So, now, let us see the update rules are not to be simple gradient updates gradient design based update. So, if we have this what is the regret bound we are going to get that is of our interest right. So, what is the regret bound? So, first thing when we give a regret bound for the follow the leader how did we do? We showed that f of t of t minus f of t omega this was upper bounded by f of t w t minus f of t w t plus 1. We ran it from t equals to 1 to n t equals to 1 to n. Now, with this adding regularization function can we have an equivalent version of this ok. So, then what will be that? So, that our first lemma says that. So, once we had a regularization the only difference the extra time we get is this term on this bound. So, this bound here get changed by this bound. So, do you anybody see what how can I get this or why this makes sense? When I did my follow the regularize leader right what is my algorithm I was doing argmin of summation f t of omega t starting from 1 to whatever let me be i i starting from 1 to t ok and this was an r omega. This is r omega which is I decide right this is not generated by the adversary this is totally in my control. I am going to treat this function as some f of omega that is a function which is generated in the 0th round ok. So, if I do this now I am basically saying that instead of starting my algorithm from round 1 I am going to start my algorithm from round 0 ok and then this this same step should hold for that also right. If this hold and what is this and in this f of omega is nothing, but r of omega earlier I was starting this algorithm, but now I am treating this to be r of omega to be another function which is generated in the 0th round. So, this whole thing should hold if I start from 0 everything right. Now, once you have this now do you see that this actually implies this you see it. So, now instead of now what I am going to treat is my sequence of function I have seen I am going to treat it as f 0 f 1 f 2 all the way up to f n where f 0 is my r function. So, just simply expand this right. So, you take it to be separate out t equals to 0 at term here then this is going to be 0 of omega 0 minus f of 0 minus what was this term here this was some u right whatever I am interested in my u and other terms I just keep like that t equals to 1 to n f t omega minus t equals to 1 to n f t of u. This whole thing is upper bounded by again I separate out the first term here that is going to be what f 0 of w 0 minus f 0 of w 1 right plus the remaining terms. So, just try to manipulate these two terms.  So, then that is all we need it or maybe take this on the other side. So, if you do that you get whatever we have here. So, if you just do this, but now what is f 0? f 0 is basically r function right.  So, if I have a regularizing function I will just get this yes we will assume that this is also it is f of 0 right like this. So, then only the whole of my function is convex. If each function is convex the sum is convex. So, I will also make it convex by assuming this r is also convex. So, now with this once we have like this earlier we have shown that my ok now with this what is the regret bound I am going to get ok. So, let us try to work out this regret bound for a given u. So, this is my regret interested and now I need to compute this value right ok. Let us compute this upper bound for my case that what is my case I want to now take this to be r equals to norm of this and my f t of omega to be omega to be z t ok. Now let us compute what happens to this portion. So, if I am going to take this.  So, now let us substitute this value what is this value this is 1 upon 2 eta w and what is this sorry this should be u here ok what is w 1 is going to be. So, when I have to do w 1. So, this is for t right if I am going to compute this at t equals to 1 in that case it is till t minus 1 right my regularized leader version. So, there is no term here when t equals to 1 what I will be interested is only on this and this term is this quantity what is the minimization of this quantity ok. So, one more thing I have forgot to tell is let us assume that my S is r d here the entire r d space ok the Euclidean space. So, because of this this can be S which I am taking to be r d. So, for t equals to 1 what is this quantity is going to be. So, this is simply saying r min of norm of over w right what is this quantity what is that minimizes this quantity 0. So, then this w 1 is 0. So, this term is going to be 0 I do not care about it ok. Now, let work out the remaining terms. So, what is this term this is going to be w z t and this term is going to be w z t plus 1 sorry. So, this is going to be w t computed at w this is w t plus 1 computed at z t is this correct. So, f t of this is f t of w t right and this is f t of w t plus 1. So, I have just substituted this value ok. Now, if you are going to simplify this further. So, I am just writing this as a compact form for these two things can I do that. So, because z t is common it is simply w t minus w t plus 1, but what I know about this. So, w t plus 1 I got it through w t right through gradient descent. What was the relation between w t and w t plus 1? So, how did we get w t plus 1 to be w t minus eta and that is z t right. So, can I substitute here? So, if I substitute here this quantity is simply z t sorry eta z t. So, after substituting this I can write it as norm of z t whole square yeah line of product between them is this correct. So, finally, what I got is the regret bound of on a particular n is above bounded by 2 eta norm of u square plus this quantity. Now, let us again use the same condition we used earlier right.  So, the gradients here z t denotes the gradient in round t right let us say they are all up like this. So, now, what is the bond I am going to get? So, this guy is also going to be l right because this is also u is coming from the same space as that no like. So, what is this u? So, this zeta are the gradients ok. What now I am assuming is these are the gradients and I am going to assume that they are upper bounded by some quantity l ok and what is this u? u is one of my reference point ok. So, let us assume that this reference points they are coming from a set u right I am also going to define this u to be such that u where all of this u's they are also bounded they are also coming from some bounded interval ok. So, only thing I am doing is instead of let us say instead of considering all the points like this I will consider some ball ok. So, one possibility to do this is let us all all what is this point x such that norm of x is. So, what does this set denote if I write it like this? So, assume dimension is 2 circle right of radius b in this case ok. So, I am just going to assume like even in this dimension I am going to consider some ball of radius b here. Now, because of that this guy is going to be some b plus I will have this eta term equal to eta and this guys I have been assumed to be upper bounded by l. So, this is going to be l. So, finally, what I end up with this is b by 2 eta plus l n eta n is coming because I am adding l for n terms. Now, we have seen this kind of bound earlier also right what are we are going to do now what we will do? So, eta is a parameter for us right this is a regularizing this is a parameter which we used in the regularizing function. So, how now it is up to us how you want to choose it can I choose it in some specific fashion here ok. Now, treat this upper bounded is a function of eta right is this a convex function in eta ok this part is linear in eta this part is 1 by eta is what ok. So, it is a convex function just you just do the second derivation 2 differentiation right 1 by eta square sorry this is going to be what if you differentiate this 1 by eta 2 twice you will see that it is having a positive look positive second derivative if eta is positive. So, if you now just differentiate it and try to find a point eta and plug it back and tell me what is the bound you are going to get. So, basically optimize this with respect to eta what is the bound you are going to get. So, if you are going to choose eta to be b l times square root 2 n it is in the denominator, but what is that this this l should be the numerator or denominator 2 ok just differentiate this and tell me what is that optimal value of eta here under root b by. Now, if you plug back in this what is the bound you are going to get I cannot tell me what is the final bound we are going to get suppose if I choose my eta specifically like this it is going to be what square root of ok just do this l n n square root b square root square root of square root b will be here then becomes b l n will be there and I will have 1 by square root 2 2 by 2 will you get this square root 2 b l n and what is this now because b and l n are constants which are chosen in this fashion and then this is like order square root n. So, you see that now even for the linear function linear loss function if I use my regularization in this fashion I will end up my follow the leader that is follow the regularize leader is going to give me a regret bound which is order square root n ok that means, this is going to this is giving me a sub linear regret ok fine. So, finally, so far looked into two types of convex function one is linear function and another is the quadratic function we have looked at, but what about the other convex functions is that we can do something about this ok. It so happens that studying other convex function is almost same as doing studying this linear functions here because of the property of a convex function which allows us to represent this convex function with a lower bound which has a linear term it. So, let us discuss that. So, linearization of convex function. So, how many of you know definition of a convex function what is a convex function this means f of x plus y lambda x plus equals to plus and what is lambda 0 to 1 right. So, we have already seen it in other class, but anyway let us try to again do it here. So, suppose let us say my function is like this and I have two points here one is at x and another at y. So, this is my f of x right and this is my f of y. So, where is lambda x plus 1 minus lambda y is going to lie? So, it is going to be somewhere in between right because lambda is between 0 and 1 and we are also assuming that this function f is defined on a convex set ok. So, if you take a linear combination of any two points of its domain it is also going to lie in the domain. So, this is going to be lambda x plus 1 minus lambda and this is let us say this is its function and what is this quantity here lambda times f of x 1 minus lambda f of y what is this? So, as you vary lambda I am going to I mean scrape through this line right like from here from for lambda equals to 0 I am going to be at here and lambda equals to 1 I am going to be at here. So, what is the saying? It is saying that my function value at this point that is what is this? This is f of lambda x plus 1 minus lambda y at this point is always going to be lesser than the line joining these two. So, this is the standard thing we know about convex function, but another interesting property about this convex function is to take any point let us take this point I will can come I can have a tangent at this point on this convex function which acts as a lower bound for this entire function that is maybe I should be drawing the slightly better one. So, let us say I take a point here another point I can draw a tangent. So, tangent here is the point where it touches my function only at this point ok and this. So, whatever this point and I can have a tangent here which is like a lower bound for my entire function like if I have a this if you look at any point the value on this line is going to be always smaller than the corresponding value on this function ok. So, in this case then let us say this point is some w. Now, what is the property of a convex function is? So, let us say let this be a convex set a function such that. So, it is saying that if f is my convex function. So, let us suppose s is a convex set then my function f is convex if and only if if it so happens that you take any point w at that point if I can come up with a another point z such that this relation holds. This is true for all u that is you tell me a point w I will be able to come up with a lower bound on my function f. So, this is my function f this is true for all u right that means, this is a lower bound on this function and and what is this lower bound? This lower bound is now defined in terms in terms of the value at that point w and also another point we are saying z which exists at that point z and if at all that exists then then that function must be convex ok and now this z here whatever we said exists this z is called sub gradient of f at w and it is denoted as del of. So, anybody has question about that z did not be unique all we are saying is there exists a z and that z could in fact, depend on the w the point at which you are looking at ok. And so, then if that such a z is going to be called as sub gradient of this function f at omega. So, we are not going to prove this this is like a standard result in convex theory of convex function and, but what we are going to do is we are going to explore this result to linearize my convex function ok. The way I have drawn my convex function here do you think this is this this is differentiable at every point right because it is smoothly changing at every point. So, in such case this if this such this sub gradients can be unique and if at all it is unique that is you tell me a w there exists only one particular z for this this relation hold then, but that particular z we are going to call it as a gradient of my function at point omega sorry w here ok. If if at all this property holds with a unique z then that z defines my gradient of my function f at that point and in that case if that is denote we are going to denote it as del f of omega. So, when we say this when this is uniquely defined the gradient when we say this is not uniquely it is satisfy for a unique z, but there are there could be possibilities we are in that case this del of f omega can be a set right because so, what we are saying this z is called sub gradient right and we are just denoting it. If there are more than once that satisfies that then all of that will be called sub gradients at omega and that will be denoted by this notation and whenever it is unique then we are just going to write it as with this capital del of f omega is that clear? So, what I want to say is if f is differentiable then this del f of omega is a single term and it denoted as f of omega ok. Now, let us see this why so, do you know can you come think of a case where the sub gradient can be a set that is it can have more than one elements in this ok. So, anything like whatever I have some let us take a convex function like this is differentiable at every point yes right this is smoothly changing. So, at any point I can have only one uniquely one unique tangent that is passing through that line ok, but now take is this a convex function right it holds our property right if you take anything and my function value is going to lie between these two that is the property of the convex lens. But now, suppose is this function differentiable at this point this function is going to not differentiable at this point right because when I approach it from the right it has a negative slope and when I approach it from the positive side it is having a positive slope. So, at this point it is not going to have a it cannot be differentiable, but is there a z here? So, let us take this is the one particular w which I am interested in. This relation can be satisfied by a unique z or there could be multiple z that could satisfy this relation. So, it so happens that in this case there could be multiple z that could be satisfying this one possibility is this one possibility is this maybe you can think of many lines which are all kind of touching this point only at one point and they are like a tangent here they are also lower bound to this function ok and, but there is no unique line there are so many lines right and each of this lines can corresponds to one z right. So, in this case at this point w my delta f omega can have multiple points. So, here the sub sub gradient is a set it is not a single term, but if you look into at this point here my function is differentiable. So, in that case my sub gradient will have a single term which I am going to call it as simply the gradient of my function at that point ok. So, now what we basically shown how can we exploit this right.  So, earlier ok let me write it in terms of this we earlier we had this function right this is my regret. Now take a particular t and look at the difference f of omega t minus f t of omega. If my f t is a convex function I am going to appeal to this function here then what I can do then this is going to be what is this true if my function is convex can I upper bound this by like this using this relation. So, what I am doing is in this case I am looking at my sub gradients at w t my points w t and this z t is the gradient of my function f at the point w t ok. So, if then I know that this relation has to hold for some z t ok and if my if my function is convex and let us say it is differentiable everywhere then this z t I can as well depressed by f of t right. So, what we have done basically is we have given here f is any arbitrary convex function right. What we have done is we have replaced we have upper bounded its regret by so, what is this this is nothing, but if I reorganize this this is nothing, but u of z t minus. So, let us see what is this if I want f of t if it goes on the other side right. It should be w t minus u right just verify this this is not u minus w t. So, if you write this then this is w t z t minus u of z t. So, what we have basically now see so ok this could be an arbitrary convex function, but if you can linearize it I can upper bounded in terms of a linear function where my z t is the sub gradient of my function at that point w t. So, once I have this what all the things I have done here for my linear functions I can appeal to this and get a bound here right. So, that will give me a bound here, but this is already upper bound on my regret. So, that bound also holds on this regret ok. So, with this I just want to write this pseudo code then we will move then what we have finally ended up with is what we are going to call it as online gradient descent algorithm. And how does it work? It takes a parameter theta and then it initialize w 1 is 0 because w 1 I do not have any control and then the update rule is w t plus 1 is equal to w t minus theta z t and what is z t here? z t is the gradient of my function at w t. So, this is what we are going to we have simplified our follow the regularize leader for the specific case of L 2 regularization to be to be this online gradient descent algorithm. So, we already discussed why this is gradient descent right ok. So, let us stop here.


there we go and got that one shared and this one shared hold on trying to run like three different rooms at one time when we finally broadcast so it's always got a little bit a little bit of a challenge all right there we go okay so anyway we've got a couple of good little movers we're doing pretty good on the pre-market on the UAVS stock started at 950 it almost hit 12 oh and about maybe 15 minutes I don't know where it came from but it was like a late starter which you could see on you could see she started around eight o'clock a little bit around eight which usually is where program trading kicks in remember eight eight thirty nine and then obviously the 930 those are the those are the biggest times when we trade going into it but but it's on the watch list and we're gonna go over a couple of things I want to talk about some stocks that we traded yesterday but yesterday just want to give everyone a little heads up we did decide Burexpo so we're in the process of editing them cutting them sharing with you will be on our YouTube channel look forward to seeing all of you that missed them let me have some great presenters we talked about psychology we talked about futures we talked about day trading five great presenters and you know when you guys get the opportunity to go out there definitely watch them I think you guys can really enjoy it and everyone it's kind of long long day five days to sit in there but you know you really want to take your time to look at it so remember we do it every month here at Cybertrain University the Cyberexpo here every single month and and it's and we got some great ones coming up next month so don't miss that also if you're a student at Cybertrain University class is today at 1030 okay so don't miss class we purposely do it during the middle of the day because we don't want you to miss the morning hours that's the best time to trade so don't forget about that also what is today today is Friday okay I hate Fridays I hate Fridays because right now I'm 50-50 okay I made money on the fifth I lost money on the 12th today's the 19th you know but so funny I look on my calendar you know it's like I'm so consistent every Tuesday Wednesday and Thursday Mondays or Fridays at 50-50 so not that I'm not going to trade just be very conservative don't try to get too cocky on Fridays no way likes to come out when news going into the weekend and all that good stuff says some big catastrophe happens all right so it's be very very quick to make your money and remember what you always taught in class you take the profit boom just go out there and and put your limit orders out immediately remember in phase 3 it's one of the biggest things I taught you 80% of your winning trades are going to be going out as limit orders now another thing I did which I thought a lot of people I know some of you here on YouTube and Facebook don't really see it as much but you know but if you saw the video yesterday a lot of your students in here know about it being in the cyber group room but like I was doing examples and explaining about direct access and online brokers people are so amazed that they couldn't believe that what is really a direct access broker when you go out there and I'm using an ECN I'm posting out there you got to watch it I did a video on it too on YouTube you got to understand because how in the hell are you going to be competitive if you don't have the right tools it's just it's you wasting your time you got the greatest job opportunity in the world and like every time I listen to this dumb $1400 you know stimulus package you know stimulus check that's coming these people why you can make it do it here what do you need that for okay you could do it right here yourself every day and that's the beauty that's why a lot of you guys are here so let me go over a couple of stocks that we did trade yesterday because there were a few good ones that did make a move I want to talk about the JFU trade all right so this one fix my time sheet so this one right here a little bit a little further all right this one had a really nice move yesterday and she had a really big nice decent pop going in the afternoon you can see it went from $1.80 to about 360 she's got some great volume she traded already five million shares already this morning this stock you know nice penny spread if you're a beginner if you're an advanced trade you could trade several thousand if you're a beginner trader you could trade a couple hundred it's exactly what you're looking for but it was a nice nice move was comfortable good learning stock and it was also a good stock to trade him it with a nice little trend on the upside what else we had we had the of the SM drop that's right that one just took a crap shoot listen the stock has been doing great okay stock was $2 in November I told you that the energy stocks were gonna do great and obviously I don't know if this has anything to do with happening Texas or whatever you know with with with the energy stocks but you could see that this thing has been on a great one I've been a big fan of it but how do you become a very good swing trader got to know what happens over the course of the day and if you saw the writing on the wall was just happening yesterday probably should have got taken out you should have got out sometime yesterday because that was a pretty substantial drop remember whatever goes up comes down twice as fast riot all those I mean listen these things are going up way too fast way too fast and I mean I got out of my riot and I think I told Josh that in the room but you know what I got out right after hours right when I saw the market starting to crash I think I got out would again somewhere around like I think like 71 or 71 72 something like that I didn't get out the 85 I got out right here when it started like backing off right I got right around here the day before and then obviously Josh like ages you go to 85 in the after hours oh my god thanks for telling me listen I money's money at the end of the day I mean this thing is going up unbelievably the big thing you have to learn is you could always buy it back cheaper okay the bad thing about being a day trader is that you're too conservative I know I keep saying that word but people think that being a day trader is very speculative it's really not you know you focus on the day's pay but what to me speculative is swing trading and Jesus options is very speculative unless you're selling options but Ryan and Mara also you're basically following their upper they're up again this morning doing okay they're coming back a little bit but those are really the big two big ones out there what else did we have the pot stocks to I mean TRL why I mean that's been our one of our favorites I got out of it I actually was money on that trade I sold it made money on it bought it back and then I lost on my second try and I got out I got out yesterday right around 32 because I thought it was gonna get a little bit of a bounce you could see had some nice support levels right around here around the 30s when I saw the stock took a big hit from 68 down to 35 I'm like you're down 50% I'm like you know what it's holding here you could see like those couple of candlesticks right there so you don't start build some stability to there and I'm like you know what the hell let me just get out of it so you know they want a way way way too fast and if you haven't learned that from GameStop then you know what you're never gonna learn but remember one thing I always say this to everybody and please don't take this the bad way losing money is actually a good thing you know why because you know why you did it you won't do it again winning could be a horrible thing if you don't know why you made money how are you gonna do it again okay the worst thing can happen I tell everybody is that you make money and trading in the beginning because then you think it's so easy and then you start doubling and tripling down you're gonna lose in trading and you gotta know how to lose properly because the winners take care of themselves the losers you got to watch out for always always I you know go by those words to all my students alright so let's go through the watch list because there are a couple of stocks here not the greatest I'll be honest with you but there are a couple of them that are moving I want to start off with this VCNX I found this this morning at a huge pop right at eight o'clock you went from three dollars all the way to six you know what now looking at it she she dropped down to four I'm like wow that thing took a nasty hit a lot of algorithms though stock trading look almost 20 million shares already could you imagine like who is trading this in the in the pre-market algorithms you could see right there on the right hand side you could see all those orders out there getting filled right there but the stock has obviously got some resistance levels right around that 26 you see 11 thousand orders trading on this thing but just be careful just don't let these files stocks fool you because this is probably like a category 3 already minimum alright so this one is pretty decent we keep an eye on this one watch another one gapped up pretty big up 70% 17 million shares the stock is kind of creeping up little by little you could see here on 845 she was just around about 7 575 cents she's already at 635 but once again algo stock how do I know that look at all those orders on the right hand side big big orders now there is a 23,000 share order right now at 650 I'd like to see if it's gonna get it's gonna break that you know remember what somebody bought a very good comment on for yesterday they said that hold on let me take this out right here they brought up a very good comment regarding how you know these orders getting executed it's the time and sales window on the right you know that's how you know if these orders are getting done you gotta use a time and sales I'm a tape reader that's what we teach at Cybertry University you gotta see these orders get filled but it looks like she's getting close to that she could see her right here right here you see that order right there on the right that's a pretty big decent order and you can see like it kind of tested up there and it came right back down yeah you see that kind of hidden came right back down so I got looks like he's holding it up let me go to a couple more because we're running out of time here JFU is another one that showed up on the watch list of a lot 18% 5 million shares not an algo stock but there is a shitload of shares looking to be sold right there I could see it at $3 and 30 cents $3 and 20 cents I mean 325 you're talking literally about maybe 50,000 shares out there okay so you could see it not really trying to get there you know but nice spread it doesn't look like it's going crazy right now looks like a comfortable stock I want to put down the watch list JMP I want to see how that works out JMP also not moving to super crazy right now you can see it doesn't look like a Christmas light on on the level to but she is trending down now she was up really nicely 81% maybe people wake up in the morning like crap you know what I could have gotten 11 now it's at 9 I mean when it was 11 it was probably up 130% maybe people will start taking profits but it's building some support levels here from after hours from yesterday so looking over there on the level 3 see a little bit more buyers but the tier size of very very small okay when there's tier size is very small that doesn't mean you trade big once again my whole thing I tell everybody what are my mottos trade buy less shares trade more that's how you learn we already know about the UA the UA the UAVS and SOS has been fun by the way this stock I mean this stock has been if you've been here every morning it's been a lot of fun stock was at $4 a couple days ago damn thing went all the way up to about 15 she started she took a big little hit yesterday she's starting to go back up if you if you're a student at Cybertrain University taught you how to do your journals so make sure you learn your journals review them review what your instructors you know here at Cybertrain University but the purpose of doing that is you want to know I tell you this you're gonna trade the stock again so how did you do on it what did it did you get burnt on it did you do very well on it did have iceberg orders you see whatever you know so this stock obviously been a play for us for a while all right now anything else I'm missing that I did not call out anything else ETFs I mean I really we don't I don't really miss around ETFs too much the problem with ETFs is that it's very hard to see iceberg orders out there that's one of the biggest problems why we don't really do that and that's just the way I was trained not that you can't make money on it also ETFs the problem ETFs you know they only really move on a very volatile market like yesterday was a pretty decent day for ETFs your market was almost down like 300 points you know it came back a little bit what are we what are we doing now we're up a little bit nothing crazy you know alright anything else I'm missing right ETFs and also ETFs are good for swing trading you know I mean that's enough you can't really day trade ETFs and like I said unless it's a very volatile day okay all right anyone else we miss anything all right so guys don't forget today's class don't miss class today if you're a student here at Cyber University if you want to know more about it go out there and talk to Education Advisor if you're watching us on YouTube Facebook live we'll be back again live at 230 okay doing training afternoon and remember everyone today is Friday if you've been doing your journals you've been watching it you use that Excel spreadsheet just be careful on a Friday don't get too cocky okay pick your spots put your limit orders out there put your limit orders to sell and just focus on the days pay and you know what crap if you hit gold the thing takes off on you congratulations but don't think it's a you know that's not usually how things work always on a Friday's and Monday's all right be safe see you back here 230 and we'll start commentary here in the trading room like I said when the market opens up and if you're watching us on YouTube Facebook live for the first time just like us friend us hit that little alarm of that we have on the bottom so you can get the alerts because we're always doing training videos for you guys to watch and also really on YouTube Facebook I'm sorry not our trading room all right be safe everybody we'll be back


So, hey everyone, I'm Avinash Rao, I work with Cognizant, I'm here to present a case study, we call it the double helix model for lean agile. It's what we're doing with large projects, 70 people, where we're moving from waterfall to agile lean model. And basically what I want to cover is, we'll start off with a few war stories. And then some of these war stories really will give you a sense of the thinking that has gone behind what we are trying to accomplish in the project, the perspective that we're coming in with. Then we talk a little bit about how we've implemented lean agile and then we look at the double helix model and the results and the reflections that have come as a result of this model. So those who were in Scrum Bangalore two weeks ago have heard this story. So apologies, but I think it really makes the point that I want to. So I'm at Taekwondo Greenbelt and I started learning three years ago and in London I went in into my Taekwondo class for the first time and there were two of us who were the real oldies in the group, meaning we were over 30 because everyone else was in college or in high school. And so my sparring partner on day one was a motorcycle mechanic. He had this guy's black hair, but he looked a little bit like that, but he had blonde hair, blonde ponytail and he was this much taller, biceps, tattoos all over. And so the Taekwondo instructor comes in and says, Avinash, now throw Ian on the floor. Ian is going to try and choke you. What you need to do is extricate yourself and throw Ian on the floor. Simple, right? You have 15 minutes. And so I spent the next 15 minutes like this and I couldn't move him. He was clearly heavier. He was clearly stronger than I was and there's just no way that I could extricate myself for get through this guy on the floor, right? So 15 minutes later, the instructor decides that I've had enough and he comes over and he says, You know what you need to do? There aren't any muscles here. So everybody is what you need to do is he has your hands here, pinch, twist, trip and he'll fall down. And 30 minutes later, that's exactly what I was doing. So the point I'm trying to make here is that some of the challenges that face us are hard work problems. We can work very hard and we can, you know, go to the gym for two years and hopefully after that we might be able to move this problem. But a much better way to solve these problems are using techniques and techniques give us results which are much better, much faster for a certain type of problem. And I think too much of...


Hi my name is David Rosales. I'm the pastor of Calvary Chapel of the Chino Valley, California. Recently a friend of mine shared something with me that I thought I would share with you. In 1942, C.S. Lewis wrote the book, The Devil's Letter to His Nephew. It was a book containing a fictitious conversation between the devil and obviously his nephew. This is part of what Lewis wrote and it certainly is prophetic.  Because of fear. Oh yes, an excellent strategy. Old and always current. But what were they afraid of? Fear of being tortured? Fear of war? Fear of hunger? No. Fear of getting sick. But then no one else got sick at the time? No. Yes, they were sick. I'm sorry. Nobody else was dying? Yes, they died. But there was no cure for the disease? There was. Then I don't understand. Since no one else believed and taught about eternal life and eternal death, they thought they had only that life and they clung to it with all their strength, even if it cost them their affection. They did not hug or greet each other. They had no human contact for days and days, their money. They lost their jobs, spent all their savings and still thought themselves lucky to be prevented from earning their bread, their intelligence. One day the press said one thing, the next day it contradicted itself and still they believed it all. Their freedom. They did not leave the house, did not walk, did not visit relatives. It was a big concentration camp for voluntary prisoners. They accepted everything, everything, as long as they could overcome their miserable lives one more day. They no longer had the slightest idea that he and only he is the one who gives life and ends it. It was like that, as easy as it had ever been. Hopefully you'll know that I'm not saying we all should ignore this terrible disease. We must exercise wisdom and prudence and not tempt the Lord our God. Still, we need to be aware of how easily we have given up our freedoms. We're living in a time when you're constantly being fed views intended to cause us fear. Our freedoms have slowly been removed from us. Many are yielding them willingly. May we be wise, but at the same time, may we exercise faith in God and hope. I for one am very suspicious of how many pressures have been placed upon our shoulders from a bogus impeachment, a false accusation against a Supreme Court nominee, a shutdown of our entire country through a virus, loss of jobs, anger in the streets, fear has taken root in many hearts. As cynical as they may be to some, if Mr. Biden wins, I would not be surprised if everything settles down after the presidential election. If he doesn't win, chaos will grow worse. The foundations of our nation are under attack and we must be awake and aware. It's time for us to use the armor God has given to us for times such as this. Gird your ways with truth. Put on the breastplate of righteousness. Wear the shoes of the gospel. Take the shield of faith. Take the helmet of salvation. Draw the sword of the spirit. Pray always. God is not given to us the spirit of fear. Put your trust in him. Hold tightly to him. Paul said to take up the whole armor of God that you may be able to withstand in the evil day and having done all, to stand. Stand as a victor, feet planted without yielding. The weapons of our warfare are mighty in God. Our enemy knows this time is short. Do not yield to his propaganda. Stand fast in the faith. Be brave. Be strong. You'll see the salvation of the Lord. No weapon formed against us will prosper. So hold on. Behold what God can do. Stay in the Word. Stay in prayer. Wait on God. If your church is open and holding services, perhaps you should go. Get strengthened. For those who are part of our fellowship, I hope to see you this Wednesday or next Sunday. And remember this. We love you and Jesus does too. This is David Rosales, pastor of Calvary Chapel of the Chino Valley, California.


No, you listen. When I sent you to Terminator to get a new moon to replace ours that exploded I assume that you would be clever enough to get the four Giants that stop it from crashing into the world. They're kind of a package deal! When you work for the Foundation, it's important you take the bad with the good. For instance, it is a tragedy, obviously, that we lost this woman and her two children due to this spatial anomaly. But we did get a lot of good data, so, you know, it's all about perspective. The moment SCPs are actually real, I'm heading straight to the containment cell for SCP-3092, or melt into a ball of flash because day will have broken, but hey, let's pretend that part didn't happen. SCP-3092 were some of the most adorable SCPs that don't get any love from the fans.  These stuffed gorillas are highly skilled at stealth and gorilla warfare. The article is called gorilla warfare. That's the joke. They are constantly trying to breach containment, but they never release dangerous SCPs or do anything that would cause actual damage. They resort to slingshots and graffiti and glitter bombs and perching a bucket of water above a partially open door. They used to employ tripwires in their arsenal, but they stopped that after they accidentally fractured a researcher's collar bone, and they never did it again because they felt bad. Please be real, I want to be your friend! What are the origins of this? Where did this come from? Hi there, Dr. Sherman. TikToks was an SCP researcher at your service. The SCP Foundation began simply as a creepypasta posted on 4chan. The mystery behind this creepypasta masquerading as a scientific research document intrigued a lot of people and inspired them to write their own SCPs. Once there were enough of them, we started our own website. SCPWiki.com is our current main site where you can learn all about the SCPs straight from the authors. Over the years, we've accrued thousands of authors, written tens of thousands of stories in a dozen languages. Because there are so many people involved in creating SCP content, it only made sense for the founders to release all of our works via the Creative Commons 3.0 share-like license. This has caused the SCP community to kind of shape itself like a wheel where the writing community is the heart of it, but then you have the spokes of the indie films and the YouTubers and the Game Devs and the TikToker, Cosplayer, Artists and all that. I hope this has helped. Leave me any other questions you have in the comments. Secure. Contain. Protect. Intox. Apologies, but if I remember correctly, it seems like the Mobile Task Force are the ones who die in every single exploration log. Not to self. Higher, taller recruits. A children's squiggle? I have eight doctorates, and you send me out here looking for a children's squiggle? Oh my... Oh, I found it, and the picture was more accurate than I ever wanted it to be. Ah! Ha! That'll do, donkey. That'll do. Donkey, I said that'll do. That'll do, donkey. That's enough, donkey! On top of that, you forgot to empty the Break Room dishwasher again. It is October, that's right. Yeah, and if you don't let him out for his walk, he gets cranky! Why do men sneeze so loud? Like, why are y'all gotta yell? The other men won't like me telling you this, but you need to know the truth. Millenia ago, just after the fall of the dinosaurs, a battle was waged. Hold on there, buddy. That information has been classified by the O5 Council.  Scroll along. Bye-bye. Bye-bye now. Bye-bye. My partner Goose is trying to do a TikTok transition in real life, and it's not working because this is not the internet, and so transitions do not exist. She is not an SCP. This is a sad day for all of us. Hey, Max? Yes, Groovy? Is Rick Astley an SCP? Hello, sir? Is Rick Astley an SCP? If not, why isn't he? Obviously, you think a non-anomalous entity would be able to successfully never give you up? Humans are fragile. Rick Astley is eternal. Sing with us! Grab me a new moon, cause our old one just blew up. Termin' us got time loops, nab theirs, they won't miss it much.  Agent Link, that means he does. I, uh, think. Those giants, too, cause we don't want it to crash. Leave behind that Skull Kid in that creepy, spiky mask.  And today, we'll be making ghosts. Yes! Murder! We'll start by tracing our ghosts on this piece of paper. Halloween.  Not bad. Decent. Mediocre. Six out of ten at best. All I'm seeing are good pumpkins. I do not see any great pumpkin. Hey, I only ride in carriages! Begin test. Skull Kid, go for a soda right now. Endured fetch quest today, nothing special. Keep it by the book. You. Wait. What is it? What are you doing over there? What is it doing? Did it just? Gee, thanks. Did it just lie? Sure looks. Oh my God, this is wildly new behavior. It's learned. It's learned how to pray. You found the perfect containment method here, boys and girls. We will use my large- Not a chance! Absolutely A-B-C-D-E-F- Yes, these are my office hours. Nope! Nope, we're not doing that today. Oh, no, no, no. You. You'll go back to your room, young man. I don't want to see you in here, not at all one bit. And if you say one word about my car's extended warranty, I know there was a lot of complaining and threats against the lives of security staff when you all forced me on vacation, but really, I am grateful. It was very necessary for me to recover. Oh my. I'm going to need a bigger cat-made waifu outfit. Just, we'll see if he does bounce. Nope. Oh Lord. Dr. Bright is not allowed to institute no bones day at the foundation. And so today I'm 11 a.m. already, huh? When this lumbetic workout program was implemented in the first place, I admit I was skeptical. I mean, you've seen the weight loss commercial, so you can't trust them. Workout without thinking about it. But this actually worked out pretty well. I mean, barring a few containment breaches where squats are not conducive to containment, I've gotten a lot better at running because of all the workout, and my posterior has gone from two pancakes to fat, and that is with a pH, so I'm pretty proud of it. Unfortunately for the entity known as SCP Redacted, or Mom's Spaghetti as its nickname, if it is vomited onto any surface that is not a sweater, it finds it very difficult to maintain its life functions. It can wander a short distance if a sweater is available, but more than likely it's going to become non-anomalous pasta. Dr. Sherman's in a dating sim. I think I should retire. Just kidding, let's make more games and a movie! Also pick up Doki Doki Anomaly, it looks pretty dope. I can read minds. No, you can't. Yes, I can. No, this isn't really happening. It is really happening. Think of a number, uh, number seven. Seven. Uh, purple. Purple. My worst nightmare. If a bottom is riding a bicycle. What's your name? Wednesday. What's yours? I don't wanna tell you. Hello, Peggy. Hey there, Site 42 fam. It's come to my attention that not a lot of people know that we have a Site 42 SCP merch store. We've commissioned art from multiple artists to make SCP-related merch, and we've got stickers for the Site 42 channel. And so if you want some SCP merch, as well as helping support the Site 42 channel, teespring.com slash stores slash Site 42. Thank you in advance, and cheers.


It's time for a giant cranial nerve review. Doesn't that make you excited? You better say yes. Here's what the review is going to look like.  You getting the idea?  Mmm. Look at that lovely medulla oblongata now.  And this time, I'm going to color code everybody. And I'm going to color code it by what kind of fibers are found in there.  I'm going to do sensory, motor, or both. And then I'm going to go back and mark the ones that carry visceral motor fibers. Are you ready? Remember our little antenna out here in front. That was cranial nerve number one.  That's cranial nerve number two. And then we have, whoa, I almost got so excited. I did not change my color. We have two little fellers coming out of the top of the pons. And this is cranial nerve number three, all motor. Cranial nerve number four is also a motor guy. Cranial nerve number five. Cranial nerve number five is the big trigeminal. That was fast. Whoa. Okay, something like that. And why did I make three little arms? Fingers, three fingers, three fingers. That's why I did it. Cranial nerve number, oops, I lost track of my numbering. Didn't die. Cranial nerve number four and five and six is also just motor. Just motor. That guy's not cool. Seven, what do you know about seven? Seven is definitely a boather. Seven is coming out here. Eight, eight, oops, dang it, I did it again. Seven, who was seven? Facial. Eight, vestibulococlear, all sensory. Coming out about her. Nine, nine is a boather. Ten is a boather. Look at me go. Nine, ten. Are you looking at me go? Dude, really, please go. I know that's not really what you're thinking. Who's this? Oops, dang it. Why did I erase number 11? Because it has to be red. Number 11 is accessory. Remember moving neck muscles because it is all motor and it's an accessorizer, right? You feel totally accessorized right now. And then 12. 12 was hypoglossal and that was just doing tongue. Remember it comes back in here and does that weird thing. There you go. That's a nice little picture. If you can visualize all of your colors, you could totally rock that picture right there. Are we finished? No, because we actually have to also mark that number two is carrying visceral motor fibers. And remember that was three. Oculomotor makes you your pupils constrict. Facial makes you salivate and cry. Glossopharyngeal makes you salivate. And Vegas does everything else, everything. Wow. Cranial nerves, holy nerves. You just did all the spinal nerves. Plexuses and you did all the cranial nerves. That's a lot of nerves and now the exciting thing is, I hope you feel excited right now. The exciting thing is we can do pathways and we have highways to travel on. So we're going to go out. We're going to get sensory information in. We're going to be able to process it. We know where we're going to process it in our central nervous system. We can know the highway that we're going to travel to get out to make stuff happen out there.  Unless you think pop stars are cooler and then you can be a pop star. I, on the other hand, am going to go to bed now.