What is up, hot makers? Welcome to Hot Makes right here on The Edge of Tech, where we aim to educate and entertain the maker community. I'm Nerdy J. This is The Pooch. How you doing, Pooch? I'm fantastic, Nerdy J. How was your weekend, my friend? Oh, good.  But hey, great weekend. Others besides that, gotta hang out with the family and hang out with the little guy, which is growing every day, man. How about you? Good, I mean, we didn't get, we got a light flurry of snow, but I know it's nothing like it's been just dumping on the entire country. It seems like it was like huge West Coast storm. Now it's huge East Coast storm and everything in between. So hopefully everybody's safe and warm and not having to shovel too much. But tell us in the chat how many feet you got if you got dumped on, I don't know, how much shoveling did you have to do, Jim?  So I have a roof rake. So we pull it all down so you hopefully can stop some of the ice dams and all that and then shovel off the front and then I snowblowed the driveway and the sidewalk. But it was really windy. So every, when I was snow blowing, that like snow, you know, mist was coming back. Just blowing back on the roof, man. So I was trying to keep it low and go, but it was okay. I went down a couple neighbors deep on the sidewalks and then came back so they didn't have to get out there. But it was a lot of, you know, there's a lot of people in the same boat. Like you said, I know Shane's in the house. He got, it looks like he only got a little snow, but I bet you, I'm not sure if he's with us, but Blasto, he was down where they got a lot more than we did. They probably got, I don't even know, nine, 10 inches down there. And he's not too far from me. He's just south of me, maybe 30 minutes, you know? Yeah, yeah. Well, I know up the hill from us a little ways up in Tahoe, you know, we've got quite a few feet. So I happened to be lucky enough that I'm like right at that cutoff where I don't have to do the shoveling, you know? Dan says he only got six down there. I think, if I remember right, I think Dan's in Illinois-ish. Tripod. Illinois-ish, yes. He got eight inches in the burbs of Chicago. They were supposed to get blasted. And I think it kind of went between and we got some good snow here. But you know what? It happens, we live here and we know it's coming. Yeah, it is what it is. Well, that's your weather report for today. How about the making report, man? We got a lot of hot makes we got to go through, which is exciting. Yeah. Yeah, yeah. I looked for the hot makes. Before we get to that though, I saw Nightbot just jumped in for us. Don't forget, at the end of the show tonight, we're giving away a $50 gift card courtesy of Thangs. We had Joanna on last week. And she's a lot of fun and Thangs is sponsoring our shows till the end of February. $50 gift card one a night until then for each show. You know, I think that's awesome of them. They- I concur. It is awesome of them. We love Thangs. They are growing phenomenally. It's like more and more up there every day. And we're excited to actually talk about some people that have been featured on there recently. We'll get into that in a little bit. But speaking of Jim, I gotta say, first of all, welcome to any newcomers out there. This is hot makes. Jim kind of gave the standard bumper, but just for those of you who are new to the show, don't know what it is. It's quite simple. The way it works is you, the community out there, hashtag hot makes on anything you see out there on Twitter. And we collect all of those juicy nuggets and stuff like that that you send to us over the course of the week. And then we go over them for about an hour, you know, every Monday, right? And that's what we're here to do today. So it's thanks to your guys' contributions that we have amazing content to show off. And it also gives you guys a break from not having to be on social media so much and worry about keeping up because we're gonna keep up for you, right Jim? That's right. Oh, I 100% agree. I love going through the list and seeing everything that's tagged. Tonight's hot makes are awesome. They always are. We have some really cool stuff coming up.  I'm pumped for everything. We got a really cool show ahead. And we are working with Shane. He's in the chat right now. Unfortunately, I didn't get time to catch up with Shane this past weekend. I apologize, Shane. We were gonna try to catch up Sunday and then I got buried in snow and I lost track of time. So between this week and next week, I'm gonna be working with Shane. We think we have it working on Instagram now or he does, he's gonna be walking us, walking me through how to, you know, figure it out and get the stuff. But Shane, I know you're watching. So let's do this. Let's make it a point next couple of days to get together. I apologize. We didn't meet up yesterday, but it's been crazy. That's all it is. Well, that'll be cool. So we can basically leverage all the fantastic hot makes stuff on Instagram as well. Instagram, yeah. Fantastic. We got a lot of Instagram people out there. You guys do Institute too? Maybe. Let us know.  Oh man, I would love 65 degrees. That's rough. That's rough. Man, but I tell you what, thanks. Thank you so much for sponsoring our episodes. We're gonna jump in to a featured artist. And I alluded to it last week. This week's featured artist, Chaos Core Tech on Thangs. If you watched Joel Telling's video, 3D printing nerds video recently, they did the printable Bernie, which is sweeping the nation, right? Everything Bernie is sweeping the nation. Everything Bernie is sweeping the nation. We're sitting cozily across the nation. And across the world, really. I mean, it's been an international phenomenon. And you can find that for free on Thangs. So I'm gonna bring that in. But first, Loyal Moses, thank you so much, sir. 33, 33. Thank you so much, Loyal. Throwing some fire our way. Thank you, Loyal. We appreciate it. And by the way, Joel said, you better have watched my video. Oh, yeah, yeah. Totally. Absolutely. I know everything about the video. And it was a great video. 300%! Anyway, I love it. I love it. It was awesome that Joel is working with Thangs as well and could send people over. And this is the page. So if you go to Thangs, Thangs.com, forward slash chaos core tech, we'll drop the link in the chat right now. And you can go here. You can get not only the regular Bernie, but you can get the cartoon Bernie as well and the chairs for each one.  I mean, he's got. The regular chair and the cartoon chair. You can't forget about that, right? Yeah. Yeah, I mean, good old chaos core tech, man. Just giving the people the options. Fantastic. Obviously it's available for free. It'd be kind of, it'd be kind of crummy, you know, to actually sell a Bernie model. You know, given Bernie's background and all that stuff. This is tendencies toward socialism and whatnot, right? Bernie belongs to the people. Amen. And I tell you what, I absolutely love, I said this last week, how fast things is compared to Thingiverse. I mean, of course my 1982 servers faster than Thingiverse. So anyway, but I love that you could jump on here. You can see the model. You can flip it around. I mean, that's what happens. That's what happens there when you've been sitting for a while. You get a really flat. In a cold chair? Yeah. You want to make sure you don't sit in a cold chair too long. This is what happens. Thank you. Thanks for the shot of the Bernie Posterior. I really appreciate that. So find this for free. And Chaos Cortec, our weekly featured artist from things.com. We definitely appreciate things. And again, Chaos Cortec, we love featuring their stuff. It seems like every single week we have something from them. So. Oh, because they're just non-stop, man. You know, we've got so many non-stop creators out there. Oh, you know, Chelsea and Garrett at Chaos Cortec. We talk about graffiti and just a lot of our regulars, but I'm just saying, oh, how do they have the time and the energy to just constantly, and this is like really detailed, good stuff, right? It is. It is. I don't know the answer to that because I am struggling so much to find enough time for work and my couple of jobs in this and all that stuff. It's like these guys, you know, like, you know, if you watch Joel's video, if you watch Joel's video, there was a lot of Red Bull. It sounded like some sleepless nights, you know, but they got it done super fast and it was, it's just amazing how fast these guys are at putting stuff out. Not only that, but they did a really good job. I mean, a really good job. I know Joel's watching. I think if Joel's watching, he should tweet me, hit me on Twitter and you can come on and show your 300% Bernie at the end of the episode. 300 Bernie. Yeah, you know, the interesting thing about the Red Bull and it's when these like memes come out, right? And we've seen this with a lot of different prints. Like when movies release and stuff like there are items that just are just super hot and everybody wants to like release the first model and all that stuff because, you know, it gets a ton of eyes that way.  Obviously how important that must be to get the timing right on the release of these things and what that does for YouTubers in particular on gaining more views and stuff by kind of being first to market with it, yeah? I totally agree. And it's, you know, I struggle with that myself when we get printers and stuff like that that aren't released yet.  Actually, I think the Ender 5 Pro, when I did a video on that, I did a live stream to build it in a video following. No one believed it was real. They thought, like, this isn't a real printer because Creality didn't even have it on their website yet. And they said, do it. And I'm like, all right, let's go, you know? And then all of a sudden I started getting like, hey, it's on the website like a week later. It's like, it's like, you got to be the first to scoop it, right? You know, that's the scoop in the three to four. Right, and Uncle Jesse says 110% of solid strategy. I love it. So, you know, that brings us into the next thing. What's the next thing, Jim? The hot or not? I have a really good one. I found one I want to show you. And I promise that you, I think it's hot. I think it's amazing it's hot. I think you'll find it hot too. One of these days, I challenge you to show us a knot, but let's see the hotness. Oh, man, hot or not? So this week's hot or not comes to us from Garrett over at 3D Print Farm, not because he just tipped us $20. Thank you so much, Garrett. But thank you, Garrett. I promise it was already planned. He was going to pay us ahead. No, I'm just joking. This video is absolutely amazing. It's getting some awesome traction, almost 4,000 views already. And he posted a video on how to take and wrap any item like you see here in a lithophane. Yeah. And he uses a Coke can as an example. Now, let me know in the chat, let us know in the chat. Do you think this is hot or not? I'm actually, Garrett, I know you're in the chat watching because you just hooked us up with 20 bucks. I really appreciate that. But let me know. Is it cool if I click on your YouTube video briefly? Yeah. You're not going to demonetize this. Yeah. You're not coming after me. This is the final strike against CG. So this is the actual pictures he posted on Twitter and it is just awesome how he took a Coke can. He made a lithophane around it. And it looks awesome. It really does. I mean, it literally looks hot. It looks hot. That's right. Let us know. Radioactive also, if you think this is hot and if you've seen this video yet, if you haven't, we have a link, we'll drop it in the description. Actually, I think we already did. But I'll do it right there. We dropped it in the chat too. So you can see this tweet, it'll take you to his video. But I gotta click. Let's see if this works here. Oh, of course not. I was smart this time and I only shared, not the whole browser. So we don't show email addresses, right? But let me pull up that video real quick. Yeah. How long is the video? Only 30, 40 minutes. Oh, okay. No, I'm just joking. We got time, sure. Yeah, we got time. I mean, it's just hot makes we're missing, right? There it is. Don't take my hot makes, man. I need my hot makes. I didn't see a response, but I'm gonna take no response as we can do it. Yeah. No, this is Gary's video. So this is Gary's video here. And let me just pop this, maybe full screen. There we go. And you can see if we start in the beginning here, he found a Coke can. There we go. That's what it looks like, lit up. It's a Coke can. Yeah, yeah. And then he shows you how to make it, how to wrap it. In, I believe he's using, what is he using there? Is it Cheetah Box? I can't remember. He's gonna correct, hopefully he'll correct us. Too long, he said. So then he shows you how to make the lithophane. There's Cheetah Box. There we go. So there it is there when we're putting it on the, getting it ready to print. Okay. And he walks through all the steps and you can do this with anything that'll fit on your printer, right? I mean, you can't obviously, I couldn't do it for something this big because I don't have a printer that prints that big, but. But. But. Yeah. He shows you all the steps and then we come, I thought, let's see. Man, look at that. Let me hit play. He's so good. If you haven't seen his videos with the resin stuff, he's such good, awesome tutorials. There we go. That's where I wanted to get to. So when you're done, you get a resin lithophane just like that. Yep. And, which is clean. Yeah. Yeah. And then let's see. Bouncing around a lot. I'm sorry. There we go. There it is. There it is again. Right? I'm sure in the very beginning, there you go. He shows it. Well, that's got some sick jams. I like that. So. Oh, and it works with other beverages as well. Yeah. You can do your favorite beverage, you know, but there you go. So that is, it's just awesome. I love that you can take an object, make it a lithophane around any object and that'll fit on your printer, right? And light it up like that. He did a great job. I love that he nailed the color on the Coke can. It really did. Hashtag not sponsored by Coke, right? No. Not yet. Not yet. Hashtag. But I really, I think, check out this video. I'm gonna drop this in the chat as well. So you guys can see it. There we go. Hopefully it'll print there or there we go. Hopefully it'll post. So check out that video. I really think you guys are gonna dig it. And if you have a resin printer, man, you gotta try this. And if you do, jump on Twitter soon on Instagram and tag that with hashtag HotMakes. Let's see what we can get printed by next week. That'll be awesome. Yeah. Good heart. Or not. I say hot. I agree with all the hotness. Plenty of hotness. Who's gonna do it? Tell us, have you done a lithophane? You wanna try this lithophane technique? Heck yeah.  Well, a lithophane, wish I had one right here, I would show you. A lithophane is essentially, you could take a picture or an item just like he showed us and you print it, but you print it in a positive instead of negative. And then when you hold it up to the light, you actually from behind, right? You can actually see the picture come through whatever you print it. I do a lot on FDM prints. I've never done a lithophane on resin. I'm gonna have to try now because of that video. But there's a lot of people out there who make it just a killing off of putting lithophanes on their Facebook pages and stuff.  Everybody wants them. But they actually started a long ago with ceramic. So people used to carve these out of ceramic and then they put a light behind the ceramic. That is a fantastic, thorough explanation of lithophane's, Jim. Well done. Right off the top. Yeah, I made it up. With a little bit of history, a little history lesson for us all. I didn't know about the ceramic piece. Yeah, I started with ceramics. I loved them. I made a ton of them. Some of my favorite ones are with like concert shots, you know? And then I'm trying to remember his name. He's doing colored, full color lithophanes. And I think we've shown some on here and I can't remember his name off the top of my head. Proust, Jason Proust, right? Pattern print. I think so. He was using his rail core and he's got a great technique with this palette. Yeah. I think so, yeah, yeah. Where he lays down all the colors and then he lays a lithophane over the top. And it's awesome. Pattern to print, there you go, yes. So definitely check that out. And if you, like I said, if you haven't checked out Garrett's channel, check it out, 3D print farm. You definitely won't be disappointed. He has some awesome stuff. It's his fault that I got into lasers. I'm gonna throw it. I'm gonna throw it at him. It's his fault. Ah, finally, somebody else taking blame. Yes. I told my wife that she didn't believe me. So what are you gonna do? Yeah, thank you, Sir Will. That's right. LithophaneMaker.com is a great resource. Yes. Yes, that's a awesome resource. I see Ron. Ron's in the house. Canadian Maker Project. How you doing, Ron? Long time no see. So yeah, I tell you, check out his channel. We're gonna keep rolling. What else do we got today, Bruce? What else we got? We got hashtag HotMakes, baby. HotMakes. All of the HotMakes today, right? All of them. All right, ladies and gentlemen, hashtag HotMakes on Twitter. Soon to be Instagram, we hope. Let's roll the HotMakes. Roll them. All right. Today, I have to start with this one. I found this. We had him on last week. I know that here. Andrew Sink, he gave us a little heads up that he's gonna start trying to print peanut butter and jelly. And I gotta tell you, he could confirm that gyroid infill is the way to go. The tastiest of all infills. Does that surprise you, Jim? Would that have been your pick for the tastiest infill? I don't know. It would look really cool, but to be honest, I did not expect peanut butter to look that good while it's printed. I thought it was gonna be much more like oozy, you know what I mean? Well, I wanna dig in on some more details of this because we talked last week, the viscosity of whatever you're putting into a paste extruder really makes or breaks whether it works. I haven't done any, excuse me, work with any paste extruder. So for those of you who don't know what's going on here, you'll notice on Andrew's printer there in the background, if we can enhance it just a little bit, maybe get a closer look at it. He's got a little attachment called a paste extruder, which allows you to put liquid, what's the right term? Viscous fluids and stuff like in there. So he's got peanut butter in there and he's actually 3D printing with peanut butter, but we've seen this effect before with chocolate. We've seen it before with, what is it? Like sugar frosting, that kind of thing. We've seen it with cheese and crackers on Chris Riley, Chris's basement, did cheese and crackers. Cheese and crackers, we got pancake batter, obviously pancake bot, is that right? I really want one of those pancake bots. I don't think they make them anymore, but recently, within the last few months, I saw one in Chicago for a hundred bucks. I was like, man, I really just want to drive now and get this thing. I would love to make pancakes like that, but I tell you, I love peanut butter and jelly. So this is right up my alley. Well, fun fact about, this is a bit of a rabbit hole, you know, just sidetrack here, but I actually went to school with the inventor of the pancake bot. So he went to Cal Poly, same time I did too. Nice guy, Miguel, lives in New York. Is it Norway now? Yeah, I don't know. Really, really interesting guy. But anyway, let's get back to Andrew's thing. So this is the peanut butter printer and he's going to do a whole sandwich. Now he said he was going to cheat and start with, you know, pre-made base layer. So, you know, Loyal was talking about no bottom layer. Yeah, the bottom layer is the bread, obviously, but then he's going to see if he can print peanut butter and then do a jelly swap and then you got your 3D printed sandwich, right? I know we talked about him printing the dough, but it would be weird. Well, one thing at a time. One thing at a time, right? Yeah, yeah. You know, we're going to iterate to that. I know that later in his post here, he actually, someone called him out and said, let's see the other side, right? So farther down here, if I can find it, here we go. So we got the peanut butter going on. Yep. That doesn't look like gyroid, by the way. No, no, that's just your standard. Yeah. And then the jelly, huh? There it is. There it is. And then, of course, full circle, full circle. Let's push it together. I love it. I think, man, a sweet bead just hit it on the head. Cookies would be amazing. Print the dough right on a hot plate of a printer and let them just cook. Yeah, you know, there's another guy that just has seems endless energy and time to do these things. I mean, he literally was talking about this last week and then boom, he's already done it onto the next thing, right? So, nice work, Andrew. We love it. Thank you so much for coming on the show last week, too. Looking for more updates. Yeah. Yeah, Dan, he's with us tonight. Dan's our number one shoe right there. He's got a butter being made. Look at that. Yes. Yes. Just a phenomenal print that looks like is Prusa Mini that he's printing on. And I mean, that print came out just gorgeous. That's clean. Yeah, I can't wait to see this all put together. So when you get the parts kit, let us know. Approved. Yeah, yeah. All right. We got Lil' Nick now. Not Big Nick, it's Lil' Nick, right? I got it. I can tell him apart. I'm good. I mean, whoever heard of a fridge magnet making, or a fridge magnet making as an addiction, I had to talk there. Seriously, it's not a thing. So he is making fridge magnets. And I absolutely love it. Could be a thing. It's starting to be a thing. Look at that one. Look at that. Resin Lego. Yeah, can we call them Lego? I think we can call them Lego, right? Yes, we can call them. Yeah, it starts multiple times. Yeah. I love it. I mean, you can buy those kits. Why not make your own, right? You can make them say whatever you want. Exactly. I love those. It's fantastic.  Right. Pooch rules. Pooch is the greatest. Yeah, all the next day. The next day, you get a call or a text or whatever. And they say, what was that? My mom saw that, you know. And it's all and your stuff. Yeah, just a shout out, just a shout out. I'll joke it aside. He's going to be selling those in aid of special effects. So if you're interested, shoot him a DM. Yeah, thank you so much. A little Nick, he's making a bunch. So hit him up. Geeky Faye, of course. A little Nick is a she, by the way, Jim. What?  What? I was today years old when I found that out. Well, there you go. Why is it little Nick then? Little public, I think it's Nicole, little Nick. It's not Nick, it's a girl. I'm sorry, I'm sorry. Yeah, we aim to educate and inform. I didn't mean to call you out publicly, but we don't want to offend anybody either. I appreciate the heads up now. I feel bad. All right, Geeky Faye, definitely she, right? She started fixing up this poor boy who's lost his top getting printed. He's feeling smarter already. Here we go. Brains. Yes, definitely ready to audition for Frankenstein. I love it too good. I'm not familiar with that model. That's interesting. I haven't seen the model either, but I know I love what she's doing with it. We see where you're going. We see you, Faye. We see you and we like it. I like it. All right. What we got next? Barb. Oh, Barb is the queen of flex, flexi prints. Yeah. So clever model by at Abbey math. I'm Colts 3d. Evil anchor alien. Tessellation. I love a little spot. Many. There's still all about minis out there just so you know. They're not going anywhere. Yeah, no. So, so this, this is awesome. I have to let me back this up. There we go. Check this out. Oh, complete with storage case. See, who doesn't love a good tessellation, right? Yes. Just so satisfying. Watch them all snap together. Especially with the, with the storage case, right? That's functional. Yes. Form follows function. So good. You entirely floor with that. Fantastic. I love it. Nice work. I love this. I might have to find this and print it because I know some people who would absolutely love that, but I love that. She said this is her first attempt at a time lapse. This is old school time lapse. Yeah. You hit play and you just do it. I like it. That's the way to do it right there. Cut. Nice work. Jump cut. Nice work, Barb. Awesome. And, and Abbey math for the, for the. Yeah. And Abbey math. So I know in the chat there was a lot of talk about having Barb and Abbey math on the show. So we're definitely going to have to do that. They got, they got in a fight over who was more worthy to be on the show. So maybe we just pull them both on and let them debate. At the same time. Exactly. It would be our own Tesla. Who is more worthy of being here. Also, let's see. I did hear from, I did hear from Ethan's dad and Ethan and, and his dad want to be on the show coming up. So we're going to work on that too. Cause we got to get that app on the show. I got to see this. Right. I know. I know. I'm seriously twice now I've had a new iPhone in the cart just wanting to pull the trigger almost like solely for the reason of like being able to use that scanning app, but not yet. I love it. Yeah. I think Nike would say just do it. Just do it. Nike was paying for it. Fat Nike money for sure. Yeah. Amen. Right. Billy Rubin strikes again. She did some, some awesome stuff. She always does some awesome stuff, but she put together some really, really cool. Posters. She puts together the graph, like the best posters and graphics. Right. This is preparing for 3d printing. And it has like the steps and, you know, what you should do and what you should avoid and what you'll need. Right. And then of course how to, you know, present print essentially. I love this so much because Jim, let me tell you, like it is, it is so hard to convey the amount of information that it takes to, I think, you know, talk about some of these processes in a succinct way that, you know, fits and is within the attention span of somebody looking at an infographic. Right. So she's a phenomenal, she's got a good job with the design and then she does a really good job of the copywriting and the editorial. And it just makes sense. Right. Yeah. And she's a great explainer and we love Billy for it. I know, I know we talked about this before, but I mean, the bed leveling graphics she did with the first print on it, one of the best graphics to show someone new or not even someone new, just one of the best graphics and 3d printing, I think hands down. I know. It'd be great to just have up as like a nice art piece or refresher or I don't know. I just, I like it. Everybody's loving it in the chat too. So that's awesome. Next up. Holy moly. I'll let you take the name on this one. You know, we've seen, we've been through this for before. I keep saying scan. I'm sure there's like a completely botching that pronunciation. Every time. Yeah. But he's been on here a number of times and he just continues to crank out awesome stuff as well. Yeah. He's, he's experimenting and sandaling, sanding and oiling the filamentum timber fill. Yeah. So I got to, I got to just pop these pictures up right away. Look at this. So this is different shades of wood fill all the different types of timber fill. Yeah. And then sanding it and oiling it to make it look like a legit wooden ring. I mean, that's awesome. It really is. It's neat. Have you played at all with any of the wood fill filament? Uh, no, I have like, like three or four roles that I should play with, but I have not, I'm not taking the time to do that. I need to do that. I think. Uh, and, and Thomas is saying, yes. Steen. Huh. I think you got it. Steen. It's, it's, it's probably like the way that you blur it together. It's probably a little different, but Steen. Hey, if you're out there watching, um, next time I say all in my video, I highlight it or something because I see Lindsay's, um, watching and she commented oil with the laughing, crying face because I don't know why I say it oil. I don't know. When I said, I must have said like, he's been sanding an oil on it. You forget the eye. You forget there's an eye in there and say, I grew up saying it. I grew up with the oil. We're going to go change the oil in the car. Not all oil. The oil in the car. I'm not in New York. Yeah. Yeah. But she's giving me a hard time in the chat there.  The owl owl. I just oil sounds so weird to me. Oil. No, just you overthink. All right. Next. Okay. Motor oil run three from the Canadian maker project. I think he's with us tonight. Um, Get out of here. Uh, she wants me to say another word that she just dies laughing when I say, um, anyways, he put together a pretty awesome print. Um, Time out. I'm not sure. Say it. Uh, print. And say cellular. And he did a phenomenal job printing this. Um, Ron, this is awesome. I know, uh, this is one of those, you know, I've changed the filament multicolor prints and I have some really cool stuff coming up that I need to get on, uh, that someone made for us will say us because you don't know about it yet. Um, it's a surprise that I'm going to get on a show either this week or next week soon. I'm getting on a show to show you. But, um, nice work. And he used glow in the dark filament, which is phenomenal. So nice work, Ron. I love this. And if anyone out there's a ham radio operator, you can find Ron. Right there. That's his radio call sign. Uh, yeah. Never got into the ham, ham radio stuff. I didn't either. It was weird. It kept getting the radio all greasy and then I would just eat it. I just, I didn't, I didn't understand. That was for you. I don't know. The puns are coming fast and furiously today. It's good. We've been having just some great contributions. I love it. Nice work, Ron. What do we got next year? Um, oh yeah, I think, I think you might have tagged this. Um, I did take this. I love print and place model. So for, for those, I'm sure everybody's got their favorite ones out there. And for those of you who don't know what a print in place model is, this is a good example of one. So it's a single print prints all at one part, but sometimes it will fold or bend or have hinges or, or what not. And a lot of times there are great way to highlight the value of 3d printing because the geometry is such that you probably, you might not be able to fabricate this, you know, any other way. Now this is a, a pretty straightforward hinge piece. So you can see he's kind of, kind of freeing it up, but you know, when it prints flat on the bed, but, but one, he's got a print and he's got now a whole another set of dimensions that he can work with. And I think he said that the purpose of this was he was growing sprouts because his dog loves to eat sprouts. Have you ever heard of such a thing, Jim? You ever have a sprout eating animal? I have not. Um, but at the end of the video, Oh no, was it this one? Yeah. No. So I think he did post a video. Here we go. There it is. Yeah. I mean, sprouts, that's a healthy dog.  I actually, I really like bean sprouts and I know that's probably a very controversial statement to make because people, I feel like bean sprouts are like a love hate thing, right? It's either like you're into it or they're the most vile thing on the planet. But me, I put green sprouts on a sandwich. If I have the choice, you don't see it very often as a choice these days, but I think it's a good one. What do you think? I don't think I've ever had them. You've never had bean sprouts? Man.  I'm a mediterranean, you know, mediterranean. So I'll file for sprouts. Someone said, you know, I do like something. I never had those. I never had those. Oh, they were. They were alfalfa. My bad. I'm sorry. Not beans. I like all different sprouts. I would like about falafel sprouts too. So we're going to have a quail egg. A little zero. I don't know what that's called. Look at it. It's a little zero. You know, I think it's very good for our animals. We all do it. And you know what? I have a dog. Loyal Moses says our ranch dogs eat. Rabbit pack rats and small children. Yeah. See you say loyal. Just fine. It's oil with the L. That's not. That's not the same as changing the oil in your car. Well, changing the loyal in your car. Hold on a second, you have the floor. No, no, no, here's the other one. You know, highest bidder wins that one. How about that? What's the state, pull up the statement again. Pull up the phrase, the pays. I'll have to go back and find it. Maybe, maybe Geary can find it. We haven't even talked about Geary today. Geary around, Geary's been quiet today. Yeah. Geary, how do you say the words that they're trying to get me to say? Yeah, I'm trying to find the, trying to find the comment. How do you say the word OIL? Oil. That was a perfect pronunciation by a robot compared to you. Oil, I love the way that sounds. That's it, say it again. I had a good teacher. His name was Microsoft Sam. Yeah, there it is, there it is. Loyal, L-Oil just said, you have to say it, Jesus. Oh man. There it is, there it is. This is for you, Lindsey. Listen, I don't think there's any oil in the cellular towers. Okay? There's no oil in the cellular towers. Cellular? Listen, it can't be, I can't even say it. Cellular, I don't, she ever, she's probably, she's probably laughing so hard in there right now. Change the oil in the cellular tower. How do you say it really? Oil in the cellular, I can't even say it. Cellular, cellular, I can't even say that right. Oh man, the chat is going bananas, that was strange. Thank you so much, Loyal. We should just end here. Cellular retellers, yeah, the cellar retellers. It's oil, oil in the cellar, I can't even say it, dude. I'm so messed up on how it really goes. How do you say it? Cellular. Cell, cellular. Could you use it in a sentence, please? He changed the oil in the cellular tower. He changed the oil in the cellular tower. C-E-L-L-U-L-A-R. Somebody said the best $50 Loyal's ever spent. Oh man, there's gonna be like clips of yours. Here's the clip for internet, you ready? He changed the oil in the cellular tower. There you go, that's what you get. Yeah, that was a better attempt at that time. That was, all right. Moving on. Lindsay's Cellular, I can't even say it right. All right, next, what do we got next here? Oh, let's pop the hot makes back in there. Yeah, we need some hot makes. Next comes to Chris Travis, I can't even talk about this. It comes to us from Chris Travis. He is south of me as well, and he went out to Lake Geneva, I believe, and checked out some ice castles, and I had to throw this on here. It's a heck of a piano right there. Yeah, it's a piano. Let me see if I can make it larger. All I wanna do is zoom, zoom, zoom, zoom. There we go. Look at that. So they make, apparently, I never saw this before, and I think we should go check it out, Lindsay. I definitely think we should check this out. But so I love this, the ice castles. This is really neat. What's going on at the base of these ice castles is they put lighting underneath? It must be lit up underneath. Yeah, it must be lit under there. And it's like there's green back here, there's red in the front. And that's a heck of a panoramic shot. Nice work, Chris Travis. I wonder if this was done with night mode and stuff, like, because it doesn't look quite dark enough for the LED stuff to pop, or whatever lighting it is to pop. Right, I know when I was talking to him in the Twitters, I asked him when he went, and he said he went right after work on Friday last week. Cause I was like, wow, is it just like one day or what's going on? But Chris Travis says, yeah, they have lights in the ice. It looks even cooler at night. So apparently Lindsay knew about this. We got the old powered Selyer towers. Selyer towers? Oh God. I'm never gonna live this down. I love it, but nice work, Chris. I had to throw that because I love this. I'm gonna follow some ice castles too, by the way. All right, what have we got next? 3D print and paint, working on one of my favorite things, those fates and dice towers we talked about last year now, right? She absolutely made a ton of those. They are amazing models. If you haven't checked it out, check out Fates End. Fates End 6. And check out this paint job too. Wow, 3D print and paint. I mean, just some awesome, awesome paint jobs here. That's really impressive. It looks like there's lights in there. Sure does. Detail on that is crazy and hands. Can you blow that one up at all? Go back to the one with the lights. I wanna pull in on that. I wanna start. Which one? Oh, the other way. That one? Yeah, yeah. Look at that detail on the roof thatching. Yeah. Is it oil paint? It might be oil painted. Oil. It's too bad they didn't put a cellular tower on top of it too. The cellular tower right on the top. Man, you guys, I love it. It's never getting old, Jim. I love the detail in the leafs. Leafs, leaves. And the little like mossy kind of stuff. And I mean, whoever did this spent a ton of time and I mean, obviously we know who did it, but they spent a ton of time on this and it looks phenomenal. What a fun and it's functional.  Yeah. Well, in this case, I'm not sure if this is actually the dice tower. So the face ends down on the far side. Oh yeah, it is. Okay, good. So what they did because they reached such a huge goal, she made each model printable as a dice tower and then you could actually just print it as a tower too without the dice part. But yeah, definitely one of the coolest painted finished dice towers I've ever seen, especially in a long time here. So nice work, 3D print and paint. Nice work. What do we got next here? What time are we doing on time? Woo, we're getting there. All right, Yuri, 3D workbench, the splits. Skippy is ready soon, we're at 95%. If you know where this came from, what game or whatever, let us know. I don't know if Geary knows, they can pop in, but is it another cyberpunk? I know we've been seeing a lot of cyberpunk 27.7 weapons. We botched one last week, in fact, where Andrew was asking if that Malorian was from Borderlands and it was a cyberpunk. So we issued corrections. Just a quick search, yeah, it looks like it's from cyberpunk. I haven't downloaded the software update in order to play cyberpunk on myself yet, so. You play it on yourself. That's, I like the way you say cyberpunk. Say cyberpunk again for us, Yuri. Cyberpunk. Fantastic. Now, Jim, you say cyberpunk. Oh man. New segment for the show, Jim pronounces things for us. Anyway. I'll get Caleb working on a bumper. I think somebody just asked me to say, anti-establishment, what is that? Anti-establishment. Anti-established materialism or something like that, I can't. I can't read it. It's not close enough for me to actually see it, but anti-establishment, I don't know. That's close enough. We're gonna, you know what? For now on, you gotta pay to make me say it anymore. I think that's fair. I think that's fair. Don't tempt me. What was it? I said, don't tempt Moses. Right? I love it. Someone said, good job. Geary plays with himself. Yes. He plays cyberpunk right on himself. Yeah. 3D printing, 3D printing professor. Did you see this one? Did you see this one, Pooch? I had not seen this one. So, 3D printing professor printed this. He's like, holy crap, didn't think this would work, but you gotta check this out here. It's always nice when you surprise yourself with something that works. Right? Okay. Look at that. Why, I would not have thought that would work either. Yeah. There's no supports and he printed with that little surface. Well, it looks like there's a, it does look like there's a print. A shenanigans. Yeah. Still though, man. There's a lot of mass on that thing. How did that stick? Because it's not a bed slinger. So. It's true. It's not a bed slinger. Yes. The bed's not moving. It's just seriously X, Y, right? Yep. Yep. I absolutely, this picture for dramatic, I love that. I mean, it's a testament to how dialed in the printer is too.  I mean. Great. Man. And Shane said that's what happens when you level your bed right there. But I, a nice work, Joe, 3D printing professor. That is awesome. I know he took a picture. I don't know if he posted it, but he took a picture of, he made a few things like that. It was really cool. All right. In the order of time. Just to say he could. Or well, and just so it could fit on that. Cause I think if that was laying down, it wouldn't fit on the bed, right? Right. Well, I probably, I don't know. I'm not sure. But this is a Prusa MK3S chain production add-on. Dig this. Yeah. We'll just watch it for a second here. But I mean, you tell me, tell me a little more. I don't have a Prusa yet. Hopefully it's coming soon. But what do we see in here? What do we see? Yeah. Well, so basically you're just seeing a modification on that swing arm. Once the print is done, it literally flips over and shovels your part off. But more than just that arm, what you might not be noticing is there's a fan on the left side of the printer there. Actually, that's on both sides. That's blowing air to enhance it and speed up the cooling process so that it releases. Yep. And then it just goes back and prints another one. And when it's done, shovels it off into there. Now I looked at this. It's a really neat model. It was awesome that it was shared with the community. I'm not entirely certain the razor blades that he's got on there are fully necessary, but I suppose it depends on the print that you're doing. So it's blown here? Yeah. There's a lot of the stuff I print. If it's left long enough to cool off, it releases itself from the bed. So you really just need something to kind of move off, but it really depends again on what you're printing. I got to imagine that something like this is great if you're doing a lot of serial printing and you know that the model is the same every time. I don't know that I would want to entrust it for something like a print queue where you're doing different things. And you definitely want to make sure you've well tested that the arm won't bind up and cause you more grief that way. I agree. Thank you YouTube for recommending 3D printing nerd and do you recognize what this one is? It's a- Is it Evan Catelyn, right right now? So I showed my wife this, oh man it's been like a little while, it's been a couple months now, but now it's like a binge, like yesterday after I shoveled I came in and we binged up, I just let it play through whatever it's gonna let it play and it was awesome.  It was all about Evan and Catelyn. Oh it was Evan and Catelyn. It played the first one and then YouTube just kept throwing them at me and I had it up on the TV. So I just laid there and napped and watched Evan and Catelyn, it was awesome. So here we go, now this one. Exactly, the show. Matt has been tweeting me behind the scenes and I tell you what, he's been kind of giving me updates on this and he finally put it out there. This is, it's called the Rad box. It's a retraction and drying box. It's a good box. Full 48 hours of drying silica for when it's off and capable of holding nine different types of filament. Let's see, perfect to feed the MMU2 and the Ender 5 and a chameleon, but he's been working hard on this trying to put something together and getting this together. And just an awesome box that he came up with a full drying solution there. And let's see, I thought there was a bunch. That was the first, here we go. So some really cool features in here. And I think Matt's watching right now. There he is. Yeah, the Rogue Research Lab. So what are we looking at? Let us know what we're looking at right here. Looks like a slide out tray, maybe. Yeah, there's a video if you scroll down where he's kind of showing some of it, but I had commented, I'm like, this looks really awesome, but I'd like to understand more about what we're looking at and what its function is and stuff, right? So is that silica, maybe? I'm not sure. I'm not really sure. So this looks to me like a food dehydrator and the trays, and he's modded the trays, but I'm not sure what the purpose of those are. But I'm very interested in this. So I'd love to be able to talk to him more about it, or if there's a video or somewhere where there's a follow-on. Let's see. Let's see. I mean, for one, I love the colors. I love the blue, the UV reactive colors too. But yeah, I'm really curious what that tray is now. Let us know, I know he's watching. Let's see, they are cog wheels, they are cog wheel polies. Yeah, I'm pretty sure he's in the chat, there. Yeah, they wheel filament in and out. Okay, so that's how you retract. That's how it retracts, maybe? Not sure, it's hard to tell with what we got here, but like I said, I'd love to see more. If you can put a video together or something with an explainer, if you want to share it with us. Phenomenal job, I know you spent a ton of time on this, and you've just done a lot of work on this thing. I'm definitely curious to see more. Definitely curious to see more, I love it. All right, have you seen this one? No. The Chin Tu V2, waking up for a nap. It was an animated version of the seven segment sculpture characters that I've been building for a while. And I saw this and I was like, holy moly, check this out. What? What is he doing? What is, it's waking up. I don't know what it does from there, but it's really cool. I'm like, okay. So this person must make animated characters, right? Seven segment sculpture characters, and it's an animated version. So I'm really curious to see and dive a little more in about what they do, but it's really cool that, I mean, they have a little servo and it moves like a little robot. I mean, maybe Geary could dig into this a little bit and let us know what this is because it's probably some weird relation to Geary, I don't know. Yeah, they could be cousins. You know what's interesting and one of the challenges of, it's the blessing and the burden at the same time of having Twitter as your interface, right? People have a short attention span so you have to give them the nuggets in a small window, but it's really hard like when we don't have the background, like the, when you scroll up and you said this is a, what's it called, a chin-to box? Yeah, chin-to-V2. So I don't know if that's something he named or like these actual characters. Is there a community of people building? Like if you know in the chat, give us some heads up. We'd love to know more about what this is. I'm gonna give Geary, it's Geary's baby picture. That's the best comment. I love that.   Let's find out what they are and maybe we can get some more information on them for next week. Let's do it. All right, what do we got next? We got two more. We got a giveaway too. So we gotta keep rolling. This is creating an RC car with a 3D pen. So this is about a minute long and this was really cool. Basically with a 3D pen, following the drawings, the layouts and then welding it all together. Yeah, have you ever used a 3D pen, Jim? Have you tried these? I have not. I have one in my basement. It's a lot harder than it looks. It's a lot harder than it looks but this is a really good technique. That's awesome. And I really, it's like anything, right? I think that when you're this artistic and this talented that you can find all kinds of different mediums to express yourself with. But this was one of the best ones I've seen. Kind of started with the bones from a 3D pen, you know? Oh, that's awesome. Look at that. You would look at that, you would never guess that that wasn't just like a typical, a typical, you know, purchased kit, right? Yeah, yeah. I mean, yeah, I just put this together with a 3D pen. What? I love how it's just like, just tears right through the toll booth right there. Yeah. Hey, I wonder if it's like a quarter or what was that? Like whatever scale, 16th scale or something. I wonder if that toll is like 16th then. Yeah, right. So it looks like Rogue sent you a video. So maybe we'll throw it on there next week. If you can get it out for us. I'll take a look. Of that box, yeah. I'm not sure where he sent it, but I will find it and we will talk about it more next week. Follow up. Kyle Paul, last one of the day, got a buddy who wanted some programmable NFC rings, gave me free control. So I found these NF or NES controller rings on Thingiverse that can be printed with MMU for down and tomorrow I'll shave an internal pocket for the NFC tags. Look at that. I mean, dude, I would love that bling. I would just roll around with my, but you know what, here's my challenge to you. Like, first of all, I'm OG in fact, and you can't see it, but I've got my classically trained NES shirt on. So it's like, those are like my favorite, but then how cool would it be if you did a whole series, right? So you got NES, you got your Sega Genesis, your Nintendo 64, Super Nintendo, like all the different, you know. Yeah, I love it. It's cool. And it's got NFC chips in them. So they're functional as well. Right. How'd you get in the gate today, NES? You know? Yeah. Like, this finger, this finger. Yeah. Ladies and gentlemen, that was our hot makes of today. That was a good batch of hot makes. Yeah, great batch, man. I love it. Let us know in the comments below or in the chat, which one was your favorite? Which one did you like the best and why? Because we were curious. I'm curious to what you like, you know? For me, for me, it would have to be the cellular oil one. Yeah. I can't even talk. Thanks, guys. Cellar oil. I am broken. Can't even talk. All right, last, last chance, if you want to get in on the drawing for the $50 Amazon gift card, it doesn't matter where you're at. As long as Amazon delivers to you, we'll find an Amazon gift card from the country of Amazon's origin by you. Could be US, could be anywhere. I think last week was like, I don't know, Sweden or something. I was, I can't remember, but it was out there. But if you want to get in, this is your 30-second warning. Go to the link in the description of the video or that nightbot's been spamming every five minutes. I just posted it there for you for your linking pleasure. And we need your names. I want to make sure everybody's on there in the next 30 seconds, not 30 minutes. Let me just look. I'm just demanding cellular oil t-shirts. I can just say, it should be like the old school Motorola with just a dipstick on the side that you just pour oil into. Because you've got to change the oil on your cellular phone. And it'll be like, O-O-L, and then under the tower, it'll be cell, like C-E-L-Y-E-R, cellular. Cellar, O-O-L, yeah. I can't even, I can't even try to say it now right, because I'm so messed up on how I'm supposed to say it. Anyways, however you say it, that's the right word, Jim. Yeah, where are you going to rant tonight? Where are you going to rant tonight? I got a micro rant. A micro rant. What's happened to me twice? And I just need to blow off some steam on it. Well, people have time to check out the registration. Let's do a micro rant. It's a little baby rant. We'll come back and give away the $50 Amazon gift card. Ladies and gentlemen, just a little baby micro rant from Pooch today. This is a baby rant. Here's Pooch rant. I'm going to rant about this now. Where's my bump? I love it. I don't know what's going on. I think our stream has gone rogue. I think Geary needs some more oil. It's all going to get a lot of oil. I'll just do my micro rant. So listen. Did the rant not play? Did the bumper not play? No, it didn't. We just played it twice? It's OK. Did it play this time? You know when you have a rant bumper and you want it to play and it doesn't play and then it plays like way too late, like that's the worst. That's not my rant. My rant is two times now and you would think I would learn, but no, no, I haven't learned yet. I go to reheat. The first time it happened it was coffee, but I told you I'm quitting coffee, which could have some part in why I'm extra frustrated. But I put my coffee in the microwave to reheat it. And of course it's on the carousel, right? Everybody has a micro over the carousel these days, right? Can you even get a microwave without a carousel? Probably, whatever. Anyway, so it's on the carousel. It's going around and around and inevitably it always ends up at the back of the microwave. And then I walk off to go do something else. I come back like 10 minutes later and I need to heat my breakfast burrito back up and forget that the coffee's in there and I go to put the plate in there and it knocks the coffee everywhere, spills all in the inside of the microwave because it stopped in the back of the microwave. I couldn't see it. So it's like either I have to condition myself to get down and really peer in here, make sure there's nothing in the microwave ahead of time or just stop doing that. But why is my luck always, that it always ends up at the back of the microwave and then I forget about it there, huh? Is that just me? Silly rant and rant. I love it. I have a kettle. I have a kettle. Now that I'm drinking tea and stuff more, I just do, I just dump the old coffee or the old tea now and then just reheat another thing. But is it Murphy's law? Is it Murphy's law that whatever you have on the carousel ends up at the back where you can't see it? Maybe, maybe it's just me. Sure. Let me know in the chat if you have that problem because I don't think I've ever seen that. Maybe the carousel spins too fast. Well, yeah, that's not really fast because yeah, no, or not fast enough. I don't know. I love it. And of course, Lindsay, my wife is like, I'm gonna keep notes and they're talking about the words I say, right? So now that's gonna be a thing. So, you wanna know another one real quick before we do a giveaway? What is it? The thing in the bathroom that you use to go to the bathroom. What do you call that? A toilet. A loo. No, it's a toilet. A toilet. Oh, John. No, it's a toilet. A toilet? Is that it? A toilet. It's a toilet. It's an OI, it's the OI sound. You can't handle it. Someone does the turlet. The turlet is a turlet. A toilet. It's a toilet. You can't say the OI sound is what it is. Maybe that's the problem. Toilet? What's another OI word? I don't know. Let's do a giveaway. What about, say koi, koi fish. Koi fish? Koi. You can say koi. Koi, yeah. I don't know. I don't know. Ladies and gentlemen, it's about to do it. $50 gift card. I grabbed everybody. Here we go. Spend. $50. We'll see you at Thangs. We'll. Thank you so much for sponsoring this episode. If you haven't checked out Thangs. Thangs.com. Check it out. Anna. Anna. Congratulations, Anna. Anna wins. Is Anna here? Anna, say you're here. Yeah, good point. Say cellular toilet oil if you want to win your $50. $50 gift card. Jiminy Christmas. $50 gift card, thanks to Thangs.com. Let's see. Where is it, Anna? Anna, I see when she put her, I see the time she put her entry in. It was tonight just a little bit ago. Anna says, yay! There we have it. All right. Anna, congratulations. Congratulations. You've just won $50 to Amazon courtesy of Thangs. So thank them. I'm going to get your information over to Joanna.  So stay tuned for an email from Joanna for a $50 gift card from Thangs. Woo! Congratulations, Anna. Thank you all for joining us for another whirlwind episode. We learned a lot.  We got some pronunciation lessons.  I love it. I have so much fun. If you've just got here, if you didn't hear it at the beginning, you can find us by going on Twitter and hopefully soon to be Instagram, tagging anything cool you see or anything cool that you make with hashtag HotMakes. And if you actually go search for that, there's so much cool stuff. I did that, what, this weekend sometime. I actually just searched hashtag HotMakes. And you should see all of the awesome things. I think if I remember seeing the spreadsheet today, there was over 1,800 things now that have been tagged for us. And that's phenomenal. Are the things or Thangs? 1,800 Thangs. 1,800 Thangs, I like it. Thangs. Yeah, okay, good. Yeah, we're gonna have to hit up Miss Thang and tell her who this week's winner was. Thank you guys so much for coming. Coach, it's been a pleasure. The pleasure has been all mine, good sir. Geary, it's been awesome. Thank you so much for hooking us up and don't forget your homework. Geary has homework. Geary, that's awesome. I might be too drunk to do the homework. I've been taking too many screenshots. Ah, see what you did there, see what you did there. Thank you Geary for the comedy. Yeah, yeah, yeah. Well, hopefully when you're taking those screenshots, you weren't taking too many bites of your food. Ooh, come on, no? Okay. Why? Why? Why? I love it. Ladies and gentlemen, thank you guys so much for coming again. Once again, you can find us by going to Twitter, hashtag HotMakes. We're here every Monday night, six PM to most of the time, seven-ish PM. Six PM Central, four PM Pacific. Seven PM Eastern. What he said? Three AM Central European time. What was it? 11 AM in Australia. But we appreciate you guys coming out. And we will see you. Anything else you got? Anything else? No. Thanks for joining us. Keep tagging them. We will see you guys soon. All right guys, have a great week. Thank you guys so much for coming. We really, really appreciate it. We will see you next week. Later. Bye everybody. Sell your oil. I can't even. It's sell your oil. Get out of here. Later, everybody.


A Red Lake man has pleaded guilty to murder on the Red Lake Indian Reservation. 18-year-old Michael Wayne Whitefeather entered his guilty plea in U.S. District Court in St. Paul today. According to the plea, Whitefeather admitted to shooting Anthony Allen Wells in the face with a shotgun and killing him. The shooting happened near the Red Lake skate park on April 28th of this year. If you enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland PBS.


If you're like most home bakers, you've probably been in this situation.  And so far, everything's going great.  That lack of oven spring is one of the most frustrating things that can happen in sourdough baking, but it's definitely not an uncommon issue, and there's plenty of things you can do to fix it. So today, we're going to talk about that. Let's get into it. If you're new here, my name is Charlie, and on this channel, I show you how to make delicious food using simple ingredients and techniques so that you can become a more confident home cook. So let's talk sourdough. For those of you that don't know, oven spring is basically that initial rise that you see in your bread during the first 10 to 15 minutes of baking as the fermentation speeds up and the gases in the dough start to expand. So it's really important to maximize your rise during that time, because by the time those first few minutes are over, the bread heats up to the point where the yeast can no longer survive, which marks the end of the fermentation process and therefore the end of the rise. So let's get into five tips that I've picked up throughout my sourdough baking endeavors that'll help you get a better oven spring on your loafs. Keep in mind these steps aren't necessarily listed in order of importance. In fact, they're all necessary steps in getting a great oven spring. So make sure to watch the entire video so you get all the info that you need. So let's get into it. So tip number one is to make sure that your starter is nice and strong and that you add it to the dough at the proper time. It seems pretty obvious, but to get a bread that rises sufficiently, you'll first need a starter that rises sufficiently. If you're struggling with your starter, I've got a video about that too, which I'll link in the description below. But basically your starter should rise and fall within a 12 to 24 hour period. And in its maximum rise, it should be about doubled in size. If your starter is just weakly bubbling and not really rising much, then it's probably not strong enough and you'll end up with a weak rise to your bread.  So it's really important to build up that strength. But once you've built up a strong starter, the next factor you need to pay attention to is the timing. The best time to mix your starter into your dough is when it's achieved its maximum rise and it's just starting to fall, because that's when the yeast activity is going to be at its maximum. So like I said, the starter should be at least doubled in size at that point. And it should also pass the float test, which indicates that it's nice and aerated. For me, this usually happens about 12 hours after I'd mixed my Levan. But your timing will depend on your particular environment and the feeding ratios that you're using. So now let's move on to tip number two, which is the shaping. Proper shaping is crucial for developing tension on the dough's outer surface, which will help the loaf rise up rather than spreading out. So learning the shape of a high hydration dough like sourdough does take a bit of practice, but there's a few things you can do to make it a little bit easier. The first is just making sure that you have a nice large work surface with plenty of space and that you're using the proper technique, scraping the dough from the side as you rotate it 90 degrees and scrape it back toward yourself. And the next factor is proper use of flour. So you want to make sure that the top of the loaf is sufficiently floured and you can also feel free to flour your hands and your bench scraper as necessary to make sure they don't stick, but you want to leave your actual work surface and the bottom of the loaf as unfloured as possible so that it can pick up traction as you scrape it along your surface. And then the last factor is to make sure not to over shape the dough. You may think that the tighter you shape the loaf, the better it's going to end up, but it is actually possible to shape it too tightly, which will cause the surface to tear. Here you can see a loaf that I totally over-shaped right next to a well-shaped loaf and it's easy to see that the over-shaped loaf completely lost its structure. So really just a few good turns to develop some tension is all you need. You can refer to my complete sourdough bread baking tutorial, which I've also linked below, for a more in-depth walkthrough on shaping, but those are the most common issues that I've encountered. Now this leads us into tip number three, which is to make sure not to over-ferment your dough. And this is the biggest mistake that I made when I first got into sourdough, but once I figured it out, it made all of the difference in my loves. Over-fermentation basically just happens when your bulk rise phase is too long and it causes your dough to become really wet and sticky and prevents it from holding its shape properly. So not only is an over-fermented dough really hard to work with, but it also won't rise properly in the oven because the yeast activity has already peaked too early and started to decline again by the time you throw the bread in the oven. I know it's tempting to extend the bulk rise phase as you see your dough getting softer and airier, but you really just want to let it go until you start to see some bubbles forming and the dough has grown in size by about 20 to 30%. So if you're currently doing a four-hour bulk rise phase, for example, maybe try reducing it to about two and a half or three hours. And I think you'd be surprised by how big of a difference that can actually make. Now moving on to tip number four, which is to score your loaves properly. A good score will allow the steam and other gases to escape through the top of the loaf, which will lead to a nice clean rise. And it'll also give your loaf a good ear, which is that little flap of dough that springs up as it bakes and gets nice and charred. If you don't score the loaf, the gases will just try to escape wherever they can, causing your loaf to burst in unexpected places and sometimes causing big air bubbles to form inside of the loaf. So it's definitely not a step that you want to skip. Really the main two things to consider when scoring are one, to score the loaf deep enough. So I'd recommend about half an inch to one inch deep and then two, to score at a slight angle rather than directly perpendicular to the loaf. And you want to score in one smooth, swift motion. So having a sharp razor definitely helps with this. As you get more advanced, you could try out some fancier scoring patterns, but really one single slash right down the middle will make all of the difference in allowing the gases to escape properly, leading to a better oven spring. Now finally, tip number five for achieving a perfect oven spring is to create steam in your oven during the first few minutes of baking. This steam helps the crust to remain soft for longer so the loaf can rise freely at the beginning of the baking process. Commercial bakeries use special ovens that can actually inject steam inside during the first few minutes of baking. But since most of us don't have access to those, the easiest way to duplicate this effect at home is just to bake your bread inside of a Dutch oven. So as the bread starts to bake and releases steam, that steam gets trapped within the Dutch oven, creating that humid environment that we're looking for. Then after about 20 minutes of baking, we can remove the lid from the Dutch oven, which allows the crust to get nice and brown and crispy. I'd recommend this Dutch oven combo cooker, which is pretty inexpensive and the low sides make it easy to just drop the loaf in, score it, toss the lid on and throw it in your oven. But if you don't have a Dutch oven, you could also just toss a baking tray filled with water onto the bottom rack of your oven and then add some damp towels or lava rocks to slow the release of steam. Then after about 20 minutes, you can remove that tray from your oven to allow the crust to get nice and browned. So both methods work, but in my experience, the Dutch oven method does work quite a bit better and it's a lot easier too. So I hope this video helped you out and remember for a full walkthrough on my favorite sourdough baking recipe, be sure to click the video in the bottom right corner of the screen. So there you go. I'll see you all in the next one.


The Minister of Fine and Zaynab Ahmad has admitted error in the budget of the Humanitarian Ministry as a 20 or 206 billionaire captured in the budget proposal of the ministry was wrongly coded by the budget office. The minister disclosed this during an investigative hearing by the House of Representatives Committee on Appropriation on alleged insertion in the budget of the Ministry of Humanitarian Affairs and Defense. While admitting errors in the process, the minister said that 206 billionaire alleged insertion was for the National's Social Safety Net Project funded by the World Bank, which is domiciled in the project of the Ministry of Humanitarian Affairs and disaster management. I want to commend the ministry of finance, but also to clarify that that issue was not raised by the Ministry of Humanitarian, and from the explanation given, we concur totally that what has been provided is part of our project. The only mistake there was reporting, which has been clarified even before coming to this committee. And it's a project that is continuing. Equally with the Minister of Defense, I have a copy of the letter from the Minister of Finance written to the President Commander-in-Chief, it's approval to give the Minister of Defense money. We are having issues with the same committee here. Both committees in the House and the Senate, they want to remove the money. If he says he doesn't know about the approval, this is the copy of the approval from the President Commander-in-Chief. It is the same minister that we invited now to come and tell the committee about the approval. And if his committees remove that money, nobody will fault Appropriation Committee. They have the right. We invited him to come here. We would have come to the committee here and said he has discussed with the Minister of Finance, they have understand themselves. But we are hearing it from the Minister of Finance, without him here. So, permanent secretary, please tell your minister. It's not about even the Appropriation Committee inviting her. She has issues with her committees in the House. Most of the time, they invited her. She doesn't come to appear before the committee. Members were, however, not pleased with the continuous absence of the Minister of Humanitarian Affairs. Saddiad Farooq for her continued disregard for the House each time she is invited. Find out about fresh news updates.


Ani, floor to yours. First of all, I want to thank the organizers for this wonderful event, the first time in tests. It's really great, even though I raved a little late. I'm happy to talk here about some of our recent work, led by primarily, I just want to try out if, aha, OK, this works, led by my student, Konstantin, who's also here sitting in the middle. Most of the work here is really done by him and also three other students in the lab, some of them are alums now, Guillaume, who is now at EPFL, Stefan, who will join Stockholm, and Nicola, who is at Tubingen. So some of the beginning will be slightly repetitive, given the first talk of, or some, you think, talk on Monday, but, OK, first I want to go to the next slide, OK. So classical wisdom is that you should avoid fitting noise. Why is that? Well, there's the intro to ML slide, introduction to machine learning I give every year. So the ground truth here is, or the function you want to learn is the red curve, but how you're trying to learn it is by fitting the black crosses, which are noisy observations of that, coming from that true function. And so the model that you learn, say you have a very complex function class to choose from, a polynomial of high enough degree, you're able to interpolate these black crosses and you get a model that's actually far from the ground truth. The model that you learn is the blue one, which is very different than the red one. And so what you do is you add some penalization instead of just minimizing the mean square error of the training points, you want to add some rigid regularization, a penalty term that is leading you to avoid fitting noise and get a model that is much closer to the true function. OK, so that's classical wisdom, avoid fitting noise. So if we look at modern complex models, however, the picture looks a little bit differently. And so here we're looking at the test error of models of neural networks that are trained on noisy C410. So actually the data set C410, they just added artificial labor laws of 15% and they train a neural network with the first order method. And on the x-axis, you see how this test error of those models change if you increase the width, in particular beyond the interpolation threshold where you can achieve zero training error. So what you'll see in a minute or in a second are lots of curves and each curve corresponds to the test error of models taken at different epochs of this training scheme. So I want you only to focus on the very dark blue one, which is the model at convergence, the other ones we don't care about for now. So if you look at the blue one, you see this double descent phenomenon I think Yuting talked about and a lot of you are probably aware of. But what I want you to focus on today or what I will focus on today is to compare this blue line, which is the model at convergence with the model that is stopped at the optimal time for the particular width. So for each width model, you take the model that has, take the epoch where the model has the smallest test error. So now we're comparing this blue line, the model at convergence with the best model or the model at the best stopping time. So if you compare the blue and red line, you will see that close to the interpolation threshold, you actually have a huge difference between the best model or best of early stop model and the model at convergence. Whereas if you look further down for very large models, when the width is really large, you actually have harmless interpolation, which means that basically trying to avoid noise is not buying you much anymore. So this is different than in the first slide where if you try to avoid noise, you get a better generalization or a better closer to the true function. Here, this is not necessarily the case anymore. And at the same time, you also get good generalization. So this is slightly starting, startling. And we are not quite sure why this is happening. So a lot of work has been trying to understand this. And in order to understand this, we want to first move away from neural networks because they're complicated. And they are trying to minimize non-convex losses with first order methods. They're learning features, and hence they are non-convex. Overparization there is essentially the width of the hidden layers that is varied. So instead of looking at these complicated objects, we want to see if we can understand interpolation of noisy data in a much simpler setting, which is linear interpolators. So here, we have reduced the complexity from non-convex losses to convex optimization problems. And here we don't have feature learning anymore, but we look at fixed features, but we still model overparamization or the largeness of the model with a number of features. So if the features are very large, the features D, number of features D is much larger than the number of samples. We also can essentially transfer that intuition perhaps to the very large width neural networks. That's the hope. If we understand the left, the right-hand side, maybe we can transfer some of this intuition to the right-hand side. Okay, so what is known about linear interpolators or linear models in high dimensions? So there's two facts that are already known. The first one is an old truth that everybody here is familiar with, probably is if the ground truth has some simple structure, such as sparsity, ground truth here is the two-parameter W star. Then we can find the ground truth W star or close to something like W star if we introduce a strong matching inductive bias towards the structural simplicity in this case, if we try to find an estimator that is very sparse. Okay, so when we try to do this, we have some chance because we're essentially limiting the flexibility of large D to something much smaller. So we want to only focus on vectors that are as sparse so that's essentially reducing our flexibility and hence giving us a chance to learn even when we have very few samples compared to the number of features. Okay, so what is well known, if we have noiseless data and we try to interpolate with finding the minimum L1 norm that interpolates the data, we get perfect recovery even if the number of samples is much smaller than dimensionality but of the order of the sparsity S. If we have noisy observations, however, we need to again avoid noise, avoid fitting noise by adding this penalty here with the lambda that is non-zero. So if we add the right lambda as a function of the dimensionality, the sample size and the sparsity, then we get a minimax optimal estimation error rate. So now what we were motivated by was to look at noisy interpolation. So we understand that regular estimators can do very well in high dimensions like the lasso. It achieves minimax optimal rate but what happens when we enforce interpolation of noisy data? So for that, we have relatively little intuition still for the L1 case, in particular, when can we get asymptotic consistency and can we get asymptotic consistency here? We'll talk about that in a second. So this is the first coming from the old perspective. This is one way to think about noise interpolation, why this could work well. And a lot of work has been done recently for another interpolator on noisy data which is not the minimum L1 norm but the minimum L2 norm. There's also been some work on L1, I'll get to that later. But there's been a lot of work on this and there's one intuition that is basically a big takeaway is the reason why we could perhaps have harmless interpolation is that when D is much larger than N, the variance decreases and hence if I compare minimum L1, sorry, minimum L2 norm of interpolation with rich regularization, which is essentially just minimizing the y minus xw squared norm plus some w times the two norm squared, then you actually get a very similar generalization performance. Hence basically regularization doesn't really help you. Like avoiding to fit noise doesn't really help because the variance is very small for the interpolator already. But here's a caveat of this line of work which is that the estimation error is actually lower bounded by some constant that's bigger than zero even in the asymptotic limit where N goes to infinity or D and N go to infinity. So both in the regime where D over N goes to constant and in the regime where D over N to the beta goes to constant where beta is bigger one so D is bigger than N and you have, beyond the interpolation threshold, you get an error that is non-vanishing. So in some sense you can say you have a bad generalization, right? So even if you have a very sparse ground truth, the L2 norm interpolator is not going to be able to take advantage of it and help you to get a good estimation error. There is one exception or maybe you can think about good generalization when you think about the prediction error performance. In particular when your covariates have a low dimensional intrinsic structure if they're following a spike covariance then you can have both low variance and low bias leading to a vanishing prediction error. So this is an exception but let's go back to the question of having a small estimation error or good generalization and a harmless interpolation. Is it possible? So we saw from the old perspective you can have good generalization if you regularize and from the newer perspective, newer line of work you can see that you can have noise interpolation. However, it's in a regime where the generalization is not necessarily good. So is it possible to get both, the best of both worlds? If you have a structured ground truth but you have unstructured access, for example isotropic gas suits. So we want throughout this talk to focus on minimum LP norm interpolators where P varies essentially the inductive bias towards varsity. And so we looked at P equal to two as bad generalization, high mean square error. However, we have harmless interpolation. Doesn't matter if you do rich or if you do interpolation then we have P equal to one where you have very good regularized estimators. Now the question is, can we have LP norm interpolator that is also close to the minimax optimal rate of the regularized estimator for P in between one and two? This is the goal of today. Yes, there's a question? No. The somehow the host would like to mute me. Okay, so how do I, what? Okay, this is the right. I'm good, okay. Okay, good. Gladly I don't have to be muted for the rest. Okay, so this is the objects that we want to study today. And what we're curious about is, can we get good interpolation interpolators with good generalization error that are close to the best regularized estimator we know, like the lasso? And so for this purpose, of course, we need to assume something. If we don't assume anything about the ground truth, there's no hope. So for simplicity here, we're going to focus on one sparse ground truth, but the results hold for general, most of the results holds for general as sparse ground truth. But just for simplicity of the presentation, we will consider 10000 as the ground truth. Of course, we don't know the location of that particular one. So it's one sparse, but we don't know where. So then I'll use the following language. I'll say that estimators have a weak or no inductive bias if they're essentially encouraging only a small L2 norm, which essentially doesn't give you any kind of sense of directionality. So all directions are equally good. And in contrast to that, we have estimators with strong inductive bias, which are essentially the ones where you have encouraging a small L1 norm. So p equal to one is strong inductive bias and p equal to two is weak to no inductive bias towards the two simple structure, which is sparse ground truth. And then our results will also hold for specifically Gaussian covariance, X, where the rows of this data matrix are IID Gaussian. Isotropic and we also have IID noise as opposed to, for example, adversarial vanishing noise. Also Gaussian. So here, the oceanity is quite important. Since one of the tools we use is essentially the convex Gaussian-Mimmax theorem. Here, some of you are very familiar with that or actually the founders of this. And also we'll look at the regime where D is of the order n to the beta with beta bigger than one. So this is a regime where we can hope for consistency. And this is not the inconsistent regime that Yuting looked at on Monday, where D over n goes to gamma. So we have a chance that asymptotically our error vanishes. Then we look at the prediction metric, the performance metric, which is the prediction error. In this case, visotropic Gaussian is just equal to the mean square error. So it's the Euclidean distance between my interpolated W hat and the ground truth W star. Okay, so we already discussed that for P equal to two, you don't have much hope because you don't really have any guidance towards the right kind of solution if your D is much larger than the number of samples. So you have high mean square error. What we also know is that variance is actually quite small because harmless interpolation. However, you have a very high statistical bias because you don't know where to search, even if you are noiseless. Okay, so then the question is what happens in between P equal to one and P equal to two or even for P equal to one. So the first result I want to present is a slow rate for P equal to one. So we had a constant rate for P equal to two, essentially, but for P equal to one, we do not get much better. So here, before our result, there were two competing lower and upper bounds. So the lower bound was saying, you should get something like one over log D over N, which is roughly like one over log N. And the upper bounds, I'm claiming that we have some sort of constant upper bound similar to the P equal to two. So the question was, which one was not tight? And so we basically proved that lower bound is tight and you get a tight bound up to leading constant factors. You have a sigma squared, which is the noise over log D over N, which is essentially the rate, plus some higher order terms that vanish faster. And so what happens if we plug in D of the order N to the beta? So it's a bit easier to parse. If we plug that in, you get a rate of one over log N, essentially. And so if you compare that with Lasso, the regular estimator, there is really no harmless interpolation, right? So forcing to interpolate noise with the min or one norm interpolator gets you one over log N versus a really fast rate of one over N. So here interpolation is doing a lot of harm. So now in order to, before we try to understand interpolation, let's just go back to what the regularization is helping us to do. Why do we get such good performance with regularization? Well, if I add this lambda term, I can trade off bias and variance, right? So if I choose a very high lambda, I really avoid fitting noise well. So my variance goes down. But the problem is I have a high structural bias and the opposite. So when lambda is very small, which is essentially the limit of lambda to zero is the minimum of one norm interpolator, I have very high variance, even though my structural bias is small. So when I'm looking at the Lasso, I can actually pick this optimal lambda. But when I'm looking at interpolators, I cannot pick lambda anymore. I don't have flexibility. So I'm stuck with this lambda close to zero picture. So this is my picture. For p equal to one, I have high variance and even though I have a small statistic of bias. So I know where to search, but my noise is still screwing me up essentially. And it's screwing up much more than for p equal to two. So if you compare the two, both have high MSEs, both have high errors, but the reasons are different. For p equal to one, it's because of how variance and for p equal to two is because of high bias. Okay, so intuitively, why do I have much higher variance for p equal to one? So just if you think about it, think of W star is equal to zero. So you're just fitting the ground truth. And the variance is essentially the norm of the vector that is fitting the noise. So in that case, when you are looking at minimal two norm interpolators of noise, you can search in all directions. You can use all directions to fit your noise. Hence, the norm that you need could be much smaller than in the p equal to one case where you're essentially confining your search space to sparse vectors where you then potentially have to use a bigger norm vector to fit your noise perfectly. This roughly is like the effect of smaller flexibility here creates actually a higher variance, which is the opposite to when you look at it, think about regular estimators. That leads to higher variance because you have less flexibility to fit the noise. So that's an interesting intuition to keep in mind. Okay, so this also happens in practice when we try to look at sensitivity to noise. So here, we just ran an experiment, fixed the dimensionality and the number of samples and just increased noise and looked at the risk or the MSE of the minimum L1 versus minimum L2 norm interpolators. And you can see if you increase noise, then the risk or the MSE increases much faster for L1 than for L2. Okay, and actually you can also see between p equal to one and two, you can see a trade-off. Now just ignore the blue line for a moment. You can see between p equal to one here and p equal to two here, the bias goes up. So for p equal to two, you don't have any clue. But the variance goes down and it leads to this actually U-shaped curve as a function of p, not as a function of lambda or model complexity that you're usually used to. So essentially we're having a new kind of bias variance trade-off that occurs for interpolators as a function of the strength of inductive bias. So maybe if we pick some p in between, you will get a better rate than the minimum L1 or L2 norm interpolator. And this is exactly the question for the rest of the talk. So we have these two pretty slow rates but what happens in between? Can we actually get rates that are close to the minimax-optima rate or to the lasso? And this is basically the next result that I want to present. Still want to go into the details for now but we can basically prove polynomial rates. So one over n to the alpha and how do these rates look like? They depend on the beta, right? So the regime D is of the order n to the beta. They will depend on beta and the rates will of course depend on p. So instead of showing you a bunch of formulas, we're gonna plot the rates here. So I just wanna go through this part really slowly. On the y-axis, we have the rate exponent. So the final rate that we interested in. What we really want is a fast rate which is alpha equal to one, which would be the rate one over n. But what we have for p equal to one and two is essentially a rate that is constant up to log factor. So the one over log n, we also treat it as essentially a constant rate. So we have these two are known, these two horizontal lines. And in between, we are plotting essentially the rate for different p's and betas. So this is the degree of overparization. Everything is in the D bigger than n regime. So we have, we can interpolate the data. And so what I want you to look at here are just two things that goes through that plot but slowly, so let's pick some fixed regime. Betas equal to close to three. And let's look at the rates for different p's. Okay, so these are lines for different p's. So we just pick a p, for example, 1.2. That gives us a rate that's pretty slow. But if we decrease this p for here, 1.1, and decrease it further to 1.01, then we get a rate that's really close to one over n. Okay, and it gets, it's a very close rate to minimax optimum. And if we look at beta close to two, it actually gets a rate really, really close to one over n. Arbitrarily close, if we let n large enough. Okay, so I just want to note that these techniques also apply to classification. Actually, I'll show you in the next slide. And also allows an extension to nonisotropic Gaussians and general S bars, W stars. But just to look at classification, this same kind of picture here. But the difference is that actually we get exactly one over n for a large fraction of betas here. For a large range of betas, if we pick p equal to 1.01, we actually get a minimax optimal rate. So just to take a step back, what did we just show? We showed that we can get very good generalization compared to the best regular estimator. We basically can match that for certain regimes and certain choices of p that are in between zero and two. And hence, we also get harmless interpolation because we are essentially matching the regular estimator, the best one, which is the lasso. Okay, for classification, I also have a couple of experiments on synthetic data and also on real data where both essentially the takeaway is that the best noisy, so in the noisy case, sigma is nonzero for the orange or the gray line, the best p interpolator is in between one and two, whereas for the noiseless data, the best p is p equal to one. Is that one? So here, basically, see this difference between noisy interpolation and noiseless interpolation. So once you have noise, you want to go away from one to attenuate the noise better. Okay, so if not too much time left, so I just want to summarize what I've been telling you today. So one major point was a new intuition for interpolators of noisy data as a function of the inductive bias strength as opposed to model complexity, as is known from classical statistics. So here we have the new bias variance trade-off as a function of p, which is essentially our proxy for inductive bias strengths. And we proved some new bounds for regression classification that showed that if you choose p in between, you can actually get a very good estimation error rate, which is close to the minimax optimal rate, and for classification even equal to, for very large DNN. So the follow-up question is, of course, how does this intuition, or does this intuition, transfer to the original first slide or second slide on newer networks? So there's lots to be done there to transfer that intuition back, and we're working on a few experiments to see if we can validate this in any way. But there's lots more to be done. I'm happy to chat with you if you have any questions after the talks in the break and also otherwise. Thanks a lot for your attention. Okay, the floor is open for questions. Thank you for this great talk. My question is the following. Before trying to move to newer networks, which is pretty hard, do you know if some of your conclusions extend to the fact where you have a mismatch, so when the model is, let's say non-linear, but not necessarily a neural network, something easier, but you're trying to fit with a linear model like this? Yeah, so we actually had an experiment with CNT case, so it's a kernel, and there we could see a phenomenon similar in that sense that basically you also have these curves where in the noisy case, it's a different best interpolated in the noiseless case. So that we see, but question is always if you move to this direction, what is a reasonable strength of inductive bias here? Of course you can think of sparsely also in the kernel, maybe regime, but then beyond that it gets very hard to think about a reasonable inductive bias. So I was curious, I mean you took a special case, this signal with a sparsity pattern with just one non-zero component. Do things extend in the regime or you would have a linear fraction of components? So I think, yeah, so this is a very good question and we think it would be quite hard to show the same. So with the same techniques, we're not sure we can do that. Yeah, so for now it's just a constant sparsity then, yeah. Okay, but do you think it extends or you don't know and it's a... Yeah, I can't say, yeah. Okay, thanks. More questions. Oh, I'll ask one in the meanwhile. Anything special about beta equal to two? The place, so do you have a point where you do reach the minimax optimal rate? Yes. Anything special about that? Well, that's... Why for beta equal to two and then not beta? So do you have any intuition of about why that's the optimal beta? Or maybe it's just a coincidence. Well, there's essentially in... Okay, so there's essentially this lower bound, universe lower bound for all interpolators. So that is actually matching this line going down. So only at that point, be it equal to two, can you even get a one over N rate? So you have N over D. So if D is... So if beta is smaller than one, you cannot get one over N anyway. So that's the universe lower bound. After that, you have a chance. So... I was wondering how come this goes... Ah, yeah. Yeah, yeah, so there's... Yeah, it's basically the variance here that has to go down first. And the curves are tight here. Yeah, right. Thank you very much. Another question? You have a question? Yeah, quick. Okay, thank you, Fanny. So yeah, just a clarification. All these results were for isotropic matrices, right? Yes, yes. Could you comment a little bit on what happens with structured covariance? So if you have a spike covariance, we don't have the result yet, but we believe that the way we're proving it, you'll still be able to get similar results with a CDMT. As far as I know, even for minimum L2 norm in classification, using this type of analysis, I don't know if there is results correct me if I'm wrong with structured covariance. Maybe you have, so that's a question. No, we don't have structured covariance yet. Yeah. But yeah, ongoing work. Thanks. Okay, so let's thank Fanny again.