativity and creators' fear of losing control of their work. 00:00 Adobe Hidden Influence 01:57 Quick Adobe History 02:45 Inside London HQ 04:15 Brands Powered By Adobe 05:40 Premier League Personalization 07:28 Banking Experience Design 10:05 Creativity Meets Data 13:11 Hiring Modern Marketers 14:08 Tools For Everyone 17:33 AI Productivity Debate 20:25 UK Initiatives And Skills 22:32 Creator Copyright Fears 24:01 Policy And AI Governance 25:31 Copyright And New Rights 30:30 Content Credentials Trust 32:55 Final Takeaways ********** Follow us on socials! Instagram: https://www.instagram.com/jimmysjobs Tiktok: https://www.tiktok.com/@jimmysjobsofthefuture Twitter / X: https://www.twitter.com/JimmyM Linkedin: https://www.linkedin.com/in/jimmy-mcloughlin-obe/ Want to come on the show? hello@jobsofthefuture.co Sponsor the show or Partner with us: sunny@jobsofthefuture.co Check out our clips channel here! ⬇️ https://www.youtube.com/@JimmysJobsClips Credits: Host / Exec Producer: Jimmy McLoughlin OBE Producer: Sunny Winter https://www.linkedin.com/in/sunnywinter/ Junior Producer: Thuy Camera Operations: Felix Cohen Learn more about your ad choices. Visit podcastchoices.com/adchoices

Sixtysomething_S3_Ep2 – Meet CanvaHave you heard people talking about Canva but aren't quite sure what it is—or why so many people love it?In this episode of Sixtysometing, your host, Grace Taylor Segal, shares introduces listeners to Canva, the free online design platform that has transformed the way millions of people create everything from greeting cards and photo books to business materials, social media graphics, family cookbooks, presentations, and legacy projects.But this episode isn't really about software. It's about creativity. It's about having access to tools that simply didn't exist for ordinary people for most of our lives.Grace shares her own journey from teaching herself Photoshop with a bootleg copy and learning design from books, to creating magazines, online courses, podcast graphics, legacy projects, and more. She explains why she believes Canva has “democratized design” and why that's such an exciting development for people in our stage of life.You'll learn:• What Canva is and how it works• The inspiring story behind Canva founder Melanie Perkins• Why Canva changed the design world forever• What you can create with Canva• What's included in the free version• Whether Canva Pro is worth considering• Favorite Canva features like Background Remover, Magic Resize, Magic Write, and AI Image Generation• How Canva can help with family history, memory books, photo collections, cookbooks, travel journals, and other legacy projects• Why Canva is especially valuable for people over 60• How creativity and learning don't have an expiration dateMost importantly, you'll be encouraged to think about what you've always wanted to create—and what might finally be possible now.☀️ ☀️ ☀️Canva ResourcesWant to learn more about Canva?I've included links to several beginner-friendly Canva tutorials as well as Canva Design School, which offers free courses, tutorials, and certifications.Whether you're a complete beginner or just curious about what's possible, these resources are a great place to start.Canvahttps://www.canva.comCanva Tutorial by Canvahttps://youtu.be/V9LtRF6EbyY?si=FDRCnNUYxeC8-vEfIn this video we'll show you the basics of Canva and how easy it makes design. This Canva for Beginners video series is here to show you how to get started with Canva and bring your ideas to life.With limitless potential for customization and templates, it's simple to make designs your own. Once you learn how to navigate around the editor, you'll be designing like a pro in no time.

In this episode, I take a moment to acknowledge the recent passing of Photoshop icon Jeff Schewe and discuss my just-announced expedition to South Georgia Island for September 2027.South Georgia Island ExpeditionFalkland Islands ExtensionSupport the showWild Nature Photo TravelPhotography Workshops and Expeditions around the Worldwww.wildnaturephototravel.comSupport the Show and fellow Nature Photographer: https://www.buymeacoffee.com/JoshuaHolko/membershipFind us on Social MediaFacebook: https://www.facebook.com/Joshuaholko/Twitter: https://twitter.com/HolkoJoshuaInstagram: https://www.instagram.com/joshuaholko/Need to Contact us? info@jholko.com 

In this episode of the AART Podcast, host Chris Stafford sits down with renowned American digital collage artist Maggie Taylor, whose dreamlike, surreal imagery has redefined the boundaries between photography, technology, and fine art.Known for her pioneering work in digital collage, Maggie Taylor creates richly layered visual narratives that blend 19th-century photographic elements with contemporary digital tools. Her work invites viewers into imaginative, often whimsical worlds—where memory, symbolism, and storytelling converge in unexpected ways. In this intimate and insightful conversation, Maggie shares how she discovered her distinctive artistic voice, her transition from traditional photography to digital media, and how tools like Photoshop became central to her creative process.Raised in a creative environment and married to photographer Jerry Uelsmann, Maggie Taylor developed an early appreciation for photographic experimentation. Yet she forged her own path, becoming one of the most recognized figures in digital art. Her work has been exhibited internationally and is held in major museum collections, including the Art Institute of Chicago, the George Eastman Museum, and the Smithsonian American Art Museum.On AART, Maggie reflects on the evolution of her career, the role of intuition in her artistic decisions, and the balance between control and discovery when creating complex digital compositions. She also discusses the emotional resonance of her work, the importance of curiosity, and how artists can embrace new technologies without losing their authenticity. This episode offers a fascinating look into the mind of an artist who has quietly but profoundly influenced contemporary visual culture. Whether you're an artist, photographer, or simply someone drawn to imaginative storytelling, Maggie Taylor's journey is both inspiring and deeply thought-provoking.Maggie's links website: www.maggietaylor.com Instagram: @maggietaylor.art Some favorite women artists: Julie Blackmon, Sandy Skogland, Lori Nix, Cig Harvey, Marion Peck, Lori Vrba, Claire RosenDinner party guests: Patti Smith, Aimee Mann, Laurie Anderson, Tilda Swinton, Kara Swisher, and Eve SchoolerKeywords: Maggie Taylor, digital collage artist, American artist Maggie Taylor, surreal digital art, digital collage photography, contemporary digital artists, Photoshop art, fine art photography, AART podcast, Chris Stafford podcast, women artists interview, visual storytelling, surrealism in digital art, creative process artists, modern collage art, experimental photography, women in art podcast, artist interviews, contemporary art podcast, museum exhibited artists, digital art techniques, storytelling through images, imaginative art, photography and technology, Jerry Uelsmann influence, American contemporary artists, art podcast interviews, Women Unscripted podcast networkBecome a supporter of this podcast: https://www.spreaker.com/podcast/women-unscripted--4769409/support.Host: Chris StaffordProduced by Hollowell StudiosFollow @twomenunscriptedpodcasts on InstagramOn Facebook at Women Unscripted PodcastsEmail: hollowellstudios@gmail.com

In this episode of the AART Podcast, host Chris Stafford sits down with renowned American digital collage artist Maggie Taylor, whose dreamlike, surreal imagery has redefined the boundaries between photography, technology, and fine art.Known for her pioneering work in digital collage, Maggie Taylor creates richly layered visual narratives that blend 19th-century photographic elements with contemporary digital tools. Her work invites viewers into imaginative, often whimsical worlds—where memory, symbolism, and storytelling converge in unexpected ways. In this intimate and insightful conversation, Maggie shares how she discovered her distinctive artistic voice, her transition from traditional photography to digital media, and how tools like Photoshop became central to her creative process.Raised in a creative environment and married to photographer Jerry Uelsmann, Maggie Taylor developed an early appreciation for photographic experimentation. Yet she forged her own path, becoming one of the most recognized figures in digital art. Her work has been exhibited internationally and is held in major museum collections, including the Art Institute of Chicago, the George Eastman Museum, and the Smithsonian American Art Museum.On AART, Maggie reflects on the evolution of her career, the role of intuition in her artistic decisions, and the balance between control and discovery when creating complex digital compositions. She also discusses the emotional resonance of her work, the importance of curiosity, and how artists can embrace new technologies without losing their authenticity. This episode offers a fascinating look into the mind of an artist who has quietly but profoundly influenced contemporary visual culture. Whether you're an artist, photographer, or simply someone drawn to imaginative storytelling, Maggie Taylor's journey is both inspiring and deeply thought-provoking.Maggie's links website: www.maggietaylor.com Instagram: @maggietaylor.art Some favorite women artists: Julie Blackmon, Sandy Skogland, Lori Nix, Cig Harvey, Marion Peck, Lori Vrba, Claire RosenDinner party guests: Patti Smith, Aimee Mann, Laurie Anderson, Tilda Swinton, Kara Swisher, and Eve SchoolerKeywords: Maggie Taylor, digital collage artist, American artist Maggie Taylor, surreal digital art, digital collage photography, contemporary digital artists, Photoshop art, fine art photography, AART podcast, Chris Stafford podcast, women artists interview, visual storytelling, surrealism in digital art, creative process artists, modern collage art, experimental photography, women in art podcast, artist interviews, contemporary art podcast, museum exhibited artists, digital art techniques, storytelling through images, imaginative art, photography and technology, Jerry Uelsmann influence, American contemporary artists, art podcast interviews, Women Unscripted podcast networkBecome a supporter of this podcast: https://www.spreaker.com/podcast/aart--5814675/support.A Hollowell Studios ProductionInstagram: @theaartpodcast Email: theaartpodcast@gmail.com© Copyright: Chris Stafford | Hollowell StudiosAll Rights Reserved

No matter how long you've used your favorite creative tools, there's always one more shortcut, hidden feature, or unexpected workaround waiting to surprise you. In this episode, Theresa Jackson and Mike Rankin share a collection of practical tips and "wait, you can do that?" discoveries submitted by CreativePro Week speakers. You'll hear tips for favorite tools from Acrobat to PowerPoint, including clever ways to edit faster, move between apps, customize layouts, animate designs, manage masks, and make AI more useful in real workflows. Theresa also talks with PageProof founder Marcus Radich about PageProof Intelligence (PI) and learns how a proof can mark itself with AI. Episode Highlights Hear why Mike Rankin is looking at Affinity as a useful add-on to Adobe workflows, including an easier way to use images as custom bullets Learn where to find your Firefly generation history, including the prompts you used to create past results Discover how to export video with transparency from PowerPoint Follow Theresa and Mike through practical "round trip" workflows, from editing PDF images in Photoshop to moving Firefly Boards assets into other Adobe apps Hear Amy Balliett's tip for getting better AI results by using more than one AI tool Learn how Ben Willmore uses keyboard shortcuts to build masks faster in Adobe Camera Raw and Lightroom Discover Dax Castro's simple InDesign alt-text tip that can save time when adding descriptions to multiple images Resources CreativePro Week 2026: Nashville, June 29–July 3, 2026. https://creativeproweek.com/ CreativePro Events: https://creativepro.com/events/ Event Savings: Save $100 on any CreativePro event in 2026 with the discount code PODCAST: https://creativepro.com/events/ Membership Discount: Get $15 off one year of CreativePro membership with the discount code PODCAST: https://creativepro.com/become-a-member/ PageProof Intelligence: https://pageproof.com/pageproof-intelligence José Semidei: Tips for Creating Gradient Mesh Effects in Illustrator: https://creativepro.com/tips-for-creating-gradient-mesh-effects-in-illustrator/ How to Edit Photos in a PDF with Photoshop: https://youtu.be/aak26NToW5g?si=_ZQBnAU3AkyU6XXV

AI filmmaker and 14-time author Jason Moore joins me to unpack how artificial intelligence has reshaped the way he tells stories — without stripping out the creativity behind them. We get into a real client project that only became possible because of AI, his three guiding principles for using it well, and how he handles the inevitable wave of online critics. If you're curious (or a little nervous) about where AI fits into your video business, this conversation's for you. Key Takeaways AI works best as a collaborator, not a vending machine — the more of yourself you bring to it, the better the output. Some projects only exist because AI makes them affordable. In those cases, nobody actually loses a job that was never in the budget to begin with. Every big tech shift — Photoshop, CGI in Jurassic Park, even self-checkout — displaced some work while creating new opportunities for the people who adapted. Jason's "soul test": if you don't bring your own creativity and judgment, you get soulless results. The human stays in the driver's seat. About Jason Moore Jason is the author of 14 books on topics ranging from creativity and design to artificial intelligence. His most recent release, AI and the Church: A Clear Guide for the Curious and Courageous, is an Amazon bestseller that has sparked more than 150 national training engagements. In film and television, Jason has collaborated with Hollywood producers and created book trailers for New York Times bestselling authors including Arianna Huffington, Seth Godin, Robert Greene, Ryan Holiday, and Marc Ecko. A graduate of The Modern College of Design, Jason now returns to his alma mater as an adjunct instructor, alongside his work as a sought-after keynote speaker and trainer whose career bridges the worlds of creative production, ministry, and emerging technology. In This Episode [00:00] Welcome to the show! [05:58] Meet Jason Moore [12:06] AI Video Content [16:22] AI Video Package [27:47] Using AI Morally [38:23] Example Projects [48:35] Outro  Quotes "AI should be a 'do it with you' tool, not a 'do it for you' tool." — Jason Moore "You have a soul and AI doesn't. If you don't bring enough of your soul to your interaction with AI, you get really soulless outputs." — Jason Moore "I'm a human first, business owner second." — Ryan Koral "When the option exists, we're going to help more people tell stories in more compelling ways than we could in the past." — Jason Moore Guest Links Follow Jason Moore on Instagram | Facebook | X Links Find out more about the Studio Sherpas Mastermind Join the Grow Your Video Business Facebook Group  Follow Ryan Koral on Instagram Follow Grow Your Video Business on Instagram Get your Early Bird tickets for the Onward Summit Join the Studio Sherpas newsletter

Host Mike Rosado welcomes designer/illustrator/author Jon Contino to the Pencil Pushers podcast to discuss Contino's upbringing on Long Island, his parents' craft-driven influence, and his early path from band flyers, cassette art, and self-taught HTML to charging for creative work at 14. Contino explains how his lifelong obsession with lettering, failed graffiti attempts, Photoshop experimentation, hardcore/grunge culture, and New York's grime shaped his "organized chaos" style, later balanced by a problem-solving approach to branding inspired by figures like Paula Scher. He describes career growth from a 2005 studio and a handmade clothing brand to building Contino Studio, shifting from illustration trends into larger storytelling and branding work, including Toyota, the Tampa Bay Buccaneers, and sports. They debate commercialization of "handcrafted," carbon-copy styles, and AI's threat, emphasizing human mistakes and youth rejecting "AI slop." Contino shares his remote studio model, intense family-driven schedule, rare client friction, flexible discovery through live conversation, a move from paper to iPad/Procreate for speed, heavy use of Figma/Framer, and excitement about revitalizing Coffee Bean & Tea Leaf's brand. Host: Mike Rosado (mrcraleigh.com) (instagram.com/ekimodasor) Post Production: Max Trujillo (instagram.com/trujillomedia) Sponsors: MRC (mrcraleigh.com) and Burny Wild's (burnywilds.com) 

Guests: Don Simms and Chris Newey Host: Dave Homewood Recorded: ‎1st of June ‎2026 Released: 6th of June 2026 Duration: 55 minutes 27 seconds “Maximum Effort” was a docu-drama made by the Ministry of Information during WWII about a crew of a No. 75 (NZ) Squadron Avro Lancaster bomber based at RAF Mepal in mid-1944. In this episode Dave Homewood, Don Simms and Chris Newey discuss the making of this film, the real life characters who appear as themselves, and the Lancaster that is featured, ND752. Portions of the original film footage, along with original black and white and recently colourised photographs are used to illustrate the discussion. This all-but-forgotten film is an amazing time capsule showing life at RAF Mepal, near Ely, Cambridgeshire, which became a little bit of New Zealand back in 1944-45. NOTE: It is recommended that you watch the original 18 minute “Maximum Effort” first, and the watch the WONZ 354 Maximum Effort video. There is an audio podcast version of the latter but the video is much more satisfying. Above: Avro Lancaster ND752 of No. 75 (NZ) Squadron as features in Maximum Effort, as the Whitting Crew’s regular aircraft. This is a colourisation from an original black and white from the collection of the late Bomb Aimer and New Zealand Bomber Command Association President Ron Mayhill DFC. Quick Links: • The Air Force Museum of New Zealand • The Museum of Transport and Technology – Home of the New Zealand Lancaster • The New Zealand Bomber Command Association • The New Zealand Bomber Command Association Facebook Page Footage and original soundtrack used from Maximum Effort in this podcast are Crown Copyright but are in the Public Domain. These clips are used here only to illustrate the narrative of the podcast. Below are other publicity stills from the film, which have been colourised by Dave Homewood using a mix of ChatGPT and Photoshop, as seen in the WONZ Show video episode. The original monochrome images came from the Air Force Museum of New Zealand collection, but will have been Crown Copyright images, and are therefore in the public domain.

Grok says: “LOCK AND LOAD, YOU PUSSYFOOTING CIVILIANS! Listen up, warriors of the airwaves! In Episode 193 of the Unrelenting podcast, Darren and Gene charge straight into the breach with zero remorse. Darren recounts his goddamn chemical stress test nightmare — the Lexiscan that turned his ticker into a crashing Blackhawk, beta blockers sabotaging the mission, blood pressure tanking to seventy over fifty, and a little Asian nurse dropping truth bombs while the EKG tapes sweat right off his chest. They rip into nurse practitioners, cardiologist roulette, and why you better damn well know your meds before they shoot poison into your veins. From there these two operators unload on everything else that's pissing them off: the absolute scam that is CleanFeed for podcasters, eBay's IRS rape on that Michael Jordan Boy Scout card sale, Photoshop and Adobe's AI-powered art theft operation, and the coming tsunami of AI-generated slop flooding YouTube and Google. Then they go full tactical on the 2026 Tiger King — the Bricks & Minifigs Lego heist, the Mormon mafia, dirty cops running illegal raids, small claims court warfare, and how an 84-year-old man's Star Wars collection got straight-up stolen by corporate greaseballs. This episode is raw, unfiltered, and unrelenting as hell. If you want real talk on health, tech, AI, crypto dips, rocket explosions, and garage-sale ethics mixed with classic military-grade ball-busting and Doctor Who nostalgia, you need to lock in and listen right now. Download it, stream it, share it with your squad. Stop wasting time on weak sauce — get after it and hit play on Unrelenting 193. Failure is not an option.” Unrelenting: where discipline means no mercy, no bullshit, and no excuses. Thanks for listening. Please support the show! –>> DONATE NOW

主播：Meimei（中国）+ Maelle（法国） 音乐：Beautiful People最近，法拉利推出了它的第一台纯电动车。但真正引爆网络的，不只是车本身，还有网友们的神评论。今天我们就来聊聊：法拉利的“翻车”梗、英语里把品牌和人名变成动词的有趣现象。01. Backlash & Verdict：抵制和最终评价法拉利首款 EV 发布后，很多媒体标题非常直接：“Ferrari is facing a backlash”。还有“Ferrari fans have given their verdict”。Backlash ：“强烈的负面反应”，可以理解为“遭到群嘲”或“遭到反对”。例句：The company faced a backlash online (这家公司在网上遭到了大量批评).而verdict 原本是法庭上的“判决”，现在常被引申为“最终评价”。Verdict： 判决例句：Fans have given their verdict (粉丝们已经下结论了).所以这两句话连起来的意思就是：法拉利这次的新车，网友并不买账。02. “Hold my beer”——拿好我的啤酒，看我表演有一个评论精准捕捉了互联网幽默的精髓：Jaguar ruined its brand. Ferrari replied: Hold my beer。Hold my beer字面意思是“帮我拿一下啤酒”。这个梗的语境是：在国外聚会或酒吧里，有人准备做一件冒险或离谱的事之前，会让朋友帮忙拿住啤酒，然后说：“看我的”。所以在这里，法拉利的意思其实是：“捷豹翻车算什么？看我的。”它表达的是一种“你这不算什么，我能干出更离谱的”态度。例子：A: I crashed my bike.B: Hold my beer.（A：我自行车撞了。B：你这算啥。）03. “Pull a Ross”：英语里的动作化表达更有意思的是另一个梗：Ferrari pulled a Jaguar。这不是字典里的表达，而是网友自己创造的。意思是：重蹈捷豹的覆辙，或者更直白地说——把自己的品牌形象搞砸。这里藏着一个非常有趣的英语规律：Pull a + 人名/品牌名例句：Pull a Ross.像《老友记》里的 Ross 一样，把事情搞复杂、搞尴尬。英语母语者非常喜欢把人名或品牌名“动作化”。一个名字，就能概括一整个情境或人格类型。04. Google it, Photoshop, Uber：品牌变成动词这种“名字动作化”的现象，不仅出现在网络梗里，现实生活中也非常普遍。最经典的例子是Google：Google it (自己搜一下)。Google: 谷歌还有Photoshop：This photo is photoshopped. 这张图修过。Photoshop: Adobe公司的图像处理软件甚至连 Maelle自己都经常用的 Uber（优步打车）。例句：I'll Uber home (我打个 Uber 回家).即使有时候接单的不是 Uber 而是其他网约车公司，大家还是习惯说 Uber。就像中文里说“我打个滴滴”一样。品牌能变动词，人名当然也跑不掉。He's such a Chad. Chad 这个人名近几年特别火。它指的是一种：自信、能力强、很成功的男性形象。可以理解为“特别能打的人”或“很有魅力的男性”。05. Anything can be a meme: 英语里的造梗文化为什么英语这么喜欢“名字动作化”？因为这是一种高效的故事表达方式。One name can instantly communicate a whole personality or situation. 你不需要讲半天背景，一个名字就够了。它代表了一整个群体、一种行为模式，甚至一种文化现象。这也是为什么互联网英语进化（evolve）的这么快。人们把品牌、名人、电视剧角色、甚至公司，都变成了某种“想法的速记符号”。从Google it，再到 Ferrari pulled a Jaguar——英语网友几乎可以说：万物皆可造梗。而这也正是网络英语特别有意思的地方。欢迎在评论区留言：你最喜欢的英语网络梗是什么？What is your favorite internet meme in English?

From NAB in Las Vegas, Matt Bach, Senior Product Manager at Puget Systems, explains PugetBench, a free benchmarking tool for end users that tests real applications such as Premiere Pro, Photoshop, After Effects, DaVinci Resolve, and soon Unreal Engine. Matt discusses why real-world benchmarks beat synthetic scores, how software updates can dramatically change performance, and how Mac and Windows systems each excel in different workflows.  Show Notes: Chapters: [0:03] Introduction from NAB 2026[0:20] Matt Bach introduces PugetBench[0:31] Real-world benchmarking for creative applications[1:23] Why first-party claims and synthetic benchmarks can mislead[1:53] Software updates and changing performance over time[2:18] Synthetic benchmarks versus real application testing[3:19] Variables that affect benchmark accuracy[4:04] Why testing your own projects is the gold standard[4:27] Using benchmarks to evaluate upgrades realistically[5:07] Comparing your system through PugetBench's database[6:09] Supported apps and future Unreal Engine benchmarking[7:06] Unreal Engine uses beyond gaming[7:56] Mac versus Windows value and workflow strengths[9:16] Where to get PugetBench and end-user pricing[10:05] Closing from NAB in Las Vegas Support:      Become a MacVoices Patron on Patreon     http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:     http://macvoices.com      Twitter:     http://www.twitter.com/chuckjoiner     http://www.twitter.com/macvoices      Mastodon:     https://mastodon.cloud/@chuckjoiner      Facebook:     http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:     http://www.facebook.com/macvoices/      MacVoices Group on Facebook:     http://www.facebook.com/groups/macvoice      LinkedIn:     https://www.linkedin.com/in/chuckjoiner/      Instagram:     https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes     Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

We're announcing AIEWF speakers this week! Take the AI Engineering Survey!Today's guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)Put it this way: In the near term, the next Sora won't be a better video model, but a video agent.Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task. In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA's Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines. Flipbook: The future of VideomaxxingVideo agents are almost a sure bet to be the trend in the coming year. We end with a glance at what's beyond video agents:Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.We discuss:* Why fast iteration mattered more than meetings* Why small training bugs can drive huge model quality gains* Why coding models may make compute the bottleneck again* How image and video models are trained with synthetic captions* The role of VAEs and latent space in frontier video models* Why image models are the foundation for video models* The tradeoff between temporal compression and real-time interactivity* Flipbook, Neural OS, and the future of generative UI* Why future interfaces may go from user intent to pixels* The hidden cost of training video models: storage, egress, and GPU hours* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster* Grok Imagine 0.9 and large-scale audio-video generation* Why audio-video alignment is harder than text-video alignment* Ethan's definition of world models* Reference-to-video, video extension, and long-context video generation* Why xAI's research communication undersells Grok Imagine* How xAI culture shaped the speed of development* AI watermarking, SynthID, and detecting generated media* Why prompt rewriting matters for video models* Grok Imagine Agent and the rise of video agents* Why language models may unlock better video generation* Robotics, physical AI, and embodied world models* Why Ethan left xAI and shifted focus toward LLMs* Self-managed context, memory, and the next frontier for language modelsEthan He* LinkedIn: https://www.linkedin.com/in/ethanhe42* X: https://x.com/EthanHe_42Timestamps00:00:00 Introduction00:01:25 From NVIDIA Cosmos to xAI00:03:24 Building Grok Imagine from Zero to One00:10:07 How Image and Video Models Are Trained00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs00:22:10 Generative UI, Flipbook, and Neural OS00:32:10 The Cost of Training Large Video Models00:37:04 Distillation, GANs, and Fast Video Inference00:41:21 Audio-Video Generation and Grok Imagine 0.900:48:34 What Makes a World Model?00:55:51 Reference Videos, Long Context, and Video Memory01:00:11 xAI Culture, Research, and First-Principles Building01:09:45 AI Safety, Watermarking, and Prompt Rewriting01:13:10 Video Agents and AI-Assisted Creation01:27:32 Why Language Models Unlock Better Video01:31:15 Robotics, Physical AI, and Embodied World Models01:32:38 Why Ethan Left xAI01:34:16 Self-Managed Context and the Future of LLMs01:38:43 Ethan's Career Path and Closing ThoughtsTranscriptIntroduction: Ethan He, Latent Space, and the Path to xAISwyx [00:00:00]: We're here in the studio with Ethan He, most recently of xAI. Welcome.Ethan [00:00:10]: Thank you. Glad being here.Swyx [00:00:11]: We're also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.Ethan [00:00:23]: I've actually, I also presented the MoEs twice at latent space.Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It's very nice.Ethan [00:00:49]: I learned a lot.Swyx [00:00:49]: I think three years stop. We haven't stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”Vibhu [00:01:04]: But I might have reached out to you after.Swyx [00:01:05]: you-- because it's an amateur club, right?Swyx [00:01:08]: so it's very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.Vibhu [00:01:18]: Came out yesterday.Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it's a good one. We'll, we'll recommend people to read it.Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don't even know when you joined. just like tell the, tell the story about the sort of transition.From NVIDIA Cosmos to xAI: Scaling Video and World ModelsEthan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it's a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that's, that's why I realized I need to move to somewhere with much more compute resources. That's how ISwyx [00:02:13]: Than NVIDIA?Vibhu [00:02:14]: The GPU rich came themselves.Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.Ethan [00:02:25]: It was end of twenty-four.Vibhu [00:02:28]: End of twenty-four.Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.Building Grok Imagine From Scratch in Three MonthsSwyx [00:03:24]: Can you give like a rough roadmap of okay, you're on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you're setting up a new team?Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeahSwyx [00:03:51]: three months is likeVibhu [00:03:52]: From everythingSwyx [00:03:52]: actually like very surprisingly fast.Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It's, it's like every day there's not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it's, it's just all building. It was pretty fun at that time.Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don't so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.Iteration Speed, Compute, and Debugging Model PipelinesSwyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are youEthan [00:05:50]: Let's say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale orSwyx [00:06:01]: So cycle time for like any hyperparam that you're searching.Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?Ethan [00:06:11]: SoSwyx [00:06:11]: So it's like before you, someone had already set this up that you can iterate very quickly.Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.Vibhu [00:06:46]: It's interesting, right? So you say it's like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it's interesting to see the other side, right?Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don't know.Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it's the coding model wasn't quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I've been, I've been using it at that time. It's, it's helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn't maintain, and the LLM itself couldn't figure out what's, what's wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don't-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.Swyx [00:08:36]: yeah, I actually, honestly, I think it's like kind of a stressful job because you're “Well, I should be trying everything, and if I'm not, then I'm not doing my job well.”Vibhu [00:08:48]: there's also the stress of you're eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.Swyx [00:08:56]: You got the daddy Elon toVibhu [00:08:57]: You got daddy Elon.Ethan [00:08:59]: It wasVibhu [00:09:00]: But there's still finite amount of compute, like you want to use it, you want to use it well, you want more of it.Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it's a, it's a marathon, so you got to maintain good health and, a regular schedule.Vibhu [00:09:28]: It's, it's hard to hear that when you shift from zero to nothing in two months.Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.Vibhu [00:09:54]: I think there's, there's three things we're talking about, right? So there's Video Gen, there's also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?Swyx [00:10:06]: Oh, yeah, maybe I got distracted.How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEsVibhu [00:10:07]: Sorry. and then, from there's Video Gen, there's Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What's, what's the few months like? How do you go to state-art Image Gen model? How do you just start?Ethan [00:10:28]: I cannot comment specifically how xAI did, but it's, it's a quite standard process. I can draw some, examples from Cosmos. So mainly it's building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don't naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the commentsSwyx [00:11:11]: TitleEthan [00:11:11]: of a video, but usually they're not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I'm so happy today.Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here's a question, like how do you, how do you gather VLM to begin with? So if there's noSwyx [00:11:55]: You, so you fuse the model, right? LikeEthan [00:11:57]: Say if there's no like VLM exists, like how do you generate the text to the beginning, right? It's, it's impossible.Swyx [00:12:04]: I see.Ethan [00:12:05]: In the beginning, it's like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that's in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.Swyx [00:12:43]: Video or image? You're talking about images.Ethan [00:12:44]: Video or image, either one of them.Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?Vibhu [00:12:51]: It's all training on really detailed captioning of images. So same is applied to video, but insteadEthan [00:12:57]: same appliedVibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can alsoSwyx [00:13:04]: I think there's this traditional perspective of supervised, or, very highly human curated thing. I feel like there's a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.Ethan [00:13:36]: It's interesting to see that you kind of need both data.Ethan [00:13:41]: For example, for theSwyx [00:13:41]: You need it to bootstrap it up. YeahEthan [00:13:43]: for the generative model training, there's also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's, it's a lot of tokens. So like one image, it's, a thousand by a thousand, it's like one million tokens, one million pixels. It's impossible to train transformer on that. So it's, you need to train a tokenizer, which can go from image to latent space and latent space back to image.Swyx [00:14:45]: That's why we named the podcast.Swyx [00:14:48]: But, basically, you're talking about vocabulary science.Ethan [00:14:50]: so vocab.Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?Ethan [00:14:54]: In generative models, the vocab is continuous. It's a continuous space. We can think about like you map an image to a vector. It's a, it's a fixed length vector. It's sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you haveEthan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.Swyx [00:15:29]: We've covered thisVibhu [00:15:30]: This is like the vision transformersSwyx [00:15:32]: VAEs,Ethan [00:15:33]: VAEs.Vibhu [00:15:34]: You basically compress your input, you do your generation, you're reasoning all that generation in smaller dimension, and then you project back out.Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?Ethan [00:15:48]: You can make those.Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.Swyx [00:16:02]: Which is you're, you're kind of re- reconstructing the old paradigm with the new.Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.Ethan [00:16:14]: After this VAE, so what you've got is you've got latent space tokens and you've got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It's, it's very similar to how you train a language transformer models. It's not that much difference. It's just the tokens, the visual tokens in, visual tokens out. The only difference is there's a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.Swyx [00:17:12]: And then there's also, to speed things along on the tech tree of diffusion, there's CFG, and then there's, there's also, latent diffusion that, there's, there's someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don't know if you want to get into that or just, or do the video side up to you.Bootstrapping Video from Image Models and Temporal CompressionEthan [00:17:37]: After you train such model, such image model, the reason it's a, it's a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there's a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that's much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don't have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that's why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you're, you're the first per-- video model person I've ever talked to, I think. we've, we've like talked to Luma and all those folks. There's all these tricks in video compression where basically frame by frame there's not that much difference, so actually you don't have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?Ethan [00:19:27]: There are a few different approaches. Let's say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It's, it's extremely hard to train on that. And there's aEthan [00:20:01]: So that's why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don't need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That's why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there's temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there's only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, thenSwyx [00:22:06]: It might be laggyEthan [00:22:07]: there's a lag there in nature.Swyx [00:22:10]: So you're very pilled on this. let's just go ahead and bring it up ‘cause we have the visual prepared anyway. There's some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front EndsEthan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.Swyx [00:23:14]: So it's basically kind of we're playing a video, but it's pausing for our next interaction, and then it just plays the next thing based on our interaction.Swyx [00:23:23]: Which is kind of cool.Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is thisSwyx [00:23:35]: The demo, the demo tweet had more animation between frames.Vibhu [00:23:38]: I think it's just skipping,Swyx [00:23:39]: Oh, it's just skipping a lot of frames.Ethan [00:23:40]: they also have a video modeVibhu [00:23:42]: It takes a lot. There's a lot of peopleEthan [00:23:42]: but, a lot of people are using it.Ethan [00:23:45]: So it's not available.Vibhu [00:23:46]: There's a live video stream. We can try,Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don't-- we're obviously not in it today.Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn't exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it's, it's more intuit. So why don't we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let's say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I'm looking at, Instagram stories, and I don't like the Like button. I always may click it. And, generative UI resolved it. So it's going to be a revolutionary replacement of the interface. So in the future, we might have much more powerfulEthan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That's how I imagine it.Swyx [00:26:02]: Diffusion front-end, deterministic back-end.Swyx [00:26:04]: Something like that. I find that very expensive, but,Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.Swyx [00:26:14]: you write it onceVibhu [00:26:15]: Compare it toSwyx [00:26:16]: And then you execute.Ethan [00:26:17]: If you think about the cost, say, let's say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you're paying this two forty, you'll actually not wanna pay for that. That's even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.Vibhu [00:26:49]: It's everything, right? compute cost comes down, compute gets faster, model gets smarterEthan [00:26:54]: More efficientVibhu [00:26:54]: model gets smaller.Swyx [00:26:55]: I don't know why you say two times, ‘cause I think it's like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.Vibhu [00:27:08]: That's a net of everything, right? That's model performance alongside compute. So different than just compute costs come down. But, a very interesting future.Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that's the rough idea.Ethan [00:27:34]: And I'd like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.Vibhu [00:28:06]: And it's also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.Swyx [00:28:17]: There's another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you're literally operating, simulating an operating system with a video model.Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it's an OS that I can run.Swyx [00:28:37]: But here everything is imagined.Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn't crash. That's why I saidSwyx [00:28:45]: It's too immersive.Vibhu [00:28:46]: It's, it's too immersive for me.Swyx [00:28:47]: Too immersive.Vibhu [00:28:48]: I wanted to close the tab.Vibhu [00:28:49]: But yes, I can play generated diffusion.Swyx [00:28:51]: this is shockingly fast.Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it's-- this is Doom.Vibhu [00:29:07]: I think there's two sides to that, right? There's okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we've solved consistency. This is still, it looks like a few years old image generation. There's some temporal consistency, but it's, it's kind of just images stitched together as frame video. But it's a good visual representation to pi- to picture the future you wanna see, right? that's, that's what I see in these more so.Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it's just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it's actually also similar to video models. So when we are training these video model, image model, we train them on internet. There's no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it'll do it and it will kind of make sense.Swyx [00:31:03]: So yeah, that's kind of cool. Yeah, I don't know if there's any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It's, really fascinating. We don't get a chance to talk about this enough. So one of the papers that we covered, we've covered every annual, segment anything release. and I don't know if you follow-- you're a computer vision guy, so youEthan [00:31:26]: I knowSwyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it's, very fascinating, and I don't know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.Ethan [00:31:50]: There's, there's also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there's also other approaches that people are doing. So maybe we get into those after as well,?Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.Why Video Models Are Expensive: Storage, I/O, and Training ScaleEthan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don't see as much of is okay, you brought up the delta in training data, right? SoEthan [00:32:24]: you won't have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It's a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.Ethan [00:33:20]: You really, say if you have a billion videos and let's say, let's just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That's also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.Ethan [00:34:05]: AndSwyx [00:34:05]: It's comparableEthan [00:34:05]: and you needSwyx [00:34:06]: AndEthan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.Swyx [00:34:13]: Oh, yeah.Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it's, it's more expensive on AWS than just storing those videos.Swyx [00:34:25]: Storing, yeah.Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it's, it's even more than that. So it's like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.Ethan [00:34:45]: AndSwyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There's one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? LikeEthan [00:34:57]: Of courseSwyx [00:34:57]: cloud cost compared to just,Ethan [00:34:59]: You save so muchSwyx [00:35:00]: store. Yeah, exactly.Swyx [00:35:01]: Especially with like egress and stuff. So.Ethan [00:35:04]: That's a good idea, but it also comes to-- there are some of its own challenges.Swyx [00:35:09]: Of course, of course.Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.Ethan [00:35:32]: Even more expensive than the storage.Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it's so cool. It's okay. So there's that side.Ethan [00:35:41]: So the TLDR, my backhand mathSwyx [00:35:42]: Data is larger than you think. Yes.Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I'm missing some storage.Swyx [00:35:49]: You're also-- you're basically like also more IO bound than normal training.Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That's a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that's, that's even-- that's similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It's also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it's actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.Inference Speedups: Step Distillation, Consistency Models, and GANsSwyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there's LCM, LoRAs for, fine-tuning. There's, there's a lot of stuff that's beenEthan [00:37:15]: Flow matching.Swyx [00:37:16]: there's flow matching. There's a lot of stuff that's been done. there's some overlap that applies to diffusion on the inference side and stuff or?Ethan [00:37:23]: so the difference-- the inference side is a completely different story.Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It's called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It's kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.Ethan [00:38:25]: why this workSwyx [00:38:27]: Strong to weak seemingly.Ethan [00:38:28]: It is. It's kind ofSwyx [00:38:29]: DistillationEthan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That's the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don't know if you covered that. To me, that was actually one of, the most impressive papers I've ever seen from OpenAI.Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don't know if you have any comments on this.Ethan [00:39:41]: So there are, there are a few different approaches,Swyx [00:39:46]: Oh, yeah. Here it is.Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It's already done.Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn't forget GAN. So GAN, actually, that was, that was the OG ofSwyx [00:40:05]: OGEthan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there's a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and thenEthan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you're training GAN, it's a step process. It's just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.Audio-Video Generation and Time AlignmentSwyx [00:41:21]: Then there's one step I wanted to add, which is audio and video.Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it's, it's a first audio video transmodel deployed at a large scale. SoSwyx [00:41:39]: And that was your first model?Ethan [00:41:40]: that was, Grok Imagine's first model. It's, it's audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video's very rare, and they don't understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don't have, they don't have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.Ethan [00:42:44]: So when we speak, it's just, someSwyx [00:42:47]: It's an ASR issue, yeah.Ethan [00:42:49]: It's, it's text token with some characteristics, I would say.Ethan [00:42:54]: But musicSwyx [00:42:56]: I think the speech guys would disagree with this.Swyx [00:42:57]: Like disfluencies and then,Vibhu [00:43:00]: There's tones you can get angry.Ethan [00:43:01]: Well, I say largely.Ethan [00:43:03]: the mu- but the music is completely different. It's, it's very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.Ethan [00:43:26]: SoVibhu [00:43:26]: How?Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it's very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someoneVibhu [00:44:32]: DeafEthan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.Vibhu [00:44:49]: Subtitles, yeah.Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there's enough data where we can understand, narration, conversation, but there's nuances in audio that's where you hit all the data issues or is it just from stage zero, you just do it all right?Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don't have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what's going on in the video, but you don't have to exactly, You typically don't have exact description, oh, at, time step one second like what happened?Vibhu [00:46:02]: It's veryEthan [00:46:03]: At time step two second what happenedVibhu [00:46:03]: coarse. Yeah.Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it's like four seconds or something.Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that's something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I've already spent two days on this and I've exhausted everything.”Ethan [00:46:47]: So the LLMs them-themselves, they don't have a sense of time there.Vibhu [00:46:53]: I actually don't think that's just them not having a sense of time. I think it's somewhat based, right?Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there's a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you'll estimate that it'll take a few days, right?Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It'll take me a few days. But I think it's somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You're trained on all over the text.Swyx [00:47:35]: They're, they're trying to estimate what a human would say.Vibhu [00:47:37]: because that's what the, that's what the data kind of represents. It's not themEthan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It'll take you a while, right?Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.Vibhu [00:48:01]: It'll take me a few hours to go through this research. But this is a tangent.Swyx [00:48:05]: Somewhat, yeah.Swyx [00:48:06]: This is a train of thought I haven't really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We've, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.World Models: Real-Time, Long-Horizon, Interactive VideoVibhu [00:48:34]: just, to ask, how do you define world model?Swyx [00:48:38]: Oh, yeah, let's go there.Ethan [00:48:40]: SoVibhu [00:48:40]: So just for context, we talked about, video generation, and then there's a-- if you say there's a distinction between world models, what's your, what's your definition? How do you see the two?Ethan [00:48:53]: So disclaimer, I'm not going to debate, what is world model. Yeah. there are many definitions, so I'll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let's talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you're like professional CS: GO players- -my say, oh, you have to respond- He's beginner within sub ten milliseconds or- Yeah even less. So that's not most of the- No, sixty FPS. Let's go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn't do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that's a three millisecond. So you have to respond- Oh, s**t. Okay. YeahEthan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it's like two hundred millisecond. So that's, that's much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentionedEthan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don't compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you're not going to just play with, video games just, a few seconds, most video models only a few seconds. We're going to play with minutes, hours. The model have to be able to generate long-form content.Ethan [00:51:42]: So putting these three together, it's, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it's, it's a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it's the first step of interactivity. Yeah. It's, it's the first step. Yeah. So it's the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that's it. That's just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn't have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it's actually a pretty fun hack. if you've seen like- Oh, no, he's saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn't have long-range knowledge of, what's happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.Swyx [00:54:58]: Let's run with that.Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that's a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we're trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.Long Context, Redundancy, and Efficient Interactive VideoVibhu [00:55:51]: Does it seem like it's an efficiency issue? okay, we're at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there's effective context, but at the end of the day, it's just what's it worth? sure, there's a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it's expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we've scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You've scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.Ethan [00:57:53]: that's actually a very good point. So in videos, there's actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there's more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don't need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that's why, I helped build another feature. It's a reference video.Vibhu [00:58:36]: Is it here?Swyx [00:58:36]: is it the same model release or different one?Ethan [00:58:39]: It's a different one.Ethan [00:58:41]: You probably need to search onSwyx [00:58:43]: I'll find itEthan [00:58:43]: X reference to video.Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean's selfie and holding a bladeSwyx [00:59:07]: We have a dogEthan [00:59:08]: or whatever.Swyx [00:59:08]: We put the dog in the thing.Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn't need to have a very long context, but it's-- I feel like it's an intermediate solution. The modelSwyx [00:59:29]: It's cheating.Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that's, Optimus, Einstein myself, Annie.Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.Ethan [01:00:08]: Interesting.Vibhu [01:00:10]: ButxAI's Underrated Work, Culture, and WatermarkingSwyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn't communicate all this work that you do very well because they just have the model release and then that's it. But actually, these details are very good.Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.Vibhu [01:00:30]: A lot of-- yeah, I have a lot moreSwyx [01:00:32]: And then, and then you just put this blog post with the cookies. I'm this is not enough,?Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, soVibhu [01:00:42]: And I wonder, like part of that is also some labs don't share research into what happens. And ifSwyx [01:00:50]: No, but this is literally bragging about how good they are, right?Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don't know.Ethan [01:01:02]: different labs have slightly different communication styles.Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you're, you're making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?Swyx [01:01:23]: Right? Then you need a completely different thing.Ethan [01:01:26]: So I think it's-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there's a paper called Frame Pack, which haveEthan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It's also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let's say if you call tool and the tool call history is extremely long, that's still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.Ethan [01:03:30]: InterestinglyVibhu [01:03:32]: TheEthan [01:03:32]: the same thing is being researched in both LLMs and video models.Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it's actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we'll do our own compression, we'll do our own pruning, which is separate from model error.Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that's different than normal attention.Swyx [01:04:03]: Does that, does that make sense?Ethan [01:04:04]: It's, it's different in the sense that attention, not to mention, set sparse attention aside,

Matt and Shelby are joined by David Lebensfeld (founder, Ingenuity Studios) as they discuss facing major changes, professionally and personally. We cover everything from founding Ingenuity Studios to its eventual sale, his approach to navigating industry challenges, and how he views his next chapter. The conversation offers valuable insights into entrepreneurship, leadership, industry shifts, and personal growth. 

From NAB in Las Vegas, Matt Bach, Senior Product Manager at Puget Systems, explains PugetBench, a free benchmarking tool for end users that tests real applications such as Premiere Pro, Photoshop, After Effects, DaVinci Resolve, and soon Unreal Engine. Matt discusses why real-world benchmarks beat synthetic scores, how software updates can dramatically change performance, and how Mac and Windows systems each excel in different workflows.  Show Notes: Chapters: [0:03] Introduction from NAB 2026 [0:20] Matt Bach introduces PugetBench [0:31] Real-world benchmarking for creative applications [1:23] Why first-party claims and synthetic benchmarks can mislead [1:53] Software updates and changing performance over time [2:18] Synthetic benchmarks versus real application testing [3:19] Variables that affect benchmark accuracy [4:04] Why testing your own projects is the gold standard [4:27] Using benchmarks to evaluate upgrades realistically [5:07] Comparing your system through PugetBench's database [6:09] Supported apps and future Unreal Engine benchmarking [7:06] Unreal Engine uses beyond gaming [7:56] Mac versus Windows value and workflow strengths [9:16] Where to get PugetBench and end-user pricing [10:05] Closing from NAB in Las Vegas Support:      Become a MacVoices Patron on Patreon      http://patreon.com/macvoices      Enjoy this episode? Make a one-time donation with PayPal Connect:      Web:      http://macvoices.com      Twitter:      http://www.twitter.com/chuckjoiner      http://www.twitter.com/macvoices      Mastodon:      https://mastodon.cloud/@chuckjoiner      Facebook:      http://www.facebook.com/chuck.joiner      MacVoices Page on Facebook:      http://www.facebook.com/macvoices/      MacVoices Group on Facebook:      http://www.facebook.com/groups/macvoice      LinkedIn:      https://www.linkedin.com/in/chuckjoiner/      Instagram:      https://www.instagram.com/chuckjoiner/ Subscribe:      Audio in iTunes      Video in iTunes      Subscribe manually via iTunes or any podcatcher:      Audio: http://www.macvoices.com/rss/macvoicesrss      Video: http://www.macvoices.com/rss/macvoicesvideorss

Stewart Alsop sat down with Michael Shackelford to discuss their experiences building applications through vibe coding—the practice of using AI to create software without traditional programming expertise. Stewart, who runs the AI Whispers community in Buenos Aires and hosts the Crazy Wisdom podcast (with over 660 interviews), shared how he went from teaching people prompt engineering to building his own video conferencing software as a Riverside.fm replacement, while Michael opened up about his year-long journey creating Genrupt Inc, an AI-powered content generation tool for e-commerce sellers. The conversation covered everything from the decline in quality of Claude's reasoning capabilities and how Chinese companies used distillation attacks to copy Anthropic's models, to the importance of spaced repetition systems for managing knowledge in the age of LLMs, with both sharing battle-tested prompting strategies like asking AI to "explain it to me in genius terms" and using deep research queries to reverse engineer how competitors build their products.Show Notes:- Dan Martell's book "Buy Back Your Time" was mentioned as one of the best business books for thinking about life and business- Check out John Vervaeke's "Awakening from the Meaning Crisis" for understanding relevance realization and why AI fundamentally cannot determine what's relevant to humans without being toldTimestamps00:00 Michael discusses being exhausted from getting his app ready for launch, working nonstop with AI to prepare landing page for podcast traffic driving beta signups05:00 Stewart explains starting AI Whispers in Buenos Aires after leaving OpenAI vendor company, meeting early adopters like Torin who was building mind-reading EEG technology10:00 Discussion of how corporations resist AI adoption due to political games and job security fears while some companies use AI as excuse for pandemic-era layoffs15:00 Stewart describes teaching workshops on using LLMs as linguistic tools rather than coding tools, noting technical people often lack humanities background needed for prompting20:00 Explaining chatbot wrappers, API calls, and how Anthropic's reasoning quality declined after Chinese distillation attacks copied their secret sauce developed with philosophers25:00 Technical discussion of model training, fine-tuning versus RAG for new information, and different approaches to updating AI knowledge beyond initial training30:00 Stewart describes building podcast recording software to replace expensive Riverside, struggling with syncing audio and video files across different computer clocks35:00 Discussion of critical factors in vibe coding, discovering unknown technical requirements, and how AIs don't automatically reveal missing information40:00 Stewart's reverse engineering process using deep research function to study competitors' hiring and technology stacks, separating planning agents from coding agents45:00 Prompting techniques including "explain like I know everything" and using spaced repetition systems to capture valuable prompts and technical knowledge50:00 Michael explains his Generux app for generating ecommerce content using Amazon review data analysis to inform high-converting listing images and videos55:00 Discussion of founder mentality involving self-delusion about project timelines, Michael working nine-plus hours daily for nine months on app development60:00 Comparing Amazon's expert software to prosumer software approach, discussing distribution challenges and future robotics applications for customized products65:00 Stewart demonstrates spaced repetition app for memory improvement and knowledge retention, explaining relevance realization problem that AI agents cannot solve without embodimentKey Insights1. Stewart Alsop started AI Whisperers in Buenos Aires after leaving his role at Invisible Technologies, which was OpenAI's largest vendor for RLHF work. He noticed that machine learning engineers at tech companies lacked the humanities background needed to properly interact with large language models, which are fundamentally linguistic tools. This led him to create weekly workshops teaching non-technical people how to use AI effectively, running events every Thursday for two years straight. The group attracted intense geeks from the start and eventually led to Stewart speaking right after Vitalik Buterin at DevConnect, marking a significant milestone for the community.2. Large corporations are resistant to AI adoption due to multiple factors including political dynamics within organizations and employees fearing job loss. Many companies that grew during the pandemic are now using AI as an excuse to downsize when the real issue is inefficiency from rapid expansion. Stewart observed that even technical people in machine learning often don't understand how to properly use AI tools because they lack linguistic and humanities training. The fundamental problem is educational, requiring companies to train people how to use these new tools while those same people resist learning them.3. Vibe coding has evolved significantly with Claude Code being a game changer that reduced the technical barrier to entry. Before Claude Code, developers needed substantial technical knowledge to work through constant doom loops and debugging cycles. The success of coding AI tools stems from thirty years of testing infrastructure that provides clear yes or no feedback on whether code works. This infrastructure doesn't exist in the same way for manufacturing, science, and other fields, which is why software became the dominant area for AI assistance initially.4. Claude's quality degradation over recent months resulted from multiple factors including distillation attacks by Chinese companies who reverse engineered Anthropic's reasoning capabilities. Anthropic had hired philosophers, sociologists, and psychologists to develop exceptional reasoning in Claude 4.5, but this was expensive to run. When Chinese models like Kimi copied these capabilities at one tenth the cost, and when mainstream users flooded the platform before Anthropic's planned IPO, the company had to reduce quality to manage computational costs. This represents a significant loss for power users who relied on Claude's superior reasoning abilities.5. Stewart built a podcast recording application to replace Riverside because he needed API access to automate workflows, which Riverside wanted one thousand dollars monthly to provide. The technical challenge involves syncing audio and video from local recordings on multiple computers with different clocks through a server, then merging them so voices match lip movements. This problem requires understanding complex timing issues across different network conditions and file formats. Stewart has been working through AI psychosis for months on this FFMPEG pipeline problem, illustrating how vibe coding still requires building intuition about technical problems even without traditional coding knowledge.6. The transition from expert software to prosumer software represents a major opportunity for AI-enabled tools. Expert software like Photoshop, Blender, and terminal interfaces have extreme complexity that intimidates beginners, but AI is making these capabilities accessible through natural language. The reign of specialists is ending as generalists with broad knowledge and curiosity can now build complete applications by leveraging AI to fill technical gaps. This shift particularly benefits entrepreneurs and founders who specialize in getting into difficult situations and figuring them out, even when they originally thought tasks would be easier than they turned out to be.7. Building applications with AI requires accepting massive time investments beyond initial estimates and developing strategies for overcoming knowledge gaps. Michael estimated his ecommerce content generation app would take months but spent nearly a year working over nine hours daily, while Stewart spent months solving audio-video sync issues. Success requires using tools like deep research to understand how competitors solve problems, maintaining separate planning and coding agents, and learning to ask the right questions. The key insight is that vibe coders can achieve ninety percent of functionality independently, but the final ten percent often requires understanding specific technical concepts that AI cannot intuit without proper context and domain knowledge.

In this episode of Business Brain, we get practical about putting AI to work on the everyday stuff, not just the big business plays. Shannon walks through building a custom food log with Claude after a doctor’s visit — snap a photo, let the AI ID the ingredients, get a clean report emailed before the next appointment, and pin it to your home screen as a web app. The bigger lesson: if all we’re doing is chatting back and forth with our favorite AI, we’re missing roughly 90% of what it can do. Don’t know what we’re missing? Ask the bot itself. Build the custom tool, start a health folder (eyes wide open about feeding personal data to a third party), and let it take the grunt work off our plate. Then Dave drops a reframe worth keeping: AI isn’t artificial intelligence, it’s assistive intelligence. It helps us, it doesn’t replace us — we’re still the one driving the bus. We connect it to history, too: when Photoshop hit, the same panic about lost jobs and “that’s not real art” played out, and it became the assistive tool every creative now relies on. The internet did the same, creating community and abundance. Every leap looks scary until it becomes the thing that powers our Charmed Life. Used right, with guidance, this is a superpower — so stop treating the chatbot like just a chatbot and start letting it build. 00:00:00 Business Brain – The Entrepreneurs' Podcast #757 for Casual FridAI, May 29, 2026 May 29th: National Paperclip Day Trade a Paperclip for a House 00:01:29 Claude Food Tracker The SAAS-Pocalypse Create an iOS Home screen app with Claude Don't forget to use your favorite ChatBot for every day things If you're only using your ChatBot as a ChatBot, you're missing out. Ask your ChatBot what you're missing! 00:09:16 SPONSOR: Shopify – For anyone to sell anywhere, sign up for a one-dollar-per month trial period at Shopify.com/BusinessBrain and upgrade your selling today! 00:10:48 SPONSOR: Bitdefender. Keep your small business safe with Bitdefender Ultimate Small Business Security. Save 30% when you go to https://bitdefender.com/BRAIN 00:12:14 AI is Assistive Intelligence AI isn't going to take jobs Photoshop didn't take jobs They both let people replace their jobs with different jobs “Before the Internet, why did you need a computer?” This Episode's Big Takeaway: Think about AI as your Assistive Intelligence 00:22:59 Business Brain 757 Outtro Check out Business Brain Blueprints Tell Your Friends! Business Blueprints Review Business Brain Subscribe to the show feedback@businessbrain.show Call/Text: (567) 274-6977 X/Twitter: @ShannonJean & @DaveHamilton, & @BizBrainShow LinkedIn: Shannon Jean, Dave Hamilton, & Business Brain Facebook: Dave Hamilton, Shannon Jean, & Business Brain The post FridAI – Assistive Intelligence – Business Brain 757 appeared first on Business Brain - The Entrepreneurs' Podcast.

From 05/28 Hour 4: The Sports Junkies discuss the top storylines around the celebrity world.

With both Brad and Dave nominated for awards this year, the guys spiral into a surprisingly deep conversation about awards, marketing, ego, and whether creators should plaster “award nominee” stickers all over their books. Later, they tackle a listener question about using 3D models, digital sets, and reference material in comics production — leading to a fascinating behind-the-scenes look at how both creators actually build comics pages in tools like Clip Studio Paint and Photoshop. Along the way, they discuss why imperfections matter in cartooning, how typography affects visual storytelling, and why “cheating” is often just another word for “working smarter.” Today's Show Should you put an award nomination on a book cover? UPDATE: Hugo Award voter packet "WSFS Membership"  Using sets and other pre-made background materials  UPDATE: Patreon Quips is now available on desktop You get great rewards when you join the ComicLab Community on Patreon$2 — Early access to episodes$5 — Submit a question for possible use on the show AND get the exclusive ProTips podcast. Plus $2-tier rewards.If you'd like a one-on-one consultation about your comic, book it now!Brad Guigar is the creator of Evil Inc and the author of The Webcomics Handbook. He is available for personal consultations. Dave Kellett is the creator of Sheldon and Drive. He is the co-director of the comics documentary, Stripped.

Margo is joined by Abby for another edition of Creative Current Events—the series where they unpack the stories, launches, trends, and internet conversations shaping the creative world right now. This episode covers everything from the rise (and critique) of the "beige epidemic" in interiors to unexpected retail collaborations, bold beauty marketing, shifting brand landscapes, and artists doing interesting work outside the algorithm. Along the way, they discuss what these moments reveal about creativity, originality, accessibility, and where culture seems to be headed next. Mentioned in this episode: The Beige Epidemic – House Beautifulhttps://www.housebeautiful.com/design-inspiration/a71336893/the-next-issue-beige-epidemic/ Colorful interiors and inspiration – The House That Lars Builthttps://thehousethatlarsbuilt.com/ Jonathan Adler collection at Michaels https://www.michaels.com/shop/jonathan-adler Review video of the Jonathan Adler collection https://www.instagram.com/reels/DXkZqF7kvtv/ GIMP's redesign: Photoshop familiarity without the subscription https://www.digitaltrends.com/computing/open-source-gimp-reskin-gives-it-a-familiar-photoshop-look-without-the-hefty-fee/ Explore GIMP https://www.gimp.org/ Create! Magazine – Call for Art Submissions https://www.createmagazine.co/call-for-art Garden & Gun Magazine https://gardenandgun.com/ Cover artist feature – Brian Steely https://www.instagram.com/p/DYe7ldUx2YO/ The Bitter Southernerhttps://bittersoutherner.com/ The Ordinary's skewered beauty marketing approach https://www.creativereview.co.uk/the-ordinary-markup-marche-pop-up-supermarket-uncommon/ "The Most Valuable Place a Brand Can Be Right Now Isn't Online" https://www.instagram.com/p/DWZzckqEYuM/ Shein acquires Everlane discussion https://www.instagram.com/p/DYfcq7Ik49Y/ The "tone deaf" commencement speech conversation https://www.instagram.com/reel/DYNfbyEP82q/ Wylie Welling × Carhart https://wyliewelling.com/blogs/journal Artist & listener spotlight: Renee Reid https://www.instagram.com/reneereidcreations Connect with Abby: https://www.abbyjcampbell.com/ https://www.instagram.com/ajcampkc/ https://www.pinterest.com/ajcampbell/   Connect with Margo: www.windowsillchats.com www.instagram.com/windowsillchats www.patreon.com/inthewindowsill

Matt and Shelby are BACK with a new season of The CMD-Z Show! In this episode, they give an update on what they've been up to as well as an update on the motion design industry. It's been 565 days since their last episode and A LOT has changed!

Peter and Eden kick off with a leisurely check-in — outdoor Godzilla screenings, Eden's miniature laundry room diorama with a 1/12-scale mahjong set, Peter's Amon Amarth/Dethklok concert recap, and new releases from Periphery and LE SSERAFIM. Then, with an assist from ChatGPT's Codex, Peter has assembled roughly 45 of the internet's most-agreed-upon worst album covers for a tier list ranking — S being the most catastrophically bad. The resulting hour-ish is essentially an appreciation of outsider art, deeply cursed Photoshop, and the specific chaos that was '90s rap cover design. Key revelation: the Rednecks, who made Sex and Violins, are Swedish, and that is the Cotton Eye Joe.SHOW NOTESOutdoor Movie Night Gone Right: Eden's projector plan collapsed due to daylight, so they wheeled the TV outside and screened the 1998 Godzilla with Matthew Broderick — which Eden argues holds up better than its reputation suggests. Friend L contributed an observation about American Godzilla's gender presentation that Peter and Eden both find compelling.Daikon 3 & 4 / Studio Gainax Origin Story: Eden showed friends the legendary fan animations made for the early-'80s Daikon convention circuit — blatant copyright-violating anime crossovers that nonetheless launched the careers of the people who would go on to found Gainax (Neon Genesis Evangelion) and later Trigger (Delicious in Dungeon, Season 2).Roombox 3 Progress: Eden's current miniature diorama project is a laundry room featuring a vending machine, an arcade cabinet, and a complete 1/112-scale Chinese mahjong set (all 144 tiles). Models are being dressed in fabric soft clothing rather than left as bare plastic.LE SSERAFIM New Album: Eden's favorite K-pop act has a new record out. The second track samples La Macarena, which prompted a mild generational crisis at the comic shop when the younger staff noted it predates them.Dungeon Crawler Carl / Discworld Detour: Peter is finishing Book 8 of Dungeon Crawler Carl (narrated by Jeff Hayes, whose per-character voice work Peter genuinely enjoys despite usually disliking that approach) and has resolved to go into Terry Pratchett's Discworld next as a pressure valve from heavy genre fiction.Amon Amarth / Dethklok Concert: Peter drove to Salt Lake for the Amon Amarth/Dethklok tour. Amon Amarth was a highlight — Johan Hegg commanding a full audience Viking rowing session — while Dethklok left Peter cold; the Metalocalypse spectacle on screen keeps the audience at arm's length from the music. Castle Rat opened and was a solid short set.Forza Horizon 6: Peter is ~15 hours in on the Japan-set new installment and finding it an ideal low-commitment diversion. Fits easily into 20-minute sessions or longer stretches.Periphery — A Pale White Dot: New album from the djent-adjacent prog-metal band. Peter's read: fewer peaks but also fewer low points than usual — more consistent, somewhat more middling. Flagged as interesting rather than essential.Bad Album Art Tier List: The main event. Peter used Codex to compile ~45 covers from various internet "worst of" lists into a tier list app, with S = truly worst. Notable rankings: The Faith Tones' Jesus Use Me nearly got its own tier above S; Rednex' Sex and Violins landed S upon discovering the band is Swedish and responsible for the definitive Cotton Eye Joe; Iron Maiden's Dance of Death — described as looking like "Baby's first Blender" — is an A despite being one of their best 21st-century albums; Badfinger's Ass (donkey with headphones, hand holding a carrot) closed the list as a deserved S.Creed Sidebar: Human Clay cover triggers a genuine conversation about Creed's arc — good debut, one-and-a-half good albums, then nothing. Peter credits Alter Bridge as the redemptive outcome.

DxO is offering PetaPixel Podcast listeners 15% off any DxO software, including the brand-new Nik Collection 9, by using the code 'PetaPixel' at checkout. Nik Collection 9 adds AI-powered object and depth masks, plus new creative filters like Halation, Color Grading, Chromatic Shift, and Glass Effect in Color Efex and Analog Efex. Blending modes now work inside the plug-in stack, so you can experiment without opening Photoshop. All AI processing runs locally on your machine, so no images leave your computer. Head over to dxo.com and check out Nik Collection 9 and use code 'PetaPixel' to save 15%!With a PetaPixel Membership, not only can you support original PetaPixel reporting and in-depth reviews, but you can also remove ads from the website and gain access to some seriously great perks, too. Members get $15 off the Moment Store, 5% off certified pre-owned gear from KEH, 10% off lighting gear from FJ Westcott, and now can download full-resolution RAW files and JPEGs from the latest cameras and lenses. It costs just $3 per month or $30 per year. Join today!This week on the PetaPixel Podcast, the team is in-person at Fujifilm's Tokyo headquarters to chat with Yuji Igarashi about lenses, specifically how it feels the results of the Focus on Glass vote went. Plus, Sony gets thrashed for bad AI advice, Nikon is rumored to be selling to Essilor Luxxotica, TSMC wants to build a sensor fab in Japan, and Jordan Drake gives his thoughts on the Lumix L10. All that and more!Watch Fujifilm's 2026 Focus on GlassCheck out PetaPixel Merch: store.petapixel.com/ We use Riverside to record The PetaPixel Podcast in our online recording studio.We hope you enjoy the podcast and we look forward to hearing what you think. If you like what you hear, please support us by subscribing, liking, commenting, and reviewing! Every week, the trio go over comments on YouTube and here on PetaPixel, but if you'd like to send a message for them to hear, you can do so through SpeakPipe.In This Episode:00:00 -Intro08:16 - You can make this 3D printed digital rangefinder at home11:04 - Nikon speculators believe it might sell to Essilor Luxotica15:05 -

I've been doing social media for ministry the same way for years… mostly reels, short-form video… But recently, I added THIS one new strategy… And it's getting more engagement than almost anything else I post. And the crazy part? Most people are either ignoring this… or doing it completely wrong. I'm not replacing video… I'm stacking this on top of it. Stick around, because I'm going to show you how you can get a tool, to do it for you, for FREE. Yeah, not joking! THE CAROUSEL ENGINE https://www.patreon.com/posts/carousel-engine-155124829?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link

Memorial Day weekend is here, and I caught my first sunburn of the year—Florida summer is back. It's also the time when small, unexpected moments seem to appear out of nowhere. This weekend brought Maya's ballet recital and flashed me back 35 years to a strip mall computer store, where I left with an $800 piece of plastic in my hand—and an unexpected memory. If you've ever pondered your place on your life's timeline, slow down for a bit and come along—this story is worth a listen. Featured Story In 1990, I walked out of a small strip mall computer store with Photoshop 1.0 on a CD—eight hundred bucks I didn't really have, but I was determined to become a producer-editor, which we used to call a predator back then. Then I saw them. An older man is being pushed to a car in a wheelchair, totally silent. Thirty feet behind him, a baby in a stroller is kicking her feet, making baby noises, and her parents are smiling. Same parking lot. Two strollers heading toward cars that they didn't drive themselves to. Standing there with my $800 CD, I realized the only difference between those two strollers was time. That moment stayed with me. Important Points Remember: the only difference between the wheelchair and stroller is time. We're each somewhere on that timeline—recognizing it is key to living purposefully. Most of the stuff you think will matter probably won't, and the stuff you almost miss turns out to be almost everything. Take a breath, look at where you actually are today, and decide who you want sitting next to you in the parking lot. Memorable Quotes The only difference between those two strollers was the time and a little distance across the parking lot that day. Most of the stuff we think will matter doesn't, and most of what we almost miss turns out to be almost everything. The more I observe, the more I take a breath and just go, what a ride. Life moves fast, and things really do change. Scott's Three-Step Approach Stop in the middle of your regular routine today and actually notice the moments and people most of us are too busy to see. Take an honest breath and figure out where you actually stand on the timeline between the stroller and the wheelchair. Choose who you want pushing you across that parking lot one day, then go invest your time and attention in them now. Chapters 0:02 - First sunburn and the start of Florida summer 1:13 - Maya's ballet recital is the cutest thing ever 1:57 - Walking out of the store with $800 Photoshop on a CD 5:43 - The wheelchair and stroller in the same parking lot 6:28 - Poopy diapers and lessons from mom at 94 8:50 - Where are you actually sitting on the timeline today 9:02 - Closer to the wheelchair and what mattered most SEO Description Scott shares a 1990 parking lot moment — a wheelchair and a baby stroller — that reveals where you actually sit on life's timeline today. Connect With Me Search for the Daily Boost on YouTube, Apple Podcasts, and Spotify If you enjoy the Daily Boost, you might like Notes From Scott. A few mornings each week, I send a short note with something I've been thinking about or noticing lately. Sometimes those ideas turn into podcast episodes later. You can sign up at https://notesfromscott.com. Email: support@motivationtomove.com Main Website: https://motivationtomove.com YouTube: https://youtube.com/dailyboostpodcast Instagram: https://instagram.com/heyscottsmith Facebook Page: https://facebook.com/motivationtomove Facebook Group: https://dailyboostpodcast.com/facebook Learn more about your ad choices. Visit megaphone.fm/adchoices

Mike Rosado interviews Raleigh-based illustrator and designer Ian Wenstrand about his highly detailed, vibrant illustrations blending cityscapes, technology, and imaginative world-building. Wenstrand describes drawing constantly as a kid, studying studio art at University of the Cumberlands (recruited to swim), and moving from production artist retouching roles into graphic design before illustration work organically became steady freelance. He self-published a children's book inspired by artists like Graeme Base and cross-section illustrators, then gained momentum through public art opportunities including a 2018–2019 Citrix window mural and later a major Film NC tourism brochure project featuring five regional illustrations packed with 50+ movie Easter eggs. He outlines his process (two sketch phases in Procreate, then Photoshop for color), time demands, work-life balance with two kids, and the importance of tight contracts to prevent scope creep. Wenstrand shares influences (IC4 Design, Moebius, sci-fi film aesthetics), discusses collaborative "Easter egg" client input, and explains why he avoids using AI, adding an AI clause to contracts and valuing the human creative process. Host: Mike Rosado (mrcraleigh.com) (instagram.com/ekimodasor) Post Production: Max Trujillo (instagram.com/trujillomedia) Sponsors: MRC (mrcraleigh.com) and Burny Wild's (burnywilds.com) 

If you've ever been told that you need Photoshop to edit better photos, that you should always shoot at f/11, or that expensive gear is the secret to better photos… this episode is for you. In this episode of Master the Moment, I'll break down 8 of the biggest photography myths holding outdoor photographers back and explain what actually matters when it comes to capturing sharper, cleaner, more compelling landscape images.Today's podcast is sponsored by my friends over at ⁠⁠⁠⁠⁠⁠⁠⁠MPB⁠⁠⁠⁠⁠⁠⁠⁠, the place to buy and sell used photography gear. Go online to get a quote for your gear today: https://tinyurl.com/mse6bzk2

What was it really like when photography went digital?In this episode of The Camera Shake Podcast, I sit down with legendary photographer, educator, Photoshop Hall of Fame inductee, and Canon Explorer of Light Eddie Tapp to explore one of the most fascinating transformations in photography history.From darkrooms and film photography to Photoshop, digital workflow, colour management, printmaking, drones, and now AI, Eddie has lived through—and helped shape—some of the most pivotal moments in professional photography.We talk about:

News and Updates: Photoshop's Accidental Origin: A 1987 PhD student's grayscale display fix evolved into Adobe Photoshop, launching commercially in 1990 and reshaping photography, publishing, and design forever. Gemini Hits the Road: Google is rolling out its Gemini AI to millions of cars with Google built-in, enabling natural, conversational interaction for navigation, tasks, and hands-free responses. Gemini Ads Coming Soon: Google's chief business officer signaled openness to placing ads inside the Gemini app, shifting from its previously ad-free stance amid growing monetization pressure. Samsung Smart Glasses Leaked: Samsung's upcoming Galaxy Glasses, code-named "Jinju," rival Meta's Ray-Bans with a 12MP camera and Snapdragon chip, priced between $379 and $499. Starlink Poised for Explosive Growth: A Starlink chip supplier projects LEO satellite internet subscribers will surpass 100 million by 2028, driven by falling hardware costs and new global competitors. Utah VPN Law Takes Effect: Utah became the first U.S. state to hold websites liable for users masking locations via VPNs, drawing criticism as technically unenforceable and harmful to privacy. BlackBerry's Quiet Comeback: BlackBerry's QNX software powers 275 million vehicles worldwide, now representing half the company's revenue and driving four consecutive profitable quarters for the once-struggling brand. Google Translate Turns 20: Google Translate now serves one billion monthly users across nearly 250 languages, adding AI-powered pronunciation practice and real-time conversation features to mark its 20th anniversary.

Car companies are beginning to use AI tools to radically speed up their development process, which could change the cars we drive forever — and have some big effects on the people who make them now. Verge contributor Tim Stevens explains. Then, The Verge's Hayden Field catches us up on Codex vs. Claude Code, Anthropic vs. the US government, the vibes at OpenAI, and more, before helping answer a question on the Vergecast Hotline (call 866-VERGE11 or email ⁠vergecast@theverge.com⁠!) about whether all the recent tech layoffs are really about AI. Further reading: ⁠The AI-designed car is taking shape | The Verge⁠ ⁠Pentagon strikes classified AI deals with OpenAI, Google, and Nvidia — but not Anthropic⁠ ⁠Google employees ask Sundar Pichai to say no to classified military AI use | The Verge⁠ ⁠Anthropic's new cybersecurity model could get it back in the government's good graces | The Verge⁠ ⁠Microsoft and OpenAI's famed AGI agreement is dead | The Verge⁠ ⁠Here's how the new Microsoft and OpenAI deal breaks down | The Verge⁠ ⁠ChatGPT downloads are slowing — and may cause problems for OpenAI's IPO | The Verge⁠ ⁠Claude can now plug directly into Photoshop, Blender, and Ableton | The Verge⁠ ⁠OpenAI's new security model is for ‘critical cyber defenders' only | The Verge⁠ ⁠Anthropic releases a new Opus model amid Mythos Preview buzz | The Verge⁠ ⁠Jack Dorsey's Block cuts nearly half of its staff in AI gamble | The Verge⁠ Subscribe to The Verge for unlimited access to theverge.com, subscriber-exclusive newsletters, and our ad-free podcast feed.We love hearing from you! Email your questions and thoughts to vergecast@theverge.com or call us at 866-VERGE11. Timestamps are approximate.) 00:00:00 Intro 00:02:00 Today Show Preview 00:04:00 Car Design Primer 00:08:00 AI Speeds Up Design 00:13:00 Clay Models and Craft 00:15:00 Jobs Pipeline Risk 00:18:00 Software Defined Cars 00:20:00 Regulation and Safety 00:27:00 Slate Truck Update 00:34:00 Claude Code vs Codex 00:42:00 OpenAI Vibes Check 00:44:00 PR vs AI Doomerism 00:48:00 Pentagon Deals Exclude Anthropic 00:53:00 Mythos Reality Check 00:56:00 RIP AGI Moment 01:04:00 Hotline AI Layoffs ROI 01:13:00 Wrap Up and Sign Off Learn more about your ad choices. Visit podcastchoices.com/adchoices

[Contemporary Realism] Alexandra Manukyan brings a background in fashion, graphic design and Photoshop to her extraordinary new works that feature fashion, beauty and nature. Hear her story and the way she thinks about art in today's episode, which is sponsored by American Art Collector. Read more at americanartcollector.com.

Dave dives into the latest strategies for Amazon listing image optimization, A/B testing, and AI-driven Amazon listing images with Michael Shackleford, a former EcomCrew Premium member and SaaS owner. They share what they've learned works best on Amazon.  Timestamps 00:00 - Introduction and Michael's e-commerce journey from poker to Amazon seller 00:18 - How poker shares mental models with online selling 00:56 - The risk and reward in gambling versus Amazon 2:20 - The importance of AI in Amazon image creation in 2026 2:29 - Do's and don'ts for Amazon main gallery images today 3:08 - How to test variations of main images effectively 3:37 - Creative ideas for image variations: packaging, lifestyle, environment 4:59 - Flexibility in Amazon's white background rule and embellishments 6:29 - Optimal image resolution and size considerations 7:10 - Mobile optimization and best practices 8:14 - The effectiveness of Amazon Manage My Experiments vs. third-party polling tools 9:16 - Strategies for high-ticket product testing with limited traffic 10:37 - Manual image switching schedule for more reliable tests 12:11 - Using PPC data to measure image performance 13:20 - The versatility of Prolific for custom surveys 14:08 - Secondary images: core types and customer objection handling 15:45 - Designing mobile-friendly, visual answer images 17:09 - Diminishing returns of lower-positioned listing images 19:09 - Image order placement for maximum impact 20:10 - Avoiding poor-quality images  21:14 - Tips for avoiding AI-generated "slop" 22:10 - The myth of JSON prompts 23:58 - Crafting effective prompts for product scenes 25:00 - Why reference images are important  26:32 - Issues with AI-generated images  28:53 - Ensuring realistic human figures 30:45 - Photoshop's new AI capabilities 31:26 - Introduction to generupt.com 34:10 - Gathering market data with extensions 37:11 - Staying ahead in Amazon Resources & Links generupt.com Prolific A/B testing tool Photoshop Firefly AI GPT Image 2 (search for latest tools) Market analysis & review scraping extension

Our 243rd episode with a summary and discussion of last week's big AI news!Recorded on 04/29/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at andreyvkurenkov@gmail.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:OpenAI released GPT-5.5 with strong coding-oriented improvements, a system card discussing chain-of-thought monitorability and misalignment testing, higher pricing than GPT-5.4, and notable quirks like a system-prompt warning about “goblins.”xAI launched Grok Voice Think Fast 1.0, claiming large benchmark leads for real-time voice agents and reporting major Starlink customer-support automation and sales conversion impact.DeepSeek open-sourced DeepSeek V4 (Pro and Flash) featuring MoE scaling and 1M-token context via hybrid/compressed attention changes, while Tencent released Hunyuan 3 preview with weaker benchmark performance; a new long-horizon agent benchmark (Clawmark) shows low task success rates.Major business, legal, and policy updates include Google's planned up-to-$40B investment and 5GW compute commitment to Anthropic, Meta's AWS Gravitron deal and China blocking Meta's Manus acquisition, a revamped OpenAI–Microsoft agreement, ongoing Musk–OpenAI trial developments, and new safety/security research on sabotage, document degradation under delegation, and bit-flip attacks.Timestamps:(00:00:10) Intro / Banter(00:02:00) News Preview(00:02:26) Response to listener comments(00:02:55) SponsorsTools & Apps(00:05:55) OpenAI Unveils Its New, More Powerful GPT-5.5 Model - The New York Times(00:23:33) xAI Launches grok-voice-think-fast-1.0: Topping τ-voice Bench at 67.3%, Outperforming Gemini, GPT Realtime, and More - MarkTechPost(00:29:00) Claude can now plug directly into Photoshop, Blender, and Ableton | The VergeProjects & Open Source(00:29:38) China's DeepSeek releases preview of long-awaited V4 model as AI race intensifies(00:47:05) Tencent Unveils Hy3 preview; Model Enhances Agent Capabilities and Real-World Usability - Tencent 腾讯(00:50:14) ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker AgentsApplications & Business(00:53:03) Google Plans to Invest Up to $40 Billion in Anthropic(00:56:26) Meta will use hundreds of thousands of AWS Graviton chips(00:59:51) China blocks Meta's $2 billion takeover of AI startup Manus(01:01:45) OpenAI shakes up partnership with Microsoft, capping revenue share payments(01:07:13) Elon Musk Testifies of AI Risk at Trial, Says OpenAI Tried to ‘Steal' a Charity - WSJ(01:11:50) Judge rejects DOJ bid to delay Anthropic appeal in Pentagon dispute(01:14:42) Google's Gemini can now run on a single air-gapped server — and vanish when you pull the plug(01:19:07) DeepMind's David Silver just raised $1.1B to build an AI that learns without human data | TechCrunchPolicy & Safety(01:22:47) Evaluating whether AI models would sabotage AI safety research(01:28:59) LLMs Corrupt Your Documents When You Delegate(01:32:50) Temporal Sparse Autoencoders: Leveraging the Sequential Nature of Language for Interpretability(01:39:53) Memorandum on Adversarial Distillation of American AI Models(01:41:41) Teen boys are dating their AI chatbots—and experts warn it could kill their careers | Fortune(01:43:57) Announcing the Anthropic Economic Index Survey(01:45:21) Scoop: CISA lacks access to Anthropic's MythosSynthetic Media & Art(01:48:03) Taylor Swift Files to Trademark Voice and Likeness to Protect Against AI MisuseResearch & Advancements(01:49:15) Maximal Brain Damage Without Data or Optimization: Disrupting Neural Networks via Sign-Bit FlipsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

HT2600 - Photoshop Has Become Too Damn Complicated I know many photographers who think that Photoshop is the cat's meow of digital processing. I'm not one of them. For me, the engineers have taken the usability right out of Photoshop by making it so "capable." For me, using Photoshop always feels a bit like driving to the grocery store in a Formula 1 race car. I use 90% of Lightroom's features and capabilities; I use 3% of Photoshop's features and capabilities. The point I'm trying to make is not about Photoshop, but rather about choosing the tools that fit your needs. The purpose of software is to make our tasks easier, not more complicated. Show your appreciation for our free weekly Podcast and our free daily Here's a Thought… with a donation Thanks!

A reporter asked Trump if he'd pardon Ghislaine Maxwell now that the Supreme Court killed her last appeal, and he immediately went into his usual “Who? Never heard of her” routine like he was auditioning for Men in Black. It was pure comedy—he acted like Ghislaine was some random lady who wandered into his photos by accident, not someone who used to orbit the same high-society circles as him and Epstein. The man delivered his line so confidently you'd think he really believed it: “I don't know her, but I hear she's doing well.” Yeah, sure, Don—she's “doing well” in prison. Real cozy setup between chow line and lockdown. The guy could be caught holding a selfie stick with her and still swear it's Photoshop and “fake news.”Trump's selective amnesia is practically a stage show at this point. Every time one of his old pals gets indicted, he suddenly turns into a witness protection participant. “Never met them, don't know them, wish them well.” It's become a brand. The funniest part is how he says it with total confidence, like he's daring the world to remember what he's pretending to forget. When asked about a pardon, you could see the wheels spin—“What's in it for me?”—but in true Trump fashion, he skipped the answer and rewrote history instead. Because in his world, he doesn't need to pardon anyone; he just deletes them from existence. One minute you're clinking glasses at Mar-a-Lago, the next you're “Ghislaine who?”to contact me:bobbycapucci@protonmail.comsource:

Solo Jim: I have nothing left, except Spider-man and Jim. That's right no Mike or Jeff this week but Jim is your savior on this fine Adam Scott Nickelback Birthday Bash.Stand By Me In Theaters: Corey Feldman snuck into a screening of Stand By Me and couldn't stop filming the screen for his social media. Fine this dude for piracy.Crowd Controversy: EROK has launched an investigation into the crowd photos from Goonies and Stand By Me screenings that have been posted by Corey. He has an expert on hand and everything! Is this a thing?COREY FELDMAN!, SHOW STOPPER!, LET'S JUST TALK!, DON CHEADLE!, BOOGIE NIGHTS!, JIM AND THEM IS POP CULTURE!, CHRIS HANSEN!, HAVE A SEAT!, DATELINE!, TO CATCH A PREDATOR!, REAL ONES!, LVL UP EXPO!, HACKAMANIA!, LIVE!, SOLO SHOW!, NO JEFF!, NO MIKE!, ONLY JIM!, JUST JIM!, FAKE FRIENDS!, WHO'S LEFT!?, FRIDAY NIGHT!, 22 NECKLACE!, REAL ONES!, KISS EM IF YOU GOT EM!, AUDITIONS!, NICKELBACK BIRTHDAY BASH!, ADAM SCOTT!, NO MAS!, SIPPING ON SHOTS!, PO BOX!, HOOK!, TEXAS CHAINSAW MASSACRE!, CHRIS HANSEN CAMEO!, FIRST THING IN THE MORNING!, GOBLIN GHOUL!, APRIL FOOLS!, ADAN GONZALEZ!, STAUNCH TV!, DRAFTED!, IRAN!, WAR!, PROTECT ME!, ICP!, MIRACLES!, THE BOY BLUE!, COREY FELDMAN!, STAND BY ME!, SNEAK INTO THEATER!, WEDNESDAY!, WATCHED STAND BY ME TOGETHER!, THEATRICAL RELEASE!, MILES APART!, CHOPPER SIC BALLS!, JIM AND THEM FINALE SPECIAL!, INTERVIEW!, PLAYING WITH YOUR FRIENDS!, AWKWARD!, ANNOYED!, RUDE!, YAWNING!, AARP!, WIL WHEATON!, ANNOYING!, PERFORMATIVE!, SUMMERTIME!, JERRY O'CONNELL!, BORED!, SHALLOW!, RIVER PHOENIX!, VEGETARIAN!, INTO MUSIC!, COPIED!, BREAKING BAD!, STOLE DEAD PEOPLE'S HABITS!, FENIX TX!, KRISTIN!, DENISE RICHARDS!, BRAVE BROWSER!, FAKE CROWD!, PHOTOSHOP!, AI!, EROK!, SATURATION!, CONTRAST!, DOCTORED PHOTOS!, INVESTIGATION!, FIVERR!,

Solo Jim: I have nothing left, except Spider-man and Jim. That's right no Mike or Jeff this week but Jim is your savior on this fine Adam Scott Nickelback Birthday Bash.Stand By Me In Theaters: Corey Feldman snuck into a screening of Stand By Me and couldn't stop filming the screen for his social media. Fine this dude for piracy.Crowd Controversy: EROK has launched an investigation into the crowd photos from Goonies and Stand By Me screenings that have been posted by Corey. He has an expert on hand and everything! Is this a thing?COREY FELDMAN!, SHOW STOPPER!, LET'S JUST TALK!, DON CHEADLE!, BOOGIE NIGHTS!, JIM AND THEM IS POP CULTURE!, CHRIS HANSEN!, HAVE A SEAT!, DATELINE!, TO CATCH A PREDATOR!, REAL ONES!, LVL UP EXPO!, HACKAMANIA!, LIVE!, SOLO SHOW!, NO JEFF!, NO MIKE!, ONLY JIM!, JUST JIM!, FAKE FRIENDS!, WHO'S LEFT!?, FRIDAY NIGHT!, 22 NECKLACE!, REAL ONES!, KISS EM IF YOU GOT EM!, AUDITIONS!, NICKELBACK BIRTHDAY BASH!, ADAM SCOTT!, NO MAS!, SIPPING ON SHOTS!, PO BOX!, HOOK!, TEXAS CHAINSAW MASSACRE!, CHRIS HANSEN CAMEO!, FIRST THING IN THE MORNING!, GOBLIN GHOUL!, APRIL FOOLS!, ADAN GONZALEZ!, STAUNCH TV!, DRAFTED!, IRAN!, WAR!, PROTECT ME!, ICP!, MIRACLES!, THE BOY BLUE!, COREY FELDMAN!, STAND BY ME!, SNEAK INTO THEATER!, WEDNESDAY!, WATCHED STAND BY ME TOGETHER!, THEATRICAL RELEASE!, MILES APART!, CHOPPER SIC BALLS!, JIM AND THEM FINALE SPECIAL!, INTERVIEW!, PLAYING WITH YOUR FRIENDS!, AWKWARD!, ANNOYED!, RUDE!, YAWNING!, AARP!, WIL WHEATON!, ANNOYING!, PERFORMATIVE!, SUMMERTIME!, JERRY O'CONNELL!, BORED!, SHALLOW!, RIVER PHOENIX!, VEGETARIAN!, INTO MUSIC!, COPIED!, BREAKING BAD!, STOLE DEAD PEOPLE'S HABITS!, FENIX TX!, KRISTIN!, DENISE RICHARDS!, BRAVE BROWSER!, FAKE CROWD!, PHOTOSHOP!, AI!, EROK!, SATURATION!, CONTRAST!, DOCTORED PHOTOS!, INVESTIGATION!, FIVERR!,

HT2593 - Mindlessness It may seem silly to insist that images of a mind of their own, but just pretend with me for a few minutes of experimentation. Pull up an image in Lightroom or Photoshop that you have not previously processed. Now, just sit back and look. Try not to think. Let go of photography, of art making, of analysis, of memory. Just look with an empty mind, at least as mindless as you can. Try to be open to the image and its will. The measure of your success as an artist is how successfully you let go of being an artist. Show your appreciation for our free weekly Podcast and our free daily Here's a Thought… with a donation Thanks!