They FINALLY did it! After months of asking, Master Nool has finally added animations for all three Egyptian God cards and to make the wait worth it, they also included additional animations for the alternate art versions of these cards. So to start things off, I wanted to do my personal favorite God card, Slyther. Now I'm not gonna lie, up here in Master Rank, I wasn't sure how I was gonna get a Slyther deck to go over well at all. So I started with some research on possible engines to turbo them out, which led me to this deck list here from user Ugly on Master Nool Meta. It actually fits Slyther really nicely into the Labyrinth Arc type, and because I haven't used Labyrinth before, I pretty much used his list one to one. So if you want to try his deck down below, it'll be in the comments. But because I wasn't familiar with Labyrinth before we get into any live rank duels, let's take a look at one of my warm-ups just so you guys can familiarize yourself with the deck, what we're going for, and how we're going to try and get Slyther out on the field. So first things first, you're gonna see in our hand, we got a good one. We've got Slyther in the hand, which basically we just want to discard him at all times. Sometimes, maybe sometimes there could be boards where you can summon him. But most of the time we're gonna special summon him from the grave, you'll see how later on.  We're popping cards in the hand! Gimme that! Gimme that! Obviously, this is just gonna help us compete against Meta Dex, maybe stop a few plays in the making, set a few cards face down. Overall, not a lot big here to this Slyther strategy, but again, this is really helping our boards stay afloat so we get Lady Labyrinth. Oh, let me pause to clarify this for a second. Dotto, when did you learn Labyrinth? I didn't. This is my first duel with the deck. I, uh, yeah, I'm bad with Labyrinth, so Labyrinth players, if you see any mistakes, please let me know, I'd love to learn the deck, even if it just helps me learn how to beat it. Okay, back to the Slyther strategy. A main reason why we're running Labyrinth with Slyther, as the deck instructor said, is that Lady Labyrinth is gonna let us get any card, any trap card from our deck and just set it. Oh yeah, I forgot, I played this defensively, I'm trash. But, that is fine, because I activated a normal trap card, triggering Lady Labyrinth's effect to search my deck for another trap card, and what do you know, we're gonna grab the Revived Sky God. This card is huge in the Slyther strategy, or this deck specifically, because it allows us to special summon one Slyther from the grave, it can't be negated, and each player draws until they have six. Obviously, that's gonna boost Slyther up. The rest of this effect, I don't run Monster Reborn, but I think you could make a case to put it in if you wanna try it out, so give it a shot. Anyway, Revived Sky God, this is when this guy should know that something's up, and he's not dueling somebody average. We're gonna skip ahead here, because I think that's about all you need to know for the Slyther strategy, up until we get furniture to actually discard Slyther. They're gonna attack directly, do their thing, yada yada, nothing big here. And here, help me out, let's skip ahead until something good happens. A few moments later. Alright, so we didn't show up, but I activated Pot of Prosperity to hit some cards, and also I run an extra deck that's nothing but filler, don't really worry about the extra deck. Just throw in whatever you have to get the cards right. Now, I greeted for a Chandelier, because it's allowing me to discard a card, and you already know what I'm trying to do here, brah. I'm using the furniture to get Slyther in the gray. Genius idea, allowing us to compete. Now, we can't activate the Revived Sky God just yet, but that's fine. We're gonna activate it on our opponent's turn, coming up here soon, so we're just doing some Labyrinth furniture stuff. I think I'm doing it right. I probably am not. I've never played this deck, but boom! Give me that Stri-Striker Ace, baby! Huge hit, we love to see the Labyrinth RNG. Anyway, we've got a whole field of trap cards now. Man, this deck is crazy. And now, we've got a board where we could tribute Slyther, if we really want. Not sure how often it's gonna happen, but the Revived Sky God comes into play! Ugh! 6,000 attack! It's insane! Man, if I had the animations on, you would have seen it. I'll just edit that in, I guess. But Slyther is looking mighty good here. Now, they also added in attack animations. Like I said in the intro, spoiler warning, I'm not gonna get it with this Slyther. Boom, he's dead. So, now that Slyther's gone, we still have to compete, and I was already pretty sad, so I'm still competing anyway. Huge Rygeki. This is gonna even out the duel pretty bad for me. Honestly, I thought I was gonna lose at this point. I was just learning the deck. Later.  I started throwing a bit. Okay, I know this is BM, guys, but hear me out. I really wanted to see the animation, okay? And then look, I paid for it, okay? I got super polly with three of my monsters into Preda Plant and whatever. I've never seen that guy, but yeah, we do have a way out of that. But listen, I deserve to lose at this point. I really did, which is why it pains me to say that I don't. You know I'm a YouTuber. I'm not showing a replay where I lose, but how do I win? That's the question. Do I get the Slyther animation? We run two Slyther's in the deck. Pot of extravagant, seven cards left. Yeah, you already know I don't hate him. Seven cards, one Slyther, I'm not doing that. One in seven odds, too good. So once again, I am planning to just straight up summon Slyther and I'm throwing. I said a monster that I could have used to attack, but I don't want to win with just a monster, but we've got two Ash Blossoms, a Maxi if I'm trying to lose. Draw, we got him, baby. Slyther in the flesh, the alternate R. I bought the pack. I was so hyped. Tribute my three pieces of furniture, 6K.  It's unfortunate. We didn't get to see the attack animation, but I actually like that better. Maybe we'll get to see it together in the video. So let's get into the game and learn this deck together. All right, here we are with Slyther's lab. Once again, let me just show the deck here. All the pieces of furniture. Again, thank you to the deck creator up on screen right now. Appreciate it, great idea.  I'm a copycat, but I want to see these animations. Let's eat. Ooh, first coin flip of the video and it's not going to go my way. Second it is. Now that being said, can we actually get Lady Labyrinth out on the first turn? I think it's a, oh no. He's a fellow Slyther enjoyer. He's a fellow Slyther, Maxi and Cyframe Gamma. Okay, dude. Okay. That's quite the opening hand for going second. Let me see what you got. Let me see what you got. Cyframe Gamma again is just a monster effect in the gate. Also, fins out our deck a little bit. I'm not a big fan of using Cyframe Gamma. I might take him out of the deck myself, but whoa, Vanquish Soul. I'm going to Maxi this real quick or should I have started with Cyframe Gamma? Yeah, probably should have hit the Cyframe Gamma first.  That's what I'm thinking, you know? That's what I'm thinking. That way we get a draw. Look, he gets Dust Devil. I'm not worried about that. I'm a little worried about that. Stake your soul. Now that is the one I was worried about. That's the one I was worried about. Ooh, he's going for Borger. He's going for Borger. I don't want to discard this just yet. Now he's going to go for the draw and that's when I'm going to Cyframe Gamma. We're just going to try to stop him in his tracks. I don't want him drawing. Sorry, bro. We're not going to let you do that. We're not going to let you do that. Cyframe Gamma is going to stop you. You can have the Dust Devil, but you can't have generic draw. Oh my God, he's stumped. Oh, that was sick.  That was very, very clever. God, Vanquish Soul is so sick. I never even thought of that. Good stuff. Good stuff. I got to give it up. Well, let's send our deck out a little bit anyway. Oh, it's still got negated. Never mind. Okay, I don't know how your gear works, but that was still pretty smart, I guess. I mean, I don't know, but I liked it. I liked the play. I like the play. Unfortunately for him, all that means is he lost his guy. Is that a special summon I see? Hold on, Rock of the Vanquisher. Let me go ahead and get my one maxi. Oh, Slyther the Sky Dragon, you don't say. All right, now I'm going to go ahead and torb it up. I'm going to go ahead and torb it up. Let me get Slyther out of here. Hopefully no call by on the Slyther. I don't think anybody would call by the Slyther though. All right, he's going with the Recycle on Mad Love. I don't necessarily mind that. We know he has another Mad Love anyway. I would have actually preferred I got the draw, but I know Vanquish Soul player, bro. He's trying to wait till my turn to play. Anyway, let's just grab the big welcome labyrinth here. All right, side frames are going to ditch. I appreciate it, boys. They were here for a good time, not a long time. And with the turn changing, it is now our turn. We know for a fact that's Dust Devil. I mean, we could just pretty much assume. Now we're going to cook though. Ariana hits the field, face up attack to activate the effect. I don't think they Dust Devil now. They might Rock of the Vanquisher though. The good news is Ariana has such high defense that we should be okay. You know what? We are going to big welcome labyrinth here just to see if we can't pop that card immediately. Max C, unfortunate Max C. They are going to get some draws here. That's fine. Let's get our lovely lady out front. Lovely lady of the silver. All right, Ariana, hit the bricks, Ariana.  All right, and we're just going to add a cuckoo clock. Oh, you know what?  Why not? Torbi effect is going to go off so we get an immediate, immediate access. And then Mad Love's going to do her thing. But you know what? We're just going to cuckoo clock real quick early on in the chain. And then we're going to Torbi again in our hands. The question is, what do we want to ditch? I kind of forgot we'd have to do that. We'll just ditch Ariana. We're ditching Ariana for Torbi, putting ourselves in a little bit of a predicament here. This is greedy, I would say. This is probably greedy. We could probably get called by or something. Again, labyrinth players, let me know if I'm risking my life out here. Big welcome labyrinth on board. All right, now he gets a draw for Max C. We get to pop a card. Yes, please. Oh, we get the borgger. I'll take it. I'm adding cuckoo clock to my hand. I'm not really trying to do all that. And let me go ahead and just get one of these trap cards in my grave here real quick. Why not? I mean, why not, right? Why not just set this real quick? And you know, let's keep that same energy. Let's set these two. And then this is going to get flipped face down if we're being honest. So if we're being honest, we just got to swing in anyway and just see what happens. Yep, there it is. The dust devil comes out, not a huge deal. You know, I wonder if this works. I guess it really doesn't matter. It would just flip our face down anyway. All right, so I guess I'm saving destructive Daruma for the next turn. So that maybe we can get rid of rock of the vanquisher. Yeah, I guess we just ended here. Dimensional barrier, not really going to be a huge for this duel. Anquish soul kind of gets around this. Sky striker gets around this. Dimensional barrier, amazing card. Really strong card. Unfortunately, not strong in this matchup. All right, we're just going big welcome labyrinth right from the get go here. Why not? And I'm using it to bring out lady labyrinth. We're doing it. Obviously would have been a lot better if lovely labyrinth wasn't face down. In fact, I'm having some doubts, having some doubts, but I will use Torbi in the graveyard. I guess we're going Torbi for Torbi. Torbi in the hand is worth a Torbi in the grave. That's freelo. And then we could just, we could just do these two again. The key is I really just want to get Slyther, man. All I want is Slyther. We already accomplished step one of getting him in the graveyard, but life is looking a little rough for us right now. Do I just do it now? All right, yeah, let's do it. Let's do the labyrinth coup clock into the Torbi play. Let's just get these cards a cycling. Let's cycle it up. I think I'm doing this right. If not, this is very embarrassing. All right, we're setting a welcome labyrinth. Why not get that down on the field coup clock? I don't know. I don't know what's happening. Welcome labyrinth. Yes, dude, in the graveyard.  Come on back for me. I'm just going to bring, I'm bringing coup clock back to my hand. I'm not trying to get greedy out here. All right, let's not do that. Let's get coup clock back in the hand. Great looking clock. Appreciate it. And here comes the Borger summon. We knew this was happening. He's going to bounce back a mad love. Okay, so we are going to lose the 500. I'm just going to do this real quick to try to get my Slyther God card. Here we go, baby lady labyrinth. I don't care what happens as long as he doesn't harpies feather duster me. I do not care what happens from this point forward. The revived Sky God. Tell me, are you willing to see a God duel? Oh yeah, and I get to special summon something. I forgot about this. Ariana, hit the field. Nah, I can't believe God's a woman for real. Shout out to Ariana. That's crazy. Okay, it's at this point where I'm just going to go ahead and flip destructive Daruma. Yeah, he's going to get the draw, which is a had been unfortunate, but it is what it is. And but hey, it is what it is. He used his normal summons. So destructive Daruma, flip this over. Daruma, hit the bricks, kids. Hit the bricks. Everybody face down defense. Here we go. Here we go. Bye bye rock of the vanquisher. Bye bye any plays.  Your special summons are gone. And you know what? I'm going to add my labyrinth labyrinth. I'm adding it. I don't care. Is it optimal? I have no idea.  I forgot he could flip summon.  We should be okay. I don't know how many attributes he's got. Okay, linking in the rock of the vanquisher. He already used the effect though. So are we scared of that? Oh, I'm not scared of that. I don't think I don't think I'm scared of that. God, if this included link summoning, man, I'd be so happy. And he already has six too. This is literally crazy.  I can feel it. Oh, God. Why can't I activate this?  He's got too many cards in his hands, bros. This is the worst day ever. I can't believe it.  I don't care. But actually we should probably, oh God.  We got to bait it out. We've got to bait it out. We've got to bait it out. We're going to bait it out, I think. We've got to hit the right card here. Okay, that was just continue. I don't really care about that. Get a card out of his hand.  Oh yeah. Oh yeah. Sure. Yes. I'm confused, bro. I'm confused. I'm not going to lie. I just don't want to botch this. Please tell me this is your face down trap card that destroys all monsters. And please tell me you plan on activating it. Like right now. Destroy a card in his hand. Dimension shifter. I don't run that card. I think that card is not great in Vanquish Soul. I mean, if you get it first turn, amazing. But why we bricking, bro? Please activate it. Please activate it. Oh no. Hell no, bro. God, this could just go so poorly, so quick. It's actually crazy. This could go so poorly, so fast. It's actually crazy. But he's going to add one to his hand. One, two, three, four, five. I can't let him add it. This is actually crazy. I have to activate this now. The revived Sky God. Tell me you average Vanquish Soul do. Oh, I forgot to chain Lady Labyrinth. Damn it. Tell me, what will you do against a God? Oh, Slyther the Sky Dragon. I had to activate it before he got six cards in his hand. I couldn't let that slide.  Anyway, I'm just going to play Ku Klok. We're going crazy, boys. We're going crazy today. I hope you like playing your cards in defense mode, man. Actually, I think they have a lot of ways to get around. Anyway, I hope you like setting your monsters. Do I want to ditch chandelier? Yeah, why not? We're just dropping Slyther's attack points to 4,000. I mean, it's okay. Oh, I have to drop it even more. Ooh, I mean, I'll ditch Slyther him again. Ooh, okay, Slyther's a lot less impressive now. A lot less impressive. But listen, it's not the size of the Slyther that matters. It's how you use it. Resolve. Big welcome Labyrinth hits the board. Love to see that. Ku Klok reset me. I'm a 2,000 attack point dragon. I've dropped this way too low. Okay, I'm getting a little crazy now. I'm getting a little scared. I'm getting a little scared. I'm not going to lie. We're welcome, Labyrinth thing. I'm also doing that just so I can make up for my former mistake. Lady Labyrinth, get me a trap card back. Bit of a long chain here, but that's fine. Let's get a, probably a little imperme over here. I don't know why I set it in that far away of a column, but whatever. Special summon from the deck. I don't know if I want to do you just yet. I mean, hey, let's go for a game. Why not? Let's go for a game. Why not? Oh my God, I dropped Slyther to 1,000 accidentally. Oh my God, I accidentally dropped Slyther to actually just 1,000. Hey, did I say 1,000, baby? I meant 2,000. Let's go. Thankfully it's going to be 1,000 when I immediately maxi next turn, but whatever dude. You know what? I'm going to get revived Sky God here just in case things go really bad. If things go really awful, we have revived Sky God back. Battle phase, hurry. Battle phase. All right, 1,600. Oh, we can only attack with the most effective timer. This is why I don't, I can't play decks like this, bro, and learn on the fly. Boom. I guess you don't have all the things you need. 2,500. No way you got that defense, bro. No way. 1,500. You're weak. You're weak. 1,600 to the face. 1,600. Oh, I forgot. I forgot. I forgot Rock of the Bank which was there. My bad, my bad, my bad.  That's dark. That's fire. That's ins. That's all you got, brother. And I saw that Kaiju too. Don't think I didn't see that Kaiju, brother. Don't think I didn't see that Kaiju. The key is how are you gonna stop me? Oh, God, did he just stop me? No, no. It's because the timer, man, I'm sweating. I'm sweating. Get me out. Oh, Slyther, that was sick. God, let me see this animation, God, please. All right, Slyther hits the deck. Bye-bye, Slyther. Now, the key is do I let him play here or do I just go nasty? Do I just revive the sky, God, so he can't even play? I kinda wanna see the animation, but I kinda don't wanna see him play.  Reinforcement of the army. What will that do against a God? The revived sky, God. Slyther, the sky dragon, ah, he's so sick.  Hey, let's both draw till we have six. Oh yeah. That means a lot more for me than it does you. Boom, two ash blossoms. They should've let me ash blossom that. You gotta work on your chain link system. Six K Slyther beast.   I paid my crimes already Hey, hey, why are we kaiju and her man? Oh that was so childish Hey, can you play against life for though? You just did that so I couldn't get my car back from the graveyard, huh?   No, bro. Hell.  You're not I have two ash blossoms in my hand because we both drew Do we have six absolutely not you don't get to play the game against the god? Okay, that was pretty smart. Damn. Banquet souls are too good at the game. I Should have returned one of my own monsters now that I'm thinking about it. Oh, I'm just gonna imperm you anyway, haha I'm not letting you add anything man. I listen. This is horrible, bro I'm sorry, man.  Nope slifer. No. Yes Yes, please okay. No no more effects. I'm stopping Yeah, yeah, you can come out please wait. I can't lose the time. I'm turning this off.  That's off from now. He can play whatever he wants to play. I can't play the game no more I'm sorry.  Oh god. Oh god. Oh god. Oh god. No 2000 2500 more like 2000 bro. What do you got? Okay, I'm just too afraid to play the game. I'm too afraid to play the game It's the end phase. So I'm turning this back on I'm gonna get my I'm gonna get my refresh a slifer return for your grave I appreciate it old friend. Oh god. Goodbye slifer. Goodbye.  Oh stop. All right. We've got some time left on the board. I'm feeling good I'm feeling alive. Let's see what we got here. All right. Why not? Let's big welcome labyrinth just to get his 2500 back in the hand and yeah, why not? Why not? I'll Ariana. Why not?  All right. I'm just gonna attack Let's let's how about we don't how about we don't let's let's not be doing all this Let's just not out think ourselves and out time ourselves.  Yeah, that's good. Yeah, 2500 is good. Yep I did give you that, huh? There goes my lady labyrinth, which means slifer is gone add another 15 on board labyrinth destroy Bada boom Bada Bing.  You literally gave me that monster if you didn't want me to have it Why were you so childish as to give it to me? Anyway, 3k see in the next dual buddy Slyper and the labyrinths take their first dub or I guess technically second dub since we showed the replay I misplayed my way Into a pretty bad situation there with the clock, but hey pretty good first dual. All right, but we're back We've got ash blossom in our hand. We're going first. We have ariana. This could be the move We do also have the revive sky god So equally interesting obviously we don't want to draw into this we could just get this later I'd rather draw into Slyper for the discard. All right, so we're just gonna go ahead and get Ku Klok back out here Actually, this was a misplay Damn Damn, we've just got to set our two trap cards and hope this is good enough interruption All ash blossom to that. Alright, so we stopped chaos space with ash blossom I don't really know if that's the move to be honest, but you know, we're just gonna feel it out This is probably dragon link and if this is probably dragon link I actually oh if they discard. Okay, that's that's fine. I mean that's sad for me I mean that's a pot of greed for them go off bro go off. Hopefully dimensional Barry can win us this game I think we just go for what synchro. I don't know what to do. What do you do? What are you dimensional Barry here? I think we just we just dogmatic of this guy so we can't link away. Yeah, I'm crazy. What of it? Is this misplaying? I think so anyway cross sheep I really probably should have put some higher. Oh no. Oh draw card. Absolutely Absolutely, give me that. All right, we got big welcome labyrinth so we can actually set this now. So that's pretty good All right, but we don't care. I'm labyrinth Ku Kloking right now That's gonna allow us to activate our big welcome labyrinth and then I'm just popping off, bro I'm popping off. I do not care no more. I don't know what you call. Damn. Can we please get a rule a check, bro What do you call against dragon link with dimensional barrier? What stops them the most synchros big welcome labyrinth time? Thank you labyrinth Ku Klok. You've served me faithfully No, I was served so faithfully This can't be happening to me. I'm just gonna say synchro I'm saying synchro. I'm saying synchro. I'm saying synchro. I'm saying synchro. No more synchro summons Can we all agree haha guys don't get me wrong? I knew he was gonna link that away, but damn I think we just lose here that ash blossom Really stuffed us. Hey, we'll still play it out cuz who knows what if what if some way sleigh forgets in the graveyard And we can just revive sky got him you never really know shout out to Romulus being summoned I really appreciate him coming out here. Oh Brother this guy links. He's only on the rocket recharger step. I don't even know what step this is I have to I have to learn to pay more attention to dragon link plays because I really do have to learn how to actually Play around this stuff, but for the time being pissed he is on the field and yeah, not much we can do now We've just got Ariane out here. We've already drawn our thing We've we've popped his one whatever looking back We definitely should have saved our punishment for something a little scarier than something that was just gonna get linked Away for free like we probably should have used it on this but hey you live and you learn in my right guys I'll tell you what though. He's actually going pretty slow, which tells me that we might have a chance. Oh Hey, you didn't he really did not need a borreload Borrel and dragon affect my ariana, but I felt that I pre a I get you Okay, now if there's any way for me to win this duel. I am not currently aware of it Oh, that's I mean, I guess that's the best draw. We could have gotten Sadly, I don't think it'll help us but let's just play it and see what happens ariana the labyrinth servant Let's do our thing for all of one second probably before we get me. Yep Well, this is a this shows why you'd want to draw slifer and not the revive sky god Not that I don't wait a minute big welcome labyrinth in the graveyard. I'm out of here. See you That was dumb. All right, I'll set a card face down and end my turn. So what do you got to beat that? All right, actually, I'll show myself out. Thank you very much. Have a good day I Never never mind. I'm on my way out. Oh basic field. Okay, so this guy's just a player of the game and we got slifer Cyframe drivers and an unfortunate draw in the opening hand labyrinth labyrinth is also a little unfortunate man unfortunate cards Okay, but at least we got ariana. So that's our saving grace as we drew ariana and we drew slifer a sky God, you know, we could have drawn that What is this shadow of the grenadier? I don't think I've ever seen this, you know It says something about master duel, but every time I see a card. I've never seen before I'm always worried that it's either an OT up nevermind. This this is a card I've seen before I thought for sure I was gonna be up against a bot or something for some reason now Don't give you why he shows no signs of being about salamander salamander. I got distracted by the salamander Wow One level nine from hand or face up chooses the hand.  All right, who do you discard though? I I've never played up against these guys I'm not gonna lie. I've seen these cards. I've never once played up against them, which is a little bit scary Not gonna lie. Okay going into an XC's nine here. Whoa Never seen this before gotta read gotta read hurry panic tribute to succeed some of cards Special come with one non fairy grenadier XC's monster from reaction deck. Is this a quick effect? It is uh-oh Then you can attach cards from any field and her graveyard so that special monster as material up to the Number of materials this card had so to you can take two of my cards for free. That's awesome, man That is that is so cool.  All right I just because I drew a card well because I I did my draw phase you get the special something that Well, please don't do this. Please don't do this. I feel like we're getting outplayed Labyrinth Labyrinth is gonna go ahead and hit the field. Oh my god I have to play through what how many negates am I playing through all right? That's fine. At least we were able to get lady labyrinth out here in front of the Absolutely disgusting effect going on that is disgusting, man I'm setting this card just to make her untargetable, but it doesn't matter I'm about to get negated anyway, and then absorbed Damn Okay, well at least I can set a normal trap. I'll take that oh that's gonna get negated I played right into his crummy little hands, and there it is that that is not a good situation for us Probably should have one with a big welcome labyrinth, but I panicked here. So oh my god. What is this? No, my I knew he was gonna take my revived sky god Detach the sky god. It doesn't matter. He's taking my sniper strategy Oh, there is no way and he gets another one back. This deck is crazy Unreal what how have I never played against these guys?  It was nothing. I could have done Oh, I'm actually pretty bad at the game. So I there's probably something I could have done And if I got Torbi, I probably could have set like I could have done some other stuff. They're mine Unreal dude, that was a craze. This is a crazy deck for me to have never played again Unless it's like super bricky, but I had from just the combo he pulled off like unless I could have tributed these monsters before They got to play or something I feel like he just had a way to clear too many of my car I mean it's over dude. I might as well hand over the last god card This guy ain't all the pieces of slifer.  That was nuts. What a board by my opponent I mean not much the slifer deck could have done there I just just would have been too hard to get through I think I'm not sure what we could have done to beat that opener That was really good.  That was insane. I'm doing one more duel I'm doing one more duel. I can't go out like that. I'm okay going out on a loss But not like that another opening hand chance and this time we're looking pretty decent This is a pretty decent situation.  Oh, it's mathematics. We lose Actually, we have sideframe gamma to negate circular. Maybe we don't lose question mark. We actually also have in per Question marks. We've got a lot of question marks sideframe gear gamma go negate circular's effect He's gonna surrender isn't me. Please don't no way I've seen math make players play around it. Just not obviously, you know, it's not ideal You'd like your effects not to be negated the math back players can play through this. I should probably I don't know what I did here I probably should have in perm first time. I don't know man I thought hey hey any to the graveyard seem pretty good to me and I failed Drollin Lockbird for the the synchro Oh What is this Drollin Lockbird hits the field? Ah, bro is here for a good time. Not a long time man. Damn. That is unfortunate All right, he's he's still going in splash mage. He's still going off unfortunate circular comes out Unfortunate man, but its effects are negated unfortunate man I still so what does he do here because I negated his effect in his hand to stop him from even getting it to the field He just use it to link So I don't think this is as good as he would probably hope right We stopped it in its hand got it in the graveyard bright and early still goes into transcode talker. Okay, so what's the difference though? What what difference did I make? I'm not a math make Expert by any means necessary, but what difference did it make and again the other play I could have made is wait for circular To actually get to the field in permit Then we could do some talking which I feel like I definitely should have done looking at this field now But maybe I'm wrong, you know, maybe like usually they do this anyway. I guess so I'm not sure Up setting a card. That's the end of his turn. All right sideframes get out of here, bro Get out of here. Nobody wants you. All right draw phase go. All right. Okay. All right a little something Now one good thing about keeping lady labyrinth is we're actually going to get to keep them You know pretty alive here as long as we set. So let's just go ariana real quick Ooh, you know technically that's totally fine. Is that totally fine though? I actually have no idea if that is totally fine.  Okay, so I think what we do here is we coup clock just to give us the ability to imperme so that we can search So we're just gonna set this and that's not a labyrinth card. So we can't do that. Damn Damn it's man. I'm not a labyrinth player.  All right. So again We are not going to do that. Why didn't set imperm in his spell and trap column? I will never know another mistake. How many mistakes can you possibly make? Let's set another trap card.     Please labyrinth players spare me, bro. I I need guidance in these trying times, man Oh, man. Oh, man. Oh, man.  Imagine if I could have derailed him right now Oh Daruma, oh my god That would have been huge. We're cooked, bro. It's good. It's we're cooked. We're cooked Damn, I gotta do one more. I gotta do one more. I gotta do one more. I gotta do one more.  I'm so dumb.  The pain I feel is crazy Okay Okay, I don't mind this. I don't mind this at all. All right, so let's do ariana And then I think we go into coup clock, right? I'm actually like super doubting myself nowadays Okay, so I think we go into coup clock. I think but I never know anymore I bro. I was so much more confident unironically at the beginning of this video. This does that and then I can Okay, okay. Okay, so I add coup clock because we have torby Okay, so then we coup clock first and we torby second And then I guess I'm gonna get rid of imperm because who needs it right? Well, we'll get another one So we torby again big welcome labyrinth big welcome labyrinth sets down on the field coup clock effect activates We can go ahead and use it. We are gonna get a tad bit greedy here special summon to the field We're gonna assume he doesn't have ash blossom for this big welcome labyrinth big welcome labyrinth allows us to summon our Lovely and we got greedy Damn He outplayed us again We the the greed The greed The greed it never stops. Oh the pain never stops does it? Oh the greed this went from a slifer video to a watch me watch me learn labyrinth only Only up to the point where I understand enough to know that I don't understand at all. Damn it hurts Oh, me. Oh my the greed is crazy, dude I even why did I learn enough labyrinth to know that that was a bad play to make and I still did it? Oh the greed I sat there and assumed that because he let ariana go off that he didn't have it Oh, anyway, no, he really thought that I also wasn't packing an ash blossom. Dude. That's crazy. That that is actually insane response Oh, you are running thin rear. That is nuts That is insane that you are running thin rear. That is insane.  No, no, please. Please don't please don't god.  It's vanquishle vanquishle He had two fen rears and now i'm sitting here pissed, bro Now i'm sitting here. I could have drawn two. I could have had pot of greed Should have just shotgunned it. I always do I didn't this time and I regret it Why this was such a fun video before this became the worst video of all time Why dimensional barriers worthless in this matchup? But at least if he wants to go into rock of the vanquisher, which I think he should actually against this board I probably wouldn't he's gonna banish my coup clock, which is awesome. But I mean, I guess why would I ever do that?  The only reason I'm staying in is I'm hoping he special summons more We draw into something usable and life goes on. Oh my god. He played in the same I'm getting I'm getting up late. I'm getting up late. I've got I've got to dodge it I've got it. This is my only chance. It's time. It's time to bounce Big welcome labyrinth get out of here. Let's get ariana out had to do it a bit earlier than anticipated Hopefully there's no counterplay to this. All right ariana safely hits our thing. Um, uh, do we Do we torby here? I don't know. Do we torby here just to make the pain hurt less? I'm gonna say no No way. He's also doing Borger into a max c-turn. Oh, well, I guess he might actually have enough for game here Because I didn't I should have torby now that I'm thinking about it He might have enough for game if he has the burn effect earth and fire We know he has too fire But does he have earth he plays raisin again That's for the battle phase does he have earth if he has earth and fire Which we know he has rise heart this game's already over. Oh my gosh This game is over if he has earth. Oh god. Is it over? Dear god. Do you have earth? Main phase two. He has earth. He has earth Oh, does he not have earth earth check? No earth not even dark. Why do you special summon borger? All right. Well, I guess just for the damage Alrighty then. What is this? Actually?   I don't see us winning this one Especially with Dee Dee crowing in it is probably and very unfortunately over for us bros I don't see a way out of this this video dragged on a little too long guys We flew too close to the sun unfortunately with this deck a little too close to the sun. I'll never I'll never Okay, listen. I'll never regret pushing it. Okay. Look, we had we had to see if we had wing Why did I play this in raisins column?  I don't know. I don't know why I did that. I don't know man Best we can do is coup clock. Well with the end of our turn That's going to bring us to the end of this video I'm doing the outro now because we all know where this is going, bro Hopefully you enjoyed this anyway. We had enough.   That's gg's I'm just gonna big welcome labyrinth. Thank you for watching this video. Hopefully you enjoyed it. Sorry the deck fell Well, not even the deck. I fell apart at the end of this video But you know the beginning was so fun that it was worth it.


Hey guys, I need to do a doodle that I'm gonna scan so I thought I'd film it so I'm gonna turn up my music and I'm gonna speed through it and Yeah, so I'm gonna use Bristol vellum when I'm doing work that I'm going to scan and turn into something digital I always like to work on smooth paper Bristol vellum hot press watercolor paper, which I really don't like to paint on but if I'm gonna do something I'm gonna scan I I feel like I get a little bit of better image of my work and the texture of the paper Doesn't get in the way of the artwork and because the textured paper scans very weird. So anyway, so I'm gonna work on Bristol vellum I've got a variety of black pens. I'm gonna speed through it and I'll be right back Okay, so there's my doodle. I really like the way it turned out. There's a reason why I needed to do it So my Facebook group just recently hit 2,000 members 2,000. Can you believe it 2,000 people? Oh Yeah, yeah, alright, so I'm so appreciative and grateful that that many people have chosen to join me in my Journey of art and self-expression and in honor of that. I am going to turn this Doodle into a piece of digital art that's going to be free in the Facebook group You have to be a member to get it. I know those of you some of you were groaning, but I'm sorry So this is for all of you in the Facebook group as a thank you for joining and following me and my crazy adventure I am going to scan this you can reprint it and use it as often as you'd like I just ask that you not sell it and that you don't take my signature off of it. That's all I ask Alright, that's it for today. Don't forget the most important thing besides looking in the description below and checking out And see if there's a way you can support my channel Don't forget the other most important thing go out and have a great day and do something nice for yourself because you deserve it And I'll see you later. Bye guys


get learned. There's some hype in there, so I will do a little bit of explaining about what machine learning is if you're not familiar with that, hopefully explaining what it is and what it isn't. In production, that is slightly a lie. This talk is about a product that was supposed to be in production at this point in time, when I committed to doing this several months ago. We've done a lot of work to get it there, but take with a slight gradient of salt in that I won't actually be able to talk about the learnings of this thing to scale up so far. But we will be using scikit-learn. I am a data engineer at Simple. I will tell you a little bit more about what Simple is. First, a little bit more background on me. I am Jeff Colucas. I came to technology through science. So I went to grad school at the University of Wisconsin in Madison, and I worked on the Large Hadron Collider, which is really fun. This is me in the cavern of the CMS experiment in Switzerland, about 100 meters underground. So why do I mention this? Since I now work at a tech company, I mention it probably because it is cool. It was a fun project to work on. I also mention it because when you think of machine learning, you often think of big data. And this was a project that was definitely big data. Like this thing is running. There are collisions happening every 25 nanoseconds. Like each one is like a megabyte. It ends up being a huge amount of data. There's all this cool trigger system to get down to a manageable size, and you still end up with petabytes of data. So petabytes, you have 1,000 gigabytes of terabytes. 1,000 terabytes is a petabyte. And you have hundreds of petabytes of data sitting on a disk somewhere, and you're doing distributed stuff to process it. So I got to do that in grad school. Never once did they do any machine learning in grad school. So huge data sets, no machine learning. Why? Some people did use machine learning in that realm, but it's kind of something you only resort to if you have to. Because in research, you want your results to be interpretable. You want to be able to explain what's going on. So I'm kind of proud of that, that we really did. I feel like we did a good job of relying on. If you can make a plot and say, I cut out everything below this line on this plot, that is understandable. And those are the kinds of papers that we were writing. So lots of data, but making these very interpretable things on top of it worth the machine learning, you're kind of handing data over to a black box. And then you say, what did it do in that black box? And you can talk about algorithms, and you can talk about linear algebra, but there's a lot more context you need to know. And in the end, there are a lot of details that are harder to reproduce. So these are the reasons why you might not want to use machine learning for a problem. And what I'm talking about today is going to be machine learning on a very small data set, which is a couple of gigabytes. This was big data, no machine learning. Today we're talking about machine learning without big data. And there are good reasons to do that or not to do that. And hopefully you'll leave this with a little bit better understanding of why you might want to or not use machine learning for your problem. So a bit of an overview of the story today. Talking about the problem that we're solving. In our case, it's about classifying text chats. We'll talk about that question of, why did we use machine learning for this problem? Why might we not want to use it for this problem? We will walk through steps of developing the model.  And it turns out that humans love live demos and seeing things actually happen. So you humans will be able to experience that rather than just seeing slides and believing me. Then we'll talk through some complications of this. Okay, so you've built this kind of toy model you've proved that it can work. And then you actually want to run this thing in production and do useful things with it. And what gets complicated about that? How do you manage that whole life cycle of creating a model and using it?  And there are lots of different reasons why the shape of your organization and your team and your problem are likely not going to be exactly what is shown here. So other approaches might work for you. So what is the problem that we're solving? A little bit of context. This is simple. It's the whole idea of banking remade with a lovely design, equally lovely tools to help you save a break inside your account and genuine human goodness. I'm gonna actually be focusing on this genuine human goodness part, which largely talks about our customer relations. A big piece of what makes it simple, different from some other bank is we have a fantastic customer relations department, which all works at our headquarters in Portland, Oregon. About half of the company is customer relations and they're great people and the problem we're talking about today is about making our lives easier. So I would say simple, I'm remote, actually, I've been to Columbus area. I'm the only one in Ohio, so I was actually in Portland last week and I'm glad to be back. Categorizing jets. So our customer relations department, the primary way that they end up interacting with customers is through, in the app, you can send a support message or a chat and then a customer relations agent gets to respond to that and be pleasant and oftentimes sends you a gift in the response. That data all makes it into our data warehouse. So what you're seeing right here is a fictionalized query somewhat to our data warehouse. Amazon Redshift is what we use there. So it's a database. You can run a query against it and you can look at a bunch of this chat data. So chats have a subject and a body when you initially submit them and after you submit the chat, there is somebody in customer relations who's assigned to looking at the queue and triaging chats, assigning them categories so that they can go to different people who are specialized in different things. So one of the categories you see here is urgent. If you lose a card, it's something that's exposing you to fraud, it's exposing us to fraud. We want to deal with that as fast as we possibly can. So that's part of the reason of triage is that hopefully we can find these really important things quickly and then make sure that somebody is assigned to it. So these are the categories that you might assign. Urgent, customer education, a new product, incidents, other things. So this is a great situation for machine learning and that we have this data warehouse, we have this data set with hundreds of thousands of chat messages that we can go take a look at and we can have a machine come look at this stuff and basically what we've got here, subject and body. These are kind of the question and then category is the answer that we want to get out. So the problem that we want to do is we want to do the categorization thing automatically. We want a machine to handle this so that a human doesn't have to and we already have this large corpus, this large set of data where we have a recorded answer, like a human has already tagged these things and we can use this as a training data set for figuring out if some approach we have works or does not work.  Just to be a little bit more explicit about what this looks like, this is in our web app. This is a case where me, Jeff, actually lost my card in St. Louis and I sent a message and this is basically what we're talking about here. So you might have this whole long chat with an agent but it's just this initial contact where I say, hey, I lost my card and the subject on this was lost card. We want to categorize this thing so we can figure out who to send it to to get the chat started. These are some of the lovely customer relations people that we have. They are smart empathetic people and we don't want them to have to spend their time sitting and just reading through chats to categorize them. We want them to be using their empathy to actually respond to issues. So how do we approach this problem? Obviously we want to use machine learning because it's got sparkles and hearts and big companies use it and it makes me feel warm and cuddly and smart. Those are bad reasons to use machine learning. Like I said before, you do sacrifice a lot of interpretability. So if I tell you that I want to use machine learning with this problem, your first question might be, couldn't we do something else? And you can imagine things we could do. We could make some simple rules of like, we just have like an if the subject contains lost card, maybe this is something we want to treat urgently. And you could imagine totally solving this and just like having kind of a list of rules that some human is coming up with. And that would be a great first pass. See if that works for you, if that works, then please do that instead of doing machine learning. There are a couple of reasons why you might want to do this. If you try to do that, but like there's still a whole lot of cases that you aren't catching, there's a good chance that machine learning is gonna allow you to get farther in terms of optimizing the accuracy and scope of that. It can get very hairy to maintain rules out like that. In our case, we have data science department that wants to own that, like figuring out how do we categorize chats. And then we have engineering that is actually taking those rules and putting them in direction. And we've had a couple of cases where we've tried to use some simple rules and it ended up being very difficult to keep that communication going and like do the data scientists like go in and change the service code. Emission learning, one of the nice things is that you can create a model artifact that then is something that's very easy for engineers to say, then like take that artifact and put it in. And it's kind of this thing that data scientists can create something that's plugged into another service. In our case, we get to use a bunch of natural language processing techniques, which like this is a whole field of research. And it's something that Suckit Learned, for example, has great support for. So one reason going down this path was nice is that you get to piggyback on a lot of these things that are already existing for the kind of problem that we want to look at. And management wanted us to use machine learning. And that's something, you know, there is ability in that in that like our data science organization has greater ambitions as to like the ways that they want to do this. And we wanted to take a small, understandable question to really prove what this whole workflow looks like. So implementing this is hopefully the first step towards having a structure to be able to tackle other problems of machine learning in the future and do exciting new things.  You would go to scikit-learn documentation and you would try to figure out what on earth is going on. So this is, I'm going to kind of walk through my journey of understanding stuff. So scikit-learn documentation, it's actually, it's pretty great. There's some good stuff in there. I recommend you check it out. So if you go to the front page, you go up to the documentation tab, you'll get to the music guide, and it's actually got this really fantastic just first paragraph section about what even is machine learning. So I'm just going to give you a little bit of a highlight here. It says a machine learning problem consists of a set of n samples of data and then tries to, sorry, yeah, it considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is a singular number and, for instance, a multi-dimensional entry, it is said to have several attributes or features. So in our case, we have a data set with two features. There's a body of the message and there's a subject on the message. So we have two features to feed in and then we want to get some answer out of a classification. There are generally two types of machine learning problems. There's supervised learning and there's unsupervised learning. Unsupervised learning is in the case where you don't have the answers already. So if you didn't have them already classified, this is things like clustering so you can imagine taking all of those chats and saying, hey, algorithm, try to figure out some things that look similar to each other and maybe that would be useful for even getting a sense of what categories might want. I want to have them. You could take a look at those clusters. What's in common of those? Oh, it looks like lost card. Things are a category that I might want to consider. That's not what we're doing. We are doing supervised learning and in particular, we're doing classification. So we already have a data set where we have inputs and what should the classification be? We're gonna train on that data set and then we are going to spit out a model that's able to take in new inputs and predict a classification for it. Okay, now we get to the fun stuff, the code. This first slide kind of lays out the whole thing to be prepared or jumping into the scikit-learn. In particular, we are creating a pipeline. So this is one of my favorite things about scikit-learn is it has this whole API with lots of bits and pieces but it provides this nice kind of wrapper of you can define a pipeline with a whole bunch of steps of how do I want to process my data and then finally apply a classifier to it. And this ends up making our job of taking all of this stuff and turning it into an artifact a lot easier on the line. So this slide serves as an introduction scikit-learn. It serves as an introduction to network language processing. So I'm going to go through each line a little bit here. So creating a pipeline, first step in here, we're giving it a name, we're calling this step pre-process and then we're passing in this message pre-processor. So message pre-processor and text processor. In our case, these are two classes that we have defined but they inherit from this scikit-learn interface of a transformer mix-in. So it gives you this way of you just instantiate a class that's based on this mix-in and it becomes something that you can throw into a pipeline. So the message pre-processor, all of that is doing is it's taking in some chunk of data that has a subject and a body in it and it's turning it into a single string and it has the subject weight in here. So subject weight is simply include the subject that many times. So we found that the subject tends to be more important than the stuff that's in the body. So this gives us a way to kind of dial up how important should the subject be.  So now we're just done to a string. Then we use this text processor. It takes in stop words and a limitizer, so stop words. This is a concept from natural language processing. These are things like and, as, but just like all of those words that don't really add information and just kind of connecting together the sentence. So this is something you might wanna play around with. We might wanna include simple, simple.com, other things that are specific to our domain that really aren't adding information to the message. And then a limitizer. So this is another natural language processing term. A lemma is the formable word that you would find in the dictionary. So if you think of stopping, the word that you would look up is stop. So a lemma-tizer is just a chunk of code that will take in a bunch of words and change stopping into stop, change going into go, et cetera. So this is some of those like pre-canned techniques that you can take advantage of. You can find lots of documentation on that. So this is cleaning it up. Next step is account vectorizer and this term frequency transformer. These are just progressively going away from, like we had text at this point and now we're just kind of creating this mathematical glob that is easier for an algorithm to understand. So I'm not gonna go into what those things are specifically and then finally, now that we have this nice mathematical form of the data, we've turned it into this account of words and it's transformed. Then we throw it into this classifier. It's a gradient-boosted classifier. Don't ask me about the specifics of gradient-boosted classifiers. One of the data scientists is just this. Okay, so this is the whole pipeline. All the steps that our data is gonna go through. Yeah, correct, yeah. Great question. So one of the great things about scikit-learn is that it has become this, it's kind of a cornerstone of if you wanna do machine learning in Python but it doesn't have to stand on its own and a lot of other packages provide wrappers that make it easy to plug things into scikit-learn. So yes, yeah, XGBoost is a separate package but it provides a nice wrapper and you can just throw it into a scikit-learn pipeline like this and yeah, feel free to ask questions as we go through them. Any other questions in a moment? It does come with some classifiers. So yeah, I mean this pipeline object gets, like this is literally a list that we're passing in here to the pipeline object so it holds all this stuff and then you'll see in the next slide like how we pass data into this whole thing. Yeah, one last question here. Is natural language toolkit a specific library? Oh, NLTK, is that what it is? I have no idea the overlap there. I think that we are using NLTK in some of the stuff that we are doing here. I can't remember the specifics of whether, you know, what's built in and what. So we have a pipeline, let's actually do something with it. So the first thing that we have to do is we have to train the model with existing data. So this gets into a little bit if you've never heard of pandas. It's part of the Python ecosystem. A quick introduction to the Python ecosystem. There's NumPy, which is a library that's just like efficient arrays and stuff, kind of like basic building blocks. There's SciPy, which is a bunch of algorithms that are useful, and pandas is kind of like data science glue stuff that makes things convenient. So pandas has this read SQL function. You just pass it in a database connection and a query and it spits out something that's called the pandas data frame as basically, you know, it's a big matrix of information with a bunch of convenience functions. So we're pulling all this data out of our data warehouse, the category subject and body, and then we're breaking it up into, you know, Y is the dependent variable. This is the thing that we want to figure out, it's the category, and then X is the independent variables, the subject and the body. Anything about this line, rub you wrong, and I think it seems non-idiomatic about this line. Somebody want to call it out. This is a capital X over here. Why is this a capital X? This would be a lowercase X because it's a variable in Python. It's a capital X, this is a case of kind of math idiom trumping Python idiom. So in linear algebra, in math in general, like this is a matrix. This is a single, this is a one-dimensional thing. This is a multi-dimensional thing. So that's why you'll see these Xs be catalyzed. Okay, so we break it up, and then we break it up even more. What on earth is going on here? Train test split. We are essentially saying we want to reserve one-third of the data for testing later, and we're only going to trade this model on two-thirds of the data. Why would you want to do that? The more data that you have to train, the more accurate your model can potentially be. This I will explain in a little bit why we want to reserve this. But that's what we're doing. We're reserving one-third of the information for testing later. Finally, we call pipeline.fit and we pass in all of this information, this training data set in there. So we're telling it that X stuff, those independent variables, and we're also telling it these are the answers that we've already created. So once you've done that, you want to go back and you want to use this test data to validate how is it performing, and why do you need to validate? Because there's something called overfitting. So imagine that these black points are some sort of data that you're interested in.  You don't know if that doesn't pass through the points. So there's some error there. It's not perfectly modeling the data. But generally, the black dots tend to be in the lower left corner and they tend to be in the upper right corner.  This blue line perfectly fits the data. It hits every point. But if I take some more observations, do you think that they are going to fall perfectly on that blue line? Probably not. This blue line tells you absolutely nothing about the underlying structure of the data. So this is overfitting. This blue line is, like you have extracted too much out of your training data set and it's gibberish. It doesn't really tell you anything about predicting future results. So that's why you want to reserve some so you can tell, have I overfit? Would this be able to make reasonable predictions for future inputs? So testing the model looks like this. We have our X test matrix here. We're throwing it into this predict method of the pipeline. So that spits out what the model says the answer should be for this test data. And then we're printing out this classification report that considers what the model predicted and then what were the actual assignments that the humans made to this? It does some statistic things. And then it spits out, this is generally how you are performing. We're not going to go into the statistics of this and this is actually, this is bogus and does not actually reflect the data that was thrown in there. But that's the kind of thing that you can get out. The score is kind of basically telling you if this were a one, it means like you always got it, your model's doing great. If this is lower, it's giving you a sense of kind of what maximum performance you can expect for future inputs. So we've actually trained the model. We put some information through the pipeline. So we have a model that we can use and now in order to put that into some context where it can be used in the future, we wanna actually wrap that in a Flask service. So this is what it looks like to actually be able to spit out classifications in an API using Flask. So we are defining a root. So we're gonna have a service that you can hit a URL that looks like this. This is the messages endpoint. You post JSON data here.  Then we're calling the pipeline.predict method again. This is the same thing that we called in testing. And then we're just massaging the data out of those predictions and JSONifying it again for something that we can send it back to the user that requested it. We'll get to that. We'll get to that. We're just asking about the pickling and persisting model. Okay, so live demo time. This is exciting because it means that I can mess up all sorts of things. So over on the right here, we're going to actually start up this Flask app. It's bundled up in this bin run executable that we created. So this is now running. We have a Flask app going and you'll see this spit out log messages as we make requests. So let's try to hit this API. We get 405 because we said that this should be a post. So we just made a get request in return of 405 because we didn't tell it what to do with the get request. So let's make this a post instead. So dash x post. I'm using curl, which is a command line thing for making HTTP requests. Got 400 this time. Getting a little bit better. We're making a post request now but we aren't actually posting any data. So we need to actually give it what data are we trying to post? So let's actually put in all that stuff here. So you can just ignore these two IDs. This is just for like tracking so that when you're making a request you can understand what is the message that I'm getting back. But we're putting in a body and a subject here and this thing is actually running and we're gonna see what classification it makes for this message. Do you like that class label? A crappy class I had as urgent. Good job. So let's make another question here. Let's say instead of this I am interested in some new feature. So let's say joint accounts. I wanna ask you about joint accounts and I'm gonna put in my body here. I want to share money with my bae. All right I ended up in customer education. I think that I, yeah, if you do a slightly different that I want to share then let's see if I can account. Yeah okay, I got this as a new product earlier. So you can tell it here, this is not perfect. There was not like material difference between those two. So it identified, so the shared accounts were like the big news thing that we launched in January. We didn't have joint accounts for a long time so people were very excited about this. But that was a new product. All right, anybody want to volunteer information for one last question, we'll see what it does. That's a good question. Let's see, I found my card. Yeah, I'm just the lost card thing up there. Yeah it might still, the ASAP part may mark this as urgent. So, interesting. Do you exclude some artists feeling hurt? They may, I don't remember whether we, I don't remember whether we strip up punctuation, may strip up punctuation. But I'm looking for a volunteer to put something else into this before we move on. Okay, what kind of card protection? I will say protection. And what kind do you offer? Yeah, customer education. A lot of these end up getting labeled customer education, which I think shows that we have a lot of things that tag customer education in our input. So I am running lower on time so I am going to keep on chugging and I'm going to go back to the slides. So great with the toy. All right, all right. So we have a nice toy. It doesn't work perfectly, but it does, it's better than not having any classification at this point. So we put it in a web app kind of shape. How do we actually take this to production and allow our data scientists to do their thing and create models and allow this to actually get into this application? This was supposed to be, oh yeah, okay. I was supposed to say something about the training wheels coming off. Anyways, so some things that you might want to consider. I'm going to talk about like three steps of stuff that we did to turn this into something more sustainable. So the first step of real life here is separating training, training the model and actually serving the model. Again, this is kind of like, data scientists want to control training the model, engineers care about serving the model. The one is kind of a better oriented thing. The other thing is something we want to serve in real time in a service. So to do that, we need to persist the model somewhere in the habit, someplace where the service can pick it up. And somebody asked the question of did you use pickle to do this? Unfortunately, yes, yes we did. And why did we do that? Because if you look in the documentation like it has a section about model persistence and it says, hey, there's built-in support for pickle, it has some issues. If you don't want to use pickle, you're on your own. And that is pretty much, this is one of the hairiest things actually. And this is something that I'll talk a little bit later about what some larger companies do to solve this problem. But we are using pickle. Issues with pickle, if you aren't familiar with it. So pickle is like a built-in Python library for taking a Python object and serializing it into something you put in the file. Issues with it are number one, it's like, it's code.  So you definitely don't, if your friend says, hey, I found this file, you want to unpickle it? Say no. Don't do it. So you want to trust where you get your pickle from before you unpickle it. This is, this is in line. Well, that's got exciting. So in our case, we're using pickle and the place that we're doing this, like we're all in Amazon web services. So S3 is the object store in AWS. So we're creating an S3 client. KMS is Amazon's key management service. So what we're doing here is we're dumping our pipeline object to a pickle file. We're then encrypting that pickle file and we're putting that object to S3. Why are we encrypting it? Again, we're using this key management thing. It's, this is to guarantee that it came from this service that has access to the key. Or like whoever created this pickle had access to the key. And hopefully it wasn't some rogue engineer that has access to many things. Hopefully it was actually the service. Yep. So this is what it looks like for us to turn this thing into a pickle and put it in S3. And then our flash gap pulls the thing up when we start the app. That's what we've decided. So we have no way of like automatically doing that. If we want to change which thing, which model we're using, we just, we change the config file and we restart the service in order to pull in that new updated model with more training data or with updated logic. And that's one of the really nice things about the second line pipeline is it's, yeah, it's packaging together all the logic of the pipeline along with kind of the trained, the trained data. So you follow this app, you unencrypted it and then you have it available to your app to use. Step two is providing an environment for doing this batch training and evaluation. I'm gonna read through this quickly. This is something like the actual training of the model. Data scientists could do that on their laptop. In our case, it's not super computationally expensive. So you could just do that, create the thing, you know, the, create your model there and then have the data scientist put in S3. We wanted some more guardrailed on that because it's like, what if they pulled in some random version of something or whatever. So when we train the model, we want to know like what version of the code is used, what are all the libraries really be able to reproduce this model if it came to it. You also might want to do really computational, computationally expensive things when you're training the model. So something that we could do is there's this grid search thing, what this whole blob is, it's just saying like, hey, our pipeline have all these parameters we are passing in. We don't know if those are the best parameters.  Let's just run this whole process to fit the model on each one of those combinations. And that's where you start being like, well that's, it would be nice to have an environment to just do all of that stuff and not do it on the laptop. So that's something that you might want to consider. And step three is when you actually, you have your app running, how do you know if it's doing reasonable things? So you want to do kind of all the things that you would do with any other web service, you want to monitor the performance. You have to figure out how you adapt to production load if there are other spikes. And when this happens, do we get pretty average consistent from the model, pretty average performance from the model or does that vary a lot? How do you degrade gracefully if something goes terribly wrong and other applications that are now, you're relying on getting these predictions back, don't get predictions back. And as I said at the beginning, we will be, I don't have a whole lot to say on this subject yet because we have not actually started relying on this yet. So we have a toy and we know that the general approach works, but we shall see how it goes when we actually get this into production and rely on it. So last thing I want to touch on is what are some other approaches? If you don't want to, you'll put together your own batch process, figure out how you're going to do all the persistence and all of that. So considerations is how big is your team? For us, we have a team of three data engineers right now. We have a team of like half a dozen scientists, but like all of us were kind of working on this part time and took us kind of three months to build what we built. A lot of companies that really care about machine learning have a whole team of machine learning engineers and stuff. And this starts to look very different in that case. If you're really big and you have lots of engineering resources to throw at it and you care a whole lot about the performance of the model, what a lot of people will do is sometimes actually train the model in scikit-learn or in Python, data scientists are familiar with that. They can create this, you know, some artifact, but you choose some custom serialization format like you dump it out to some JSON with the parameters and then you might have an application written in Java or Scala or something else to actually run model and get better performance that way and you kind of define your own interface to get from one language to the other and that takes a whole lot more effort than what we threw at this. So how large of a problem space do you need to cover? If you do that sort of thing that I just said, of like defining your own custom serialization format between one and the other, you have to do that for every different kind of model that you want. So this is something where like right now we don't know what all kinds of models we might want to use. So we wanted to focus on, let's try to do something that we could adapt to, like other stuff in scikit-learn and we might want to spin off some completely different model. So it's kind of nice that right now we could do something that is significantly different from this natural language processing problem and throw it in there. So those are some of the considerations you might think about. There are also companies that would love to take your money. I don't know how legit any of these are. These are some of the ones that I found. Why Hat has a science ops platform, there's Anaconda Enterprise, there's Domino, they all have these nice shiny marketing materials and they claim that they're gonna solve your communication issues and everybody's gonna be able to live in their happy land. And it's some variation of like we'll provide infrastructure for you to run this stuff. Your data scientists can like click on buttons and decide what version of a model is being promoted.  We didn't go too far down the path of understanding that. All right, I am going to finish up here. You wanna train test and batch environments, even if you wanna serve your results in real time. You have this nice concept of like serializing something and having this artifact of your trained model that you can then use in a different context. And Suck It Learns Pipeline module is really helpful for making it happen and then actually serving the model in a real time context. Once you figure the rest of this out, this looks a whole lot like just serving any other application and likely the details of your own environment apply to that. So thank you. And we have approximately eight minutes for questions. I am on Twitter at Jeff Gluckus. I have already posted these slides. So you can see them on Twitter. I will try to remember to post them in Slack or if somebody would like to go ahead and post that link in Slack. That would be good. Questions, do you have that question here? Once it is in production, do you have any way of seeing from the results of learning from the results of the thing so that it's say 95% of the queries are classified as customer education? You can think you might be able to detect that there might be an issue with your training materials and so forth. Yeah, our plan is to, first of all, we're gonna be logging off these results to a database. Okay, yeah, I will repeat the question. So the question was how will we know, as we're using this in production and we get answers out of it, how are we gonna know how well we're doing and if it's way overclassifying things to one thing, how are we gonna react to that? And we are planning to persist all of these classifications that happen, do a database. So it'll be kind of a flexible dataset that we can look at. Also we're expecting if we classify things wrong, humans will still end up reclassifying them. So that'll be a very interesting thing to look at is how many of these things get reclassified and then it's to be seen how much of that we've been back into the model and what exactly parameters we use to filter out what we drain into new persons of the model. So does this take a lot of resources while you're prototyping it? That probably depends on what you're doing but in our case, it's like the query to the database, it's the extensive thing. So I pull that in and then I can pretty quickly, like training the model in this case, it's like it takes a second or two. But again, that very much depends on what algorithm you're using. They're all sort of expensive classifiers and all the processes that you can do. Yeah, so the question is, yeah, what optimizations have we found? Like running a Flask app, how does that perform? And we don't know at this point, we expect it to be like, it's a pretty low volume dataset at this point. So we're not expecting to see performance issues but it'll be very interesting to see how we will handle that. Yeah, there's not much we've learned yet in that area. How many messages were in our training set? I should have an answer for that. Something on the order of like 100,000. So yeah, if our data scientists were using something like R, would we be able to modify this approach? We kind of decided to go all in on Python of like, most of our web services we create are in Scala. Spending up a Flask app in Python is weird for us so that's somewhat uncharted territory but we decided like, that's worth doing because it unlocks the old Python data ecosystem and we can just train the model in Python and then serve it in Python and that gives us a lot of flexibility for right now. So the sort of chance there is like, we would also have to figure out how to serve an R model if we wanted to do that and so that is not something we're planning to do. So okay, did we have trouble building a batch training environment that we could actually get buy-in from? So we already happened to have a, basically a scheduled task service running that happens to be in Python that it's using salary to like basically be a really dumb and expensive cron solution. So that was something that was already existing. We're like, this is Python, can throw Python code in here. So that's what we're using and it was convenient for our particular situation. And our data scientists were all used to creating jobs in there. So yeah, the question is, when we have humans going in and reclassifying things, does that get into the realm of reinforcement learning if we're throwing that back into the model? I am not super familiar with the term reinforcement learning. Yeah, reinforcement learning. Yeah, so I am not going to make a comment on that because I don't want to spread misinformation but likely, likely yes. Yeah, so the question is about, have you seen examples of where, like you have kind of like a concept of please help and is it able to find variants of that that you weren't, you know, that a human wasn't anticipating? I don't, yeah, not off the top of my head, but I mean, if you play around with this, like yes, I mean that idea of like stemming words, like lemmatizing, or they like it, it works pretty well. So yeah, it's something we're playing around with. All right, I will take one more question, yeah. I know you mentioned this in the beginning. Yeah, so we already have a process for humans classifying these chats. What's the benefit of having a machine do this? So it's two things. First of all, it's getting that workload off of a human, so it's a human that can go do something else. I'm sorry, the workload is just... Yeah, so yeah, the workload is, I mean the workload is reading through the message, understanding what it is, picking some category from a dropdown and going through that. So yeah, so it removes a human having to be dedicated to that task and being able to do what they are more talented at doing. It also reduces the amount of time to get to that classification. So even if you have a triage system set up, maybe somebody's going in once an hour and you're working down the list of new messages and as soon as the message comes in, within seconds it can have that first dev of classification. So then, people that are assigned to your working urgent tickets, like those are immediately available in that queue. So we're hoping that it's gonna bring down the mean time to resolution on those urgent issues. Thank you, everybody. And happy day.


This is stock number 10557. We are here at Summit Automotive in Fond du Lac, Wisconsin. You're new and used heavy-duty truck headquarters. Today we are checking out this super clean 2013 GMC Sierra 2500 crew cab short box. This truck has a 6.6 liter Duramax diesel with the LML motor which pumps out 397 horsepower. Summit white is the color. This truck has been fully safety and inspected by our service shop. It has a fresh oil and filter change. All the fluids have been checked and topped off and this truck is 100% ready to go. Summit white, we shoot all of the videos that we do in 1080p 60 frames per second. So if you have HD capabilities on your computer tablet or smartphone device, turn them on right now because it is definitely your best way to ensure the quality and condition of the vehicle before seeing it in person. All our rockers look really good as factory chrome step bars. It comes with the 20 inch polished aluminum rims. I didn't see any major corrosion on them and no scuffs or scrapes. If you want to check out all the photos of this truck in the upper right hand bar screen, they link right to our website. Click that and check us out there. It comes with the Goodyear Wrangler SRA LT 26560R20 tires. These tires are very new on here. I'd say probably 80 to 90% maybe even a little bit more on those tires. The front fender is in nice shape. The headlight lenses are nice and clear. Factory fog lights, front grille is in excellent condition as well. Front bumper, not too bad. No major dents or dings or cracks. Passengers side front fender is in nice shape. Did rain just a little bit here. I do apologize for that. I had the truck all ready to go and then it just sprinkled for about one minute. Anyways, passenger side rims in nice shape. Didn't see any dents or dings on that front fender. As you go down this side of the vehicle, take note of how clean the body is, how reflective and mirror-like that paint is. We take these HD videos so if you are far away. Or even if you're close by and just cannot make the trip down but you're still interested in purchasing the vehicle, you can see the vehicle, hear the vehicle, and have confidence in what you're looking at before you even get here. So that when you do get here, there's absolutely no surprises. And that's why we offer the Summit Auto FastPass in addition to everything else we do. In the upper right-hand part of your screen, link right to our website where you can apply for financing, get pre-approved, see a payment quote, even appraise a trade-off from the comfort of your own home. Has all the remaining factory exhaust so it hasn't been altered in any way. And the frame and underbody looks really good on this truck. Lower rockers look great as well. And those back tires have just as much tread as the front tires. See the frame and underbody, those box rails all look really good. And this back rim, pretty nice condition as well. As we come around to the back of the vehicle, rear bumpers in nice shape. I didn't see any denser dings on that. Has a full towing package which includes a receiver hitch and seven-pin wiring, backup parking sensors. The tailgate is in really nice condition as well. I didn't see any denser dings on that. It comes with an access soft roll-up tonneau cover. Didn't see any rips or tears on there and that looks to be in really nice condition. So it comes with a drop in bed liner, goose neck hitch and seven-pin wiring back here as well. Locking tailgate, backup camera, Z71 off-road suspension package. Has nice mud flaps which keeps your lower rockers looking great. And as you go down the driver's side, very very clean. Didn't see any denser dings on the box here. And for full disclosure, this back rim is in nice condition as well. And the back tire has just as much tread as the rest. Down the rest of the side of the truck, doors all look really clean. Comes with the telescopic tow mirrors with the built-in directional signals. They pull out like so. They also fold in. Inside the Denali package gives you the tan leather interior. There are no rips, there are no tears on the seats. They are in nice shape. Both these seats are heated. I believe they're both cooled as well. Yes they are. Weather tech floor mats throughout. Power windows, power locks, power mirrors, heated cooled seats, memory driver's seat, auto headlamps, turned-out four-wheel drive and the factory brake controllers right there. As we hop inside the truck here you can see that this one has 67,586 miles. Instrument cluster is very nice and clean. Comes with a heated leather-wrapped wood grain trim steering wheel, Bluetooth and audio controls on the right, cruise controls and information and heated steering wheel controls on the left. Comes with the six-speed Allison transmission with the optional tap shift, six-disk CD changer, Bose sound, AM FM and Sirius XM radio capabilities, dual climate control, power pedal stability control, backup parking sensors and your factory exhaust brake is right there. You've got two 12-volt power points and AUG's jack. Always like pointing out the dashes on the trucks here. This one's in excellent shape. Sometimes they can crack on this body style but that one's in great shape and this is also where your backup camera shows up in the mirror here. You also have homelink or I'm sorry on-start buttons right there. Your homelink buttons are right there and you do get map lights, headliners in nice shape and the passenger side seat is in nice condition as well. No rips or tears on there. Smells very clean inside this truck and you do get a weather tech format on that side. We'll take a quick look at the back seats and then we'll check out under the hood. Back seats are in excellent condition. Didn't see any rips or tears back here. It does have the fixed glass rear window with the built-in rear defrost. Seats are in nice shape. Comes with a set of weather tech formats back here as well. Carpeting is in nice condition. No rips or tears and it has the factory formats underneath there. Nice shape all the way around has the child safety locks on the back doors. Lower rockers or lower doors look really good and the kick panels there and we'll take a quick look under the hood. I would personally like to thank you for checking out the video today and hopefully from this HD video you'll been able to tell just how clean this truck is all the way around inside and out. Under the hood we have the 6.6 liter Duramax diesel LML motor. Gives you 397 horsepower engine base. Very clean runs very smooth. This truck has been fully safe and inspected by our service shop. Has a fresh oil and filter change. All the fluids have been checked and topped off. The truck has been gone through mechanically 100% and is 100% ready to go. Has newer tires on it and to see more pictures of this truck or one of our other 450 new and used cars, trucks, SUVs, minivans, Wranglers, halftones, sweet quarter tons, one-tons. You name it we got it go to our website www.SummitAuto.com. Full pictures and descriptions of every single vehicle from two locations all at Summit Auto.com and if you haven't if you like to check out more videos you can go to youtube.com slash Summit Auto. Remember to like subscribe and share on this video and all the videos that you see there. In fact any second you will see link to subscribe to our YouTube channel on your left. A link to more GMC Sierra 2500 truck videos like this one on your right. If you have not been to our website on the bottom link to this vehicle on our website click those checks out and we really look forward to helping with this super clean 2013 GMC Sierra 2500 Crew Cab Short Box Denali Z71 with the Duramax diesel. Thanks again.


So let's look at fatty acids. Remember that in fats, we have a glycerol with three fatty acids attached. And in a phospholipid, we only have two fatty acids. And in these amazing steroids, we have no fatty acids whatsoever. So what is a fatty acid? Well, fancy that you asked me such a question. Fatty acids, I think I already said this, are basically just crazy long chains of carbons. There's a carbon and hydrogens. They basically have a carbon backbone. And my little hydrogens are chilling off to the side. And notice, this is a chemistry fact. Carbon, you'll learn this when you're brilliant in take chemistry. Carbon forms four chemical bonds. When carbon is happiest, it has four chemical bonds that it has created or shared with other atoms. And remember that an atom, a chemical bond is formed when atoms literally share their electrons. So in this scenario, what you can see is that carbon and hydrogen are sharing electrons. And here, carbon and carbon are sharing. Here's another carbon and carbon sharing. And here's another carbon and hydrogen sharing electrons. And the sharing of the electrons forms that chemical bond. All carbon atoms are going to form four chemical bonds with atoms around it.  And in fact, if a carbon atom was missing a chemical bond, it only had three. That structure that only has three chemical bonds is going to be super unstable. And it won't stay with three chemical bonds. It just won't do it. This is stuff that you'll totally explore when you take chemistry. You'll explore it further.  And here's the deal. In a saturated fat, every single carbon has formed chemical bonds, four chemical bonds, and as many hydrogens as are possible are attached to those carbons. Now look, you remember my green carbon background? I mean backbone. That ain't a background. That's a backbone. If I form a backbone of carbon, each carbon is going to have enough electrons left over to form two chemical bonds with hydrogens. So we're going to have two hydrogens attached to every carbon until we get to the end of our chain, in which case we're going to add, have three hydrogens bonded to that carbon. We're saturated. We've filled up as many of our bonding potential places with hydrogen atoms. There are no, it's saturated with hydrogen. If a fat is unsaturated, then it could have more hydrogens attached to the backbone, but it doesn't. Why? I just told you, that ain't possible. That ain't possible. It is. It's possible if you have a double bond. In a double bond, there's two double bonds in this little scenario here. We're not just sharing two electrons between these carbons. We're sharing four. We have two chemical bonds between those two carbon atoms. Now think about this. In this carbon, this one right here that I just made green, is attached to two different carbon atoms, right? That's like all the other ones. However, it's only attached to one hydrogen. Look, there's only one hydrogen. But does it have its four chemical bonds? Of course, because it must. One, two, three, four. Boom. Carbon still has four chemical bonds, but we've actually like thrown away a hydrogen. There was a seat for the hydrogen, and the carbon said, I don't want to share my electron with that hydrogen punk. I want to share my electron with this carbon, and I'll share two electrons with this carbon. And still forming the correct number of chemical bonds. Now, who cares? Like, why does this even matter? This is so cool. I'm going to give you an example of a saturated fat. Even better, lard. Lard is an example of a saturated fat. I'm going to give you an example of an unsaturated fat. Oil, any one of them. Olive oil is an unsaturated fat. What's the difference between butter and oil? How about saturated fats are solid and fat room temperature. And unsaturated fats are liquid. And here's the reason why. If you had a pile of saturated fats, and they're not moving around very fast because it's room temperature, they're linear, can you imagine stacking like 500 saturated fats into a little box? They're all going to fit in there nice and clean, right?  Throw in an unsaturated fat. Look at those twangers twanging off in a lotty twanging land. They're twisted and twanging.  Why they're liquid at room temperature? Who cares? Well, the reason why we care is because unsaturated fats supposedly are better for us, although there's current research that's suggesting that we're not exactly sure what's better for us and what's not better for us. Another type of fatty acid that is kind of, like, questionable, like, we're hearing a lot about it in media because we have these trans fats. And this is interesting because trans fats are made by us. We go in and we add hydrogens into an unsaturated fat. We add hydrogens into the mix. And it's this, I don't have any idea how they do this. But when they do it, when they throw in those extra hydrogens and basically try to make something saturated, this configuration is the normal configuration of a unsaturated fat found in nature. But when we make, when we add hydrogens in and try to unsaturate the fat, we actually produce a different form of the exact same molecule. Can you see that all we did was spin this half, and when you spin it, this is the trans configuration. And even though there's an unsaturated chemical bond in there, do you agree that it acts like a saturated fat? So these trans fatty acids actually are solid at room temperature and there's a suggestion that those things are gnarly. Now, trans fatty acids come from processed foods and it's the processing of the food that produces them. Under normal circumstances, they would be in this configuration, which would mean they wouldn't be solid at room temperature. All right, are you cool? Why do we care about fatty acids again? Well, because we're going to use fatty acids in our phospholipids to make up our cell membrane. So let's go back and look at the structure of the cell membrane itself.


Hey everybody, let's talk about knee cave during deadlift. So it's very similar to squatting. You're just not gonna see it quite as much because the knees don't bend quite as much in the deadlift as they do in the squat. If you want to kind of translate this stuff over to the squat, feel free to pull up my knee cave during squat video. That's probably what it's called, I don't remember. Similar principles, right? So a lot of this is dictated by our hip position. And if our hips are really arched back that tends to drive our hips inward, okay? And that will in turn maybe drive our knees inward is a better way to say it for the purpose of this video. And so with that, we put more stress internally, right? We flatten our feet and we need to kind of correct that because I like you pushing through your arch but I don't need your feet totally flat, right? I need you to be able to access all your muscles. So instead, if you know somebody, I'm gonna show you my booty but if somebody is deadlifting, it might be pretty subtle. I might be, I'm gonna back up even more here. I might be here, this is too far out, this is too far in but you can't like, it's so subtle, right? This is right, this is wrong. Now I know it's subtle, but I know you can also see it. And now that I've shown it to you, maybe you can take a look at your videos when you're watching your deadlifts and say, you know what, I think my knees are caving or you know, maybe at some point we'll talk about the knees falling too far outward because that's also a thing that can happen, okay? Other things to do, watch your feet, right? So if I'm really, really flat I see my heels are kind of like turning inward like this or falling inward like this that I'm gonna wanna fix that. I need to get some stability in that foot. So one, I need to set the foot but two, I also need to make sure I'm not driving that position from my hips. Okay, so we're looking for a straight line from a hip through the knee all the way down to the ankle. Okay, so that's your end goal. Whatever you gotta do to get there is good.  If you can, then this is usually fine and you just gotta go slow and you gotta relearn how to do this lift. If you can't do that, usually you don't need another exercise, usually you need a podiatrist. So look at your feet. If your arches are really high and your feet are kind of rolled outward you might need some help under the medial surface, under the inside arch. If your feet are really flat and cave in you in you might need a little bit more help supporting the arch of the foot. If your shin bones are bowed, yeah, like this, I'll give you a black background here, like this. Instead of being straight, like a normal they might be bowed like this. That's gonna direct your foot inward because that's how the bone is bending. Foot goes inward, it wants to roll out and so to protect it your body just collapses that foot and that's just gonna be persistent unless you can find some sort of support under the medial side of the foot. You know, I have a guy who has the same issue going on. So every time he lands when he runs he lands in a supinated position and the foot then collapses hard inward driving the knee inward and it's putting extra medial stress on the knee. Okay, so consider these things. You might wanna go see somebody who isn't a movement expert. Maybe they are a foot expert and they can help you out. What were we even talking about? This is great. Knee caving, good. So some people, one last point I wanna make is some people will say that your knees are caving because that tangent, that was just funny. Some people are saying that the knees are caving because the glutes are weak. Generally my experience is that the glutes might be kinda weak but normally they're just appearing weak because they're not in a position to where they can work, right? They're working from, they're overly long or overly short and in those positions our muscles can't generate their own force very well and so you're relying then on joints and ligaments and other passive structures to keep you supported. It's like the medial knee, okay? Like the inside of the knee there. So if you do have this knee cave during your deadlifts try to set your knees outward a little bit. Make sure your foot pressure is pretty even and if you need more help, talk to a podiatrist.