Thank you very much Alina. That was happening here. Sorry. One moment. Yes, thank you very much Alina. So I will be talking about the optimality of the logarithmic upper bound step. I will make clear what I mean by that. And this is, this was a joint project with a research team. We got together in Oaxaca in Mexico. And the team was composed of, well, the surviving members of the team are myself, Emmanuel Carnado, Andres Chirre, and Julian Mejia Cordero. All right. So what are we talking about? Can everybody read my handwriting? It's not worse than on a board. Okay. So an upper bound sieve of Selberg type, or not to be so personalistic, a quadratic upper bound sieve. For me, it's just a choice of a function from the natural numbers to the reals with rho of D equals one for D in a certain initial interval, and rho of D equals zero for D a little bit larger. So you have this function is one at first, some sort of thing, D1, D2. Here it would be zero. And here anything can happen. Here you're free to choose it however you want. All right. And it could be that, so D1 and D2 are given to us as parameters. So we could have to deal with the case of D1 equals one. It's one of the most common ones. It's called the one parameter case, logically enough. All right. So, well, it's a choice of function, but what's the point of choosing that function? The point is to make the following sum s rho, which is the sum for all positive integers up to big n, sum of the following squared. It's the square of the sum over the divisor, z of n of mu of D rho of D, where mu, of course, is the previous function. And the point is to make these as small as possible, given D1 and D2. Okay. And here I should explain why we care. Sorry, a little. It's up here. Okay. Much better. See, I'm having a little bit of a little bit of a tiny upward inconvenience. Okay. Very good. All right. So why do we care? Let me talk then a tiny bit about applications. So there's a classical use of sieves. That's when you are just trying to estimate the number of integers excluded from certain that do not fall. So you have, you're talking about the integers that are forbidden from falling into certain congruence classes, model of p for many p. So congruence class we may forbid is we could forbid the congruence class zero, say. That is, you could be counting a number of integers that are not divisible by any of many prime numbers. Actually stated things. So it's the, it is actually the class zero that we are excluding. I could make things a bit more general, but so numbers because what happens here? You see when, yeah, so evidently, this square being a square is always on zero. And whenever you have that n is a prime number bigger than the two, you're going to have that it has, yes, it, it cannot have any prime device or it's other than itself. So you cannot have any prime device or smaller than the two. It cannot have any device or smaller than big two is except for one. So the only term here is going to be one. And so whenever you have a prime n that n is prime, this inner sum is one. It's a prime bigger than the two. And in general, it's going to be at the zero. So this is an upper bound on the number of prime numbers. That's a simplest case. And you could state more generally. So a classical early application was an upper bound on the number of twin primes. Of course, we believe that there are infinitely many prime numbers. We don't actually know that. But what already Brun managed to show by a different sieve in the early 20th century was that there cannot be so many. I mean, the twin primes must be fairly thin on the ground. So this will be, and that's a very classical application. It's an upper bound on the number again, such that n plus two is also prime. This is old hat for many of you. If not, it doesn't matter. But let me tell you, before I really get started on the main part of my talk, let me tell you about some interesting recent applications of this kind of sieve. By recent, I mean things that have been going on for the last 20 years and that are really an active field of research. So there's what people call enveloping sieves. It's an old name. It's not a perfect name because it's really the, it's not that there is a specific sieve called an enveloping sieve. It's rather that sieves can be used in the mode of enveloping sieves, so to speak. That's what you're not using this sum here, just as an upper bound or to count. Rather, this is used as a weight. So sieves have well-known limitations, so called parity problem that sieves have when used on their own. But then you may want to combine sieves with other powerful techniques. What you do then is you use this weight, which is a very beautiful weight, especially when D2 is not too large. It's distributed.  So you use it as a weight to buy ascent towards primes and then you work further using other techniques. So you find this, this used to be known to the specialist, some specialist, so you find this in the works of Julian Ramirez. And then the wider number theoretical public, I think, really became aware of this use of the sort of technique with the work of Colson Vincent Gilderim. They can further buy polymaths, main art, and so forth. I'm not being mean to Jean. It's just that Jean's innovation is more or less orthogonal to this. And we can also find this in Greenteau and so forth. Yeah. Personally, I came into the subject in a somewhat different way. It's also that sieves like these can appear in a way that is completely uninvited, so to speak.  And then you cause a Schwartz because that's what we do have of the time.  And you can do that, though, you know, this is not as big as it looks. This is all size about a constant sense. But then you can ask yourself, well, what if I'm a bit clever and I use a smooth or somebody tells you why, but why are you not smoothing your sums? Always smooth. So you use the most wide once identity and hope that improvements use a smooth once identity and she Schwartz and you are dealt, you are led to some of these four. So row will be some smooth weight sort of seeing that cropped up in my work on three primes, especially in the second version. And also this, this has cropped up in other ways. So see also, for instance, Graham's work on Linux, Linux constant. So what I'm saying is that the quadratic family of seeds is particularly interesting because it's, it's actually quite useful for what when you give certain not completely traditional users to see and it's also naturally enough that it crops up on its own naturally while you are doing other things. It's a sum of squares that does show up spontaneously. All right. Well, let's go back to the problem of making S row as small as possible. So let's look at the, at the case of one parameter first, the one equals one. Then we actually know exactly what is a row for which M row. No, sorry, I'm getting a bit ahead of myself because I defined S row, not M row. Where is my page one? One, second, not a page one. All right. So before I go to D one or M row, let me, so we were at this stage of minimizing. There's a further reason why we care about this particular kind of sieve, namely sieves are usually limited. I mean, if your function, you can define other sieves that are not quadratic in terms of functions. Then you have the sieve support, which in this case goes from one to D two. And if you're, if the square of your seed, this of your, of the length of your sieve support, D two, is greater than began, then you're usually stuck.  There's nothing you can do. That's not the case for this kind of sieve, at least not necessarily. But let us focus for now. So the simple, it's very simple to see that you can have this expression where M, you can gain a power of love here, usually D one, D two. So we want to minimize M row also possible. And this is important to give that bounce that are of the same order or greater than N. And that's, that's actually important. Something important about quadratic sieves. Then you don't have a reduction to M row. You have a reduction to something that is similar to M row. But for now, let us focus on M row. Sometimes your, the sound is not very good. It's maybe you touch the microphone or the microphone. Okay. Let me see. Because sometimes it goes that the sound goes a bit soft. Now it's okay. Now it's okay to sound. Maybe that I'm getting away from the microphone. Let me raise the microphone perhaps so that the distance of the microphone is constant. Okay. Thank you so much. Yes. I think that might be better. Is this good now or not? It's good. Yes. Thank you. All right. So we want to minimize M row. Very good. So let's consider the one parameter case first 81 equals one. Then M row is minimal. So it's like this. That's exactly this. It's gamma plus So far so good. Well, this is even negative. And that's very nice. So you might think end of story, at least for the one parameter case. But for many applications, they are taking a reason why row celibate, which actually I haven't written down, is not so good because it is, it is not anything continuous or monotonic. It jumps all over the place. You can write, yeah, asymptotically, it's a product of a continuous factor and an arithmetical factor. You can always express it in that way. And by arithmetical I mean that row of n always depends on the visibility properties of n. So it's not going to have any sort of nice plot. And for any, from any purposes, it is better to work with row monotonic of a given continuous age. So it's basically scaling. It's naturally now from this context. So say row of t, row h of t, we're going to do it in this way. Load it to t, load it, load it to 1, t1. Simply so that, I mean, here, this is just a logarithmic rescaling. And here we're bringing it to the same, to the right scale. But do I mean by the right scale? So h is going to be a function on the reals with h of x equals zero. So what you have is that when t is less than d1, this one, this argument is going to be bigger than one. And so you have h, that h of x has to be one. That's what we wanted for t less than d1. This thing is one. For t greater than d2, we're going to have that this is negative. And so h of that will be z again as we want. So row selberg is not of this form. There is one particular choice of h that has been started plenty. So it's the choice, which is just, you know, so it's going to be zero here, it's going to be one here, and it's the simplest choice. It's just a linear function here. Zero x1, zero for x less than zero, one for x greater than one. All right, so this was studied first by Barban and Behoff. I have some names I think on the first slide. Important, they are the first ones to really study this in detail. Motohashi, Graham, the same time period. So the basic theory was really developed in the late 60s to early 80s. And then the subject seems to have plain moral assortment. And what you have later is much later. It kind of suffocates retaken as a spectacular application. It also includes yielding. It's falling close on the first paper. Again, pincis contribution was, yeah, actually does have more to do with these and junks, but polymath, polymath, may not, there's also recent paper by Yvette Wanny. All right, but what you have here is really, as I said, generalizations and some very beautiful applications. Yeah, you also find it in the work of Ian Tao, as I mentioned before. But I would say our feeling was that, yeah, the study of the basic theory had been left partly incomplete. It had been cultivated in the 70s, up to the early 80s. And then there were some gaps there that had, you know, and the recent work had not really had not been its task to fill those gaps and really complete the theory. So what was the state of the theory? The one parameter case, the main term is the same as in Selberg. So the, the, yeah, so since Selberg cv is optimal, then this has to be the optimal main term. So it's a thesis of one of my students. So figure out the second term is 0.6071. Pricky to give a close expression, right, but he managed to show that it is the actual value or that it lies between the, this first five digits are correct. D1 and D2, the main term is 1 over log D2, D1, and it is optimal. Yeah, whether it was known that it's the main, let's say that it was, it was known that the main term was optimal, though it's a little bit hard to, to pinpoint exactly where that's stated. And what I had shown, say this came up while preparing the second version of the three prime proof. So I had worked out, you know, the second order term for the case that we will not be discussing today so much, the case of D2 greater than square root of n. Then the analysis gets trickier in some ways, but in the end you reduce things to expressions that are not the same but are analogous to the ones we will consider. And here, again, the second order term is negative. So I'll tell you in a moment what the second order term here is at any rate. So what about the order term? So this has been, as I said, partially worked out very recently. So the lower order terms were not known and they were partly worked out in, you know, one of the iterations of the infinite three prime manuscript, mostly available on my website by now, end of advertisement, and end of apology for taking so long at any rate. What about lower order terms? Those had been, those were partly worked out but only very recently, they were not known in the 70s and 80s and I haven't seen them anywhere. And also the question is what, so are they optimal, you know, what are the optimal values and when do you get the optimal values? All right, so let me state the main theorem per take. So the one parameter case would be for D1 equals 1, the quantity we want to minimize is, you know, m rho. So let's do it from rho H0. So this confirms Sebastian's results. We are following different methods. So he was following the same kind of methods. So these two results here on the left side, the results from just a year ago or a couple of years ago were following a real analytic approach. We follow a more complex analytic approach and we get the same. In fact, one can do quite a bit better than log cube two here. Let me see the actual truth. Something that we can, and then we don't really have a hard limit for when the computation has to end. And what is really important, is that this is optimal. Not just the main term, but the second order term. So the simple choice that they have made is actually the best choice and not just when it comes to the main term. So you can do better. At least the second order term is so optimal. If there's any wiggle room, it would have to be so far down that I'm skeptical that even that is possible, but we can show that for general, for H continuous, and for you cannot do better than this. What about cell receive? Well, there is one condition. So you need, seems to be scaling, so for fixed age, of course, you could say, well, I can play tricks with that. Yeah, but the condition is just that for this, you have the condition that the total variation of the derivative of age we sign it, and that it be bounded, you know, the constant here will depend on the total variation of each kind. Yes. We have a question from the audience, from Fabian Pazzucci. Fabian, maybe just unmute your microphone and ask away. Hi, just a quick question. You say that the result is optimal for this value of kappa, but not that it's optimal for the value of kappa, the result is optimal period, that the kappa here is optimal. Yes. So it means that the kappa, you have a closed formula for it. Can you define kappa? Yes. Okay, let me go to the next page now. One moment. One moment. It's an integral. It's not that close, but you asked for it, so don't complain when they write a big formula. I'm not scared. Well, I haven't written down what age of t is yet, so this is what, and by the way, the computation here is rigorous, with integral arithmetic, the integration is rigorous, everything is rigorous. And age of t here is an infinite product. At the end, I will talk, if there is time, about how on earth you do rigorous computations with integrals of infinite products, but it is very much possible, and it doesn't have to take a long time. You asked for it, so don't blame me. Thank you. Okay, now it's going to be 1 over log d2, d1, d1. And this is optimal. Again, here it implies constant the total variation of, and to be really technical, we can't do better than, so the main term is optimal, that was known. The second order term is optimal. Hypothetically, there might be improvements in really lower order terms. And how far down it goes, it's a little bit worse in the first case, but there you have it. Okay. And there are several directions in which this may still be taken. There's work to do. Yes, so for instance, the case of this d2 squared is greater than n. I may do that in the final, finally final version of the three-prime book, bear with me, at least to some extent. And with fully explicit constants, let's not talk about that. So there's this thing, it's an interesting question. There are other things that one might consider. So for instances, how do you combine this with a preliminary sieve in my work and the work of Sebastian? So say sieve-only, odd numbers, and so forth. So actually, this second task is quite easy, and we might include it in the papers, I think we will not make it over long. But for instance, if you just are sieving, if you try to, yeah, a note for the real insiders. So you see that the second order term here is negative, and that's really very convenient. For many practical applications, it's not quite large enough to give you Brunt-Each-March without an error term right away. However, if you decide to restrict to odd numbers and you do get Brunt-Each-March, I think Montgomery von Strengths Brunt-Each-March right away. Just trying, other people can listen again. I was just trying to give the real insiders the accurate measure of exactly how strong the result is. All right, very good. So let me get started on the proof. I have 20 minutes or 15 minutes? 15, 20 minutes you can take. This is the advantage of this online seminar that you can go. Yes. So the first half of the work is proving the following proposition. And I should say that I believe this was half known. So not with, the main term was known. And you define rho as before. M rho, which is a quantity we want to minimize, is going to be h prime squared log d2d1. The main term is going to be this. It's going to be dL2 norm squared of h prime. And here, as I was saying, the main term here was known. You can find that in C polymath 14. Of course, sometimes you have to say that something is first one in the literature in a certain place, but it was sort of recent folklore was in the air. For this polymath, you don't even have to make the distinction because polymath is sort of the aggregation of wisdom, common wisdom, to some extent, common wisdom at a certain point. So I could add recent folklore. Some polymath people have told me that they already knew this before starting the project, but I think that's known, that's true by definition for polymath. But it's not to be found in the literature from the 70s. But what we find in polymath is this main term times one plus a little of one. And for our work, we really need to figure out a smaller answer here. And you can see, well, in fact, what we need and prove is also a bilinear version. That right here simply for simplicity, it's proved in exactly the same way. Yeah. Well, once you have that, then it's clear right away that the main term here is going to be, the main term is going to be optimal for H was H0 because it's just going to be Cauchy-Schwarz, right? So it's clear that you're not going to get a better term, a better main term than H0. And then you can work further what happens when you wiggle things a bit. That's why you need a bilinear version of optimality. So you see where this is going. But first, let me sketch for you a proof of the proposition. The method has precedence in the work of Motohashi. And you can find something a bit like that in polymath as well. But one has to be careful with the procedure because we are aiming at a more or less precise error term.  But one thing at a time. So this can be expressed as a double contour integral a Dirichlet series, but you can put some Zetas into it. Then everything converges more nicely. And that's something that will be useful in the future. This really starts as a Dirichlet series where it's not obvious that there are Zetas in there. But once you factor out some Zetas, it becomes something nicer.   You don't have to read all of this. The important thing, as I said, is that it's bounded on a strip. All the nastiness goes into the factors of zeta. All right. So forget about that expression for g, all that we need. So g is greater than, say, one-fifth minus one-fifth. So what you start is, so you let's see be your classical contour. So just your classical contour for the prime numbers here, m is placed by 1. And what you do is you move your, so you start with this is you start with two contours to the right of the y-axis. Then you move one of the contours to the classical contour. And you invert the order of summation. Now you shift to the left. And then for, then you're going to pick up a pole, a simple pole at s 2 minus s 1. So you are going to end up with a double integral as your error term. And as your main term, you're going to have the contributions. So for each, the, instead of the integral here, you're just going to have one term, one contribution coming from the simple pole. So you're going to have an integral of contributions. And then what you can do is this integral, which is the main term. Well, it's in the classical contour, but you can displace this classical contour again. Why not? Let it be on the y-axis. This is an integral on the classical contour twice. This is the error term. So what I'm going to have is I displace it to the y-axis. And then let's have this integral. How do you cope with this sort of thing? So by planche del, you actually can work out what will turn out to be the main term of this main term. Because if you just have t squared, FIT squared here, sort of sensible approximation, as we shall see. This is just going to be H prime X squared dx. And so it remains to bound, EIT minus IT. And this converges nicely. This is much nicer than what we had here. So this is O of t to the fourth. As t goes to zero, it's not hard to show. And also you have that this term here, which looks nasty, actually increases very slowly. Are you forgetting anything? No, I think it's correct. And for continuous H, this has faster decay than t squared. So this is all good. This is a basic idea in the one parameter case. That's how you work out what things are for H0, at least at first. At least as far as the main term is concerned. And then for the second order term, what do you do? So for d1, d2, general, this is actually very straightforward. What you have is, must give a better bound for here, with myself, where H, which is the H from the beginning, is dIT minus IT. And F of H is the main transform of H0. So let us focus on H0 right now. That's what we made, since we already know that whatever gives you the optimal main term will have to be H0 plus something smaller that we know from already from the main term that we worked out. Yeah. For H0, this is going to be worked out like this. Yeah. And then you can actually get again from the fact that this is oscillatory. Very good. So just do the analysis. You have some contours and you're happy. For d1 equals 1, you have to do more careful analysis. Some more careful analysis you have that the error term, again, is something that you can do exactly for H equals H0. And then what you do is, you use your previous work to say for a general H, in order to get to help to get the right main term, I'm going to have H0 plus a small displacement and then you recur. All right. I don't want to really get into the details of the recursion in order to make the error term smaller, the iteration really. Let me just tell you something that some of you may be asking yourselves. I already mentioned it once. How to compute this kappa here rigorously. And this is important. It's not just that I keyed something into the computer. We actually prove using a computer that this value has, you know, that it's very six significant digits are such and such. All right. So the syndrome, of course, has different chunks. So in the range C0 epsilon, do a Taylor expansion, rate of T. In the range, T to the infinity, which would use explicit bounds for one over the zeta. And in fact, by use, well, we had to prove them because what happens is complex analytic methods were not being used for cancellation very much because I mean, until recently, there were basically no explicit bounds in the literature for these. And it was believed that they were going to be horrible. Now, so there is there are some bounds by tradition, but they have a constant in front, which is like, best of cases, something like, well, it's more than 1000 has four digits. So that's too much for computations. You don't something that is a million times log is not good enough. So we actually managed to improve traditions bounds using some other bounds by tradition and basically his method, but there were some optimizations with that. And so this is something reasonable. So yeah, um, yeah, and you can use this is probably about 10 to 20 times worse than the reality. But it's already good enough that you can show that when big T say 20,000, the tail of our internal is going to be quite small. And then what remains of course is to, well, how do you deal with H of T, which is an infinite product? Nowadays, yeah, there are finally, you know, consumer and products that, so to speak, that let you do internal arithmetic and rigorous integration without really getting into the guts of things. So we use the internal arithmetic or rather ball arithmetic because we were using our, which has a nice integration feature. The really the question is how to, how do you base approximate?  So how do you approximate it well by a finite product? Well, already in order to express H of T, as something that was clearly bounded on a strip, we had to take out factors of Zeta. So what we do is just we take out more factors of Zeta. So this is a very old idea. The earliest reference, I found this in a paper by somebody called Western in the 20s, which says that basically little would give him that idea. Yeah. And I think this idea is, an idea like this is just, well, the logarithm of this idea is using Pari to some, to accelerate convergence of some infinite sums. I don't think Pari, that's the biggest error bounds that I don't know at any rate. So this is an old idea. And what you have is that H of T, for instance, can write H of T as we already were taking out. You want to get values of Zeta that look like it, that get rid of the lower order terms. And so what you will have is a very rapidly convergent series. You can work out more terms. And when I was giving this talk for the first time just two months ago, and Max Glank, Don Seguir was in the audience and he told me that what he would do, so we have, we do well enough with what I am writing. So a few terms and then we have an infinite product that converges quickly. So it can be truncated after a few terms, and then you just estimate the tail. Yeah. So what Don Seguir was telling us is that in fact, he could show that, yeah, you can actually express H of T as an infinite product of values of Zeta. And then truncate that using, yes, just using your standard facts, standard bounds on Zeta. So that's an alternative way of doing it.  You have to, at least, include the factors two or two and three, or otherwise your infinite product of values of Zeta doesn't converge that nicely. So some combination of the two. Yeah. And that's basically it. So I think my time is up. Yeah, so that's where we have left things. I hope that you have all found something of interest in this talk. But the main moral is that, yes, the function that people were using is the optimal one and not just as far as the main term is concerned, but also as far as lower terms are concerned, lower terms are concerned, and you can work out the second order term precisely. It has a nice constant. And what's really important is that it's negative. And that helps you for many things in your life. Thank you so much.


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Catching kind of slipping or sleeping on bottom. I could just grab his leg and throw it in but again This is home base. When you guys let your leg ride slip below his knee. I don't have a leg ride anymore Okay, it's important that my leg It's in be above his knee. Everybody see that It's got to be somewhere on his thigh and then my ankles are always crossed and my knees are always wide Okay, now we're gonna talk about our first turn which I use more as a set up an actual turn All right, so we're getting our legs and get our legs in Our ankles crossed. I don't want this guy to do Whatever arm opposite of my leg in I just want him to put it down by his side for now Okay, whatever arm opposite of my leg ride. I wanted to put it down I don't want it straight out. I don't want it in front. I just want it down by his side Okay, now all I'm gonna do is I'm gonna act like my body is an airplane taking off And I'm gonna go from my leg ride side across his body do a shoulder And I'm gonna arch my back as hard as I can okay We call this shock in his back so I'm like I'm an airplane taking off and I'm a shock my back See I rolled his hips open Okay, and started turning his belt buckle to the ceiling a lot of people call this like helicopter in the leg or Swinging the leg. I don't like just swinging the leg is when I swing the leg It puts me here when I go to my hip on the side away from my leg He can turn into me and get me in some squirrely positions now We'll talk about that and we got some positions that we can beat that with but I want you to Come 45 degrees across his back and get your chest out over his opposite shoulder Okay, we're gonna have our legs in he's wide I want his arm down like an airplane take it off and then right when my chest gets to his shoulder I'm gonna arch my back. I'm gonna smash my belt buckle right into his low back right here Notice my left knee Went wide my chest is tall Okay, I'm still arched in a match. I won't I want my outside knee almost float I want all my body weight right here on the stairs Okay, we have to get that down in order to be able to turn the best guys Okay, there's some guys that'll take a lot of punishment down there Okay, but if I'm shocking his back and then fall into my hip, I'm gonna lose a good position of power All right


I've got the seven most useful college majors where you can earn six figures and in some cases you can do it in your very first job right out of college like I did. And the first one on the list is going to be a tie between statistics and mathematics. So I know the moment you hear mathematics you're probably going to be afraid but I found this really good quote it's mathematics is not about numbers equations computations or algorithms it's about understanding this is by William Paul Thurston so the way you think about mathematics is it's kind of like a swiss army knife right a swiss army knife has multiple tools that can be used for many different purposes and in the same way mathematics has a wide range of techniques that can be applied to many different fields so whether you're interested in business finance computer science engineering or even art mathematics has something to offer so for instance there was a mathematician named James Simons who became one of the most successful hedge fund managers on wall street so let's go ahead and look at the statistics with this one I'm not going to spend too much time on it but I am going to use these statistics from my college degree ranker and this is the newest 2023 version of the college degree ranker so the early career pay is going to be $57,000 the mid career pays around 102,000 the demand score is off the charts at 120 out of 100 and the meaning is about 45 percent which is decent and the total amount of bachelor grads per year is about 17,000 so let's quickly talk about the pros and the cons of getting a mathematics or statistics degree so first of all your skills are going to be very versatile mathematics and statistics related skills can be used in just about every business just about every industry out there there's also a high demand for your skills there's not that many people who are good at mathematics and if you are good at it and especially if you like doing it this can be great and the salaries tend to be really competitive so if you are really good at mathematics especially if you specialize in a certain industry or a business you can get paid really well now let's talk about some of the cons so there are limited job opportunities for doing purely mathematical related things so typically you're going to be using your math skills but you're going to be using it in such a way where you're probably not challenging yourself nearly as much as what you studied in school speaking of studying in school that's another con is that mathematics is relatively difficult it's probably one of the hardest degrees and the third con is of course that a lot of people do not like mathematics so of course if you don't like math do not get a mathematics degree overall mathematics degrees do offer quite a bit so you're going to learn versatile skills you're going to have high demand you're going to have really good personal satisfaction and it's going to teach you how to accurately count all the times that you smash the like button on my videos number six on the list is going to be another tie it's going to be between accounting and finance now accounting and finance are both going to have to do with the financials of a company but typically accounting is going to look at the ins and outs that happened in the past whereas finance is going to try to predict or model the ins and outs that are going to happen in the future so accounting tends to be a little more versatile there's typically more accounting jobs but finance jobs tend to be higher paying but they're also more competitive and there's definitely some downsides and i really like this quote by warren buffett it goes the stock market is a device for transferring money from the impatient to the patient and i definitely agree with that as messed up as it is i wish it wasn't like that but that is absolutely true all right so let's look at the stats really quickly the early career pay is about 55,000 mid career pays 98,000 the demand score is going to be 133 out of 100 which is amazing the meaning is 40 which is on the lower side and the bachelor grads per year is about 37,000 and this is for the finance degree i'm just using that one as an example but the accounting degree has really good stats as well so some of the pros of getting an accounting or a finance degree would of course be high earning potential especially with a finance degree you can make a ton of money but even with an accounting degree there are positions out there where you can make a lot of money for instance i interviewed bill hannah the financial controller on this channel and he makes i believe well over 250,000 a year and he got an accounting degree the second pro is there is a wide range of career options just about every industry every business out there needs an accountant so if you get into accounting you don't like the specialty that you chose it's very easy for you to switch into a different type of specialty or if you don't like the business that you work for it's going to be very easy for you to go to a different company and then the third pro here is going to be transferable skills so when you get an accounting degree for instance you are going to learn how to manage your money you're going to learn all about how money works and that is going to help you in your personal life as well so people who get accounting degrees tend to be really good when it comes to financial management things like investing saving making good financial decisions so some of the cons of getting specifically a finance degree are going to be intense competition and this goes for you if you get an accounting degree and then you try to go for some of the more competitive jobs as well but yeah the finance industry is highly competitive it's kind of like a winner takes all zero-sum game type of industry so there are people who work in finance that make tens of millions sometimes even billions of dollars a year there's hedge fund managers that make billions of dollars a year and then there's a ton of people who work in finance that don't last very long and then the second con is long work hours if you're going to be working in finance chances are you're going to be working 60 to 80 maybe even more hours per week and then the and then the third con is the stressful work environment and the risk of burnout number five on the list is going to be management information systems now this one is technically a business degree but it is a business degree that has technology related aspects to it and it tends to specifically focus on information aka data and I've talked a lot on this channel about how data is more valuable than even oil or gold and it's definitely kind of messed up how they've been collecting our data without our consent like basically semi illegally but they're you know no one's probably going to get in trouble for it but they definitely were not supposed to do it but they did it anyways but yeah that's a topic for a different video so MIS is basically kind of like a company's GPS right so just like a GPS is going to guide you to your desired location in the most efficient way possible MIS is going to help you use data to run your company in the most efficient way possible so when it comes to the stats MIS is really solid early career pay is going to be about 60,000 mid-career pays 105,000 the demand score is 113 out of 100 which is great and the meaning is 42 percent which is relatively good for business degrees because they tend to be a little bit low and then the amount of bachelor grads per year is a little over 7,000 so some of the pros of getting an MIS degree first of all you are going to have a super flexible skill set right when you learn about business and technology that's such a rare combination and just about everybody out there is going to need that skill set it also has really high demand there's great career opportunities the salary is really competitive it's a constantly evolving field and there tends to be high job satisfaction some of the cons of getting an MS degree is the fact that you are going to have to learn some really technical skills it also tends to be a fast paced environment there is a risk of burnout and because of the fact that you're kind of working with data it is relatively limited when it comes to creativity now one thing I will say about technology related careers is a lot of the time you can get into them without a college degree now that doesn't mean college degrees are scams by any means they can help people get into it but you do have to take it on a case-by-case basis because for some people going to a bootcamp getting a certification maybe you know doing self-training even can be a good idea for instance I've interviewed a ton of people on this channel that have gotten entry-level technology related jobs like James for instance who was able to get a job in two weeks and he didn't even have access to a computer he literally used an iPad and I will link that so you can check it out if you want to number four is going to be computer science and this is one of the best degrees out there and computer science of course would lead you to going into software development and there's a quote by Steve Jobs where he basically says everybody in this country should learn to program a computer because it teaches you to think and computer science really is like a toolbox that is filled with many different versatile tools and these can be used to solve complex problems but with that being said you definitely do have to know your stuff it used to be you could get into software development basically knowing nothing as long as you kind of networked and now you do actually have to know your stuff you have to actually know how to be a software developer you know there's a really good quote by Pablo Picasso where he basically says computers are useless they can only give you answers and that's actually a really deep quote because basically what he's saying there is where real intelligence lies is asking the right questions and if you don't ask the computers the right questions then you're not going to get good answers right garbage in garbage out and this is why with chat gpt for instance everybody's freaking out about this thing called prompting and prompting what it essentially is is asking the right questions and when you ask it the right questions you're going to get better answers now when it comes to the stats here this is actually my number one degree out of like 900 degrees on my college degree ranker so the early career pay is going to be about 68 000 mid-career pays 114 000 the demand score is absolutely off the charts at 154 the meaning is 41 percent which is kind of normal for technology related degrees and the number of bachelor grads per year is about 23 000 so some of the pros of getting a computer science degree i mean there is a lot of pros i mean high demand uh competitive salary versatile skills creativity access to benefits that you can get when you work in technology that you probably aren't going to get in any other industry i mean the list really goes on and on and on some of the cons of a computer science degree is first of all the curriculum is extremely challenging it is one of the harder degrees another con is the technology is constantly changing another con is the job market has gotten more competitive you really do need to know your stuff and you need to specialize to get a job in software development and another con is it is a relatively sedentary lifestyle so you're going to be sitting on your computer typing quite a bit also many software developers find their job to be relatively boring because they'll typically do like one to three hours of work and then they have nothing to do for the rest of the day which is a quality problem to have and i made an entire video about whether a computer science degree is worth or not you can definitely check that out so if you find videos like this informative by the way definitely share this with friends and family that is one of the main ways that people actually find my channel is people find it extremely informative they think it's super valuable because nobody else is really doing this this is kind of the channel that i wish that i had at 18 for instance and so they'll share it with their friends or their family whenever they see somebody who needs to see it number three on the list is going to be industrial engineering and this is actually one of my favorite engineering degrees and there's a great quote about industrial engineering which is industrial engineering is not just about manufacturing it's about improving any system including healthcare finance and even entertainment this quote is from lisa bosman who is the president of the institute of industrial and systems engineers industrial engineering is basically a combination of engineering and sort of a logistics slash operations management type role and an industrial engineer is kind of like the conductor of an orchestra just how a conductor coordinates different musical instruments to make a beautiful symphony industrial engineers are going to coordinate the different aspects of a system in order to optimize its performance so the stats here are really good as well so early career pay is 67 000 a year mid-career pays 110 000 demand score is really good at 101 out of 100 meaning is 46 percent and the bachelor grads per year is about 5000 so some of the pros here are going to be high demand diverse job opportunities high earning potential problem solving skills and continuous learning i guess the continuous learning could be a pro or a con depending on your perspective some of the cons here are there is a very high academic demand engineering school is no joke the workloads can also be relatively heavy there can be high stress levels as well there is limited creativity in this career and you're going to have limited interaction with other people next one on the list number two is going to be mechanical engineering and a really good quote here is mechanical engineering is the basis of all engineering it provides the background for understanding and designing everything from tiny micro mechanical devices to large systems such as spacecraft and this was actually from the lead of the mars rover project whose name is adam steltsner so mechanical engineering is basically the backbone of modern technology just as the backbone provides stability and support in the human body mechanical engineers design and build structures and systems that power the world so the stats on this one are really good as well 66 000 early career pay 110 000 mid career pay the demand score is 105 the meaning is 51 percent and the bachelor grads per year is 32 000 so some of the pros of getting a mechanical engineering degree are going to be job opportunities lots and lots of job opportunities i mean mechanical engineer that skill set is incredibly flexible right and that is the next pro which is a flexible skill set you also have high earning potential you can make a lot of money as a mechanical engineer or in careers related to mechanical engineering i've talked about this before on the channel but engineers over a lifetime make the most money out of any type of degree another pro is innovation mechanical engineers are great at innovating and you know coming up with creative solutions to different problems that people are having in the world some of the cons are of course the challenging coursework uh engineering school is no joke like i said engineering degrees are incredibly difficult another con is engineering jobs especially at the entry level do tend to be relatively competitive there's also a high level of responsibility the work hours can also be long and you have limited creativity the next one on the list is going to be a nursing degree or any other healthcare related degree and nurses are basically like the foot soldiers of the healthcare system right there in the trenches helping people out with their own hands and the stats on this one are great so early career pay is 60 000 mid career pay is 79 000 but keep in mind a lot of nurses work part-time so it's probably in reality a lot higher than that the demand score here is off the charts at 135 the meaning is off the charts at 83 percent and healthcare related degrees tend to be very high and there are 134 000 bachelor grads per year so the pros of getting a nursing degree is there's very high demand the salary is also really good the job satisfaction does tend to be relatively high especially in the long run because of the fact that you're helping people out so much nursing also tends to be one of the most versatile healthcare professions and there's also opportunity for continued learning as well as career advancement within nursing some of the cons of nursing is it tends to be extremely physically and emotionally draining there's also quite a bit of educational and training requirements sometimes the hours can be really weird so you might be working like 12 hour shifts and that could be a pro or a con so for instance when i was in healthcare i absolutely loved 12 hour shifts because they felt about the same as eight hours to me another con is you have to deal with a bunch of red tape right the government has certain regulations they might make you do things that you don't enjoy like in the you know whole cough cough situation that happened i think we all knew that the government had no idea what they were doing and it was absolutely ridiculous but you couldn't really say anything and there is potential for high levels of burnout click on this video right now to check out my ultimate guide to choosing a college degree


Welcome, folks. We had the Dow Industries finished down 67, NASDAQ off 40, S&Ps down 12, bottom line, folks, is that that's quite a comeback from where we were before we started the trading day at 9.30 Eastern Standard Time this morning. So that puts the NASDAQ down 1.5%, Dow Industries down 2 tenths of 1%, S&Ps down 4 tenths of 1%. That's like basically a flat market. Gold contract. Gold contract also flat at 12.81 an ounce. We had silver down 5 cents, $14.92 an ounce, light sweet crude. That rejected lower price. You're up 95 cents, $62.89 a barrel, and notes and bonds. You have the 10-year note, up 9.6, $123.20. 30-year bond, up 14 at $147.26. If we go over to the 30-year, you take a look at the 30-year, what you're going to see in the 30-years that you pushed over the highs of last Thursday, you had 264,000 contracts. That's saying you're going to be right back topside once again. The divergence, it would be inside the 10-year, because what we had with the 10-year, you got up right into that level, and you had quite a contraction of volume, actually. We were going into 1.9 million contracts, you did 1.3. So that 10-year does look like it wants to pull back a bit, and if that's what we get, you'll get the 30-year pulling back simultaneously. Kingdala. Kingdala, bottom line, couldn't hold price out here today. What you had, the Kingdala trade 97.275. If we take a look at this continuous contract inside Kingdala, the number you want to keep your eye on, folks, is 97.705. We're back inside the lower range right now, and the longer that we stay inside this lower range, the more the probability comes up that this thing's going to try to pull back into this 94.675 level. If we get over and we take a look at the S&Ps, the spy out here, what we did with the spy is that we get down to a price point of 288. The rejected lower price, that being said, when you get 95 million shares down here, we're going to be right back down. Bottom line, you get way too much volume on the way down out here, as is, I suspect, the Qs. Not so much the Qs, the Qs only have 37 million. That's not bad. The Qs can go hit the highs before they want to pull back down. Stay right there, folks. You're going to have some numbers after close out here. Dow industrials finished down 68. Nasdaq off 40, S&P down 13.5. I'm right back.


This is theCUBE's exclusive coverage of Big Data NYC where we will be breaking down all the analysis of the show here at Strata Conference, Hadoop World, and Big Data NYC. And we want to have a shout out right now to the two key sponsors to make theCUBE happen here because the top story here is to sell out at O'Reilly Media across the street. Hilton is sold out. They have sold out everything inside the show. It's packed. We are here across the street at the war work and we would not be here live bringing you our independent open source coverage of Hadoop World and Big Data NYC. It wasn't for Hortonworks and it wasn't for Wendisco. Those two guys, thanks for you guys for supporting us. I'm here joined with Dave Vellante, my co-host for the week. Dave, welcome back to New York City. We're back again, fourth consecutive year for Hadoop World. Thank you, John. It's a pleasure to be here. I remember the first one that we did in 2010. You pulled me out of a miserable conference in Dallas and got me into Hadoop World. That really started sort of opened my eyes to the Big Data world. And you know, John, back in 2010, it was what is this thing called Hadoop? I remember I was leaving that conference, traditional sort of old enterprise tech conference and everybody was saying, what's Hadoop? What is that? What is that one person out of the 30 people that I told, said, you know, you're lucky you're going to Hadoop World. And we've seen a migration really from what is Hadoop to how can I utilize it? How can I take advantage of it? Now we're sort of entering the phase of how do I get more business value out of Hadoop? You know, we look at the surveys from the Wikibon community and they're not getting as much value as they could. Well, we're here live in New York City and again, we're excited. It's the fourth year this market of Big Data started out as Hadoop. No one's ever heard of it. We were there, President at Creation. It's grown up, it's big, it's mainstream.  Everyone's doing things with Big Data. But Dave, there's two types of companies. There's creators and the commoditizers. And we've been following this traction of Big Data from the technology side to the business side. And there are platforms that are out there. There's decisions to be made by business users about who to buy and ultimately create value. So that's going to be the topic here. I also want to bring in Jeff Kelly who will be co-hosting with Jeff. He's going to be coming in and out throughout the event. Is the Big Data Analyst at Wikibon. Jeff, welcome to theCUBE. John, thanks for having me. So you're going to be out scouting the landscape. I know you're going down to the NYC Hall for an event tonight. Talking about some Big Data there to find out what's going on with Pivotal. Pivotal again, the spin out from VMware is having a big event down there. Pivotal doing very, very well, building on all their momentum around Green Plum. Really trying to figure out the software side of the equation. There's a lot of things they're working on. So hopefully we'll hear some news that may be getting their toe in the water with Big Data from Pivotal. So I want to find out what's going on there. What else are you finding to show here? Sure, there's going to be a lot of news as there always is at Strata Hadoop World. Seems like every company who's got Big Data in its purview drops some news, big and small at this event. So there's just going to be a lot of noise happening. And part of our job here is to help cut through that noise and bring in the signal and really drill down into some of the important areas. And I think one important area, as always, being Hadoop World is all about Hadoop as a platform. I think we're going to talk about where it stands in terms of the technology, in terms of enterprise readiness, in terms of making it accessible to a wider range of users. Of course, the horse is on the track. We're also going to talk about the different players in the market. Hortonworks, Cloudera, Platfora, as you mentioned, MapR and others. And of course, there's also a lot of talk at this conference around making business analytics, advanced analytics available and more accessible to regular business users. So we're going to see a number of news releases around that and we're going to kind of break that down for you and really tell you where that, is it real yet? Analytics for all, as we're calling it. Well, we're here excited to be here again. The big story here across the street at the Hilton is it's a sell out, O'Reilly has sold out again. They're doing very, very well with this conference, monetizing it very, very well from their standpoint, Dave. But we are here for the fourth year and one of the themes is the Freedom Tower and our logo for the big data NYC because Open Source is about, not about free, it's about freedom and we're really passionate to be covering it. And again, the world is really exploding. The developers and technology, just from four years ago, with Doug Cutting, with Cloudera, hence Hortonworks, that's the equation that way and disco and a bunch of other companies has created business value and that's attracted a lot more startups, a lot more developers and VCs. So we're gonna hear a lot this week, Dave, about startups being funded. We're gonna hear from folks like ClearStory where they're gonna make their announcement kind of I think two years later. And also the other big story is the evolution and the transformation of Cloudera where Cloudera is changing their colors again. They're gonna hear some announcements from them. We were pre-brief, we kind of know it's coming down but you're gonna start to see Cloudera going after a different market space. I don't know why I say I call it a pivot but GigaOm ran a story this week that kind of talked about some of the telegraph messaging we're seeing from Cloudera which is essentially saying that Hadoop and others aren't competitors. So that tells me that it looks like they're pivoting out of the Apache space. Pivotal is, so it's a little bit of a pivoting. So they're pivoting to a straight line into the Pivotal, the EMC. So we have a lot of the data around this. So I want to really drill down on Cloudera on that because in a way that GigaOm article is intimating that they don't look at Horton versus competitor. If that's the case, then Hortonworks is the number one open source pure Apache if that's the case. I mean, what are you taking? Well, so here's the thing, John. As you well know, there was these sort of what I sometimes call urinary Olympics between Hortonworks and Cloudera and obviously MAPR as well. Sort of fighting over who has the biggest contributions to open source. Sort of the Hortonworks came out of the Woodworks said we've got the most contributions and most committers. MAPR was doing its thing. So the question is this, John. Did Cloudera essentially tap out of that battle? Say, hey, that's a race to zero. You know, we want to go compete with others. The likes of Pivotal. So that's sort of an interesting theme. I don't know, Jeff Kelly, what you think about it. But I would say that Hortonworks or rather Cloudera is really trying to take the high road here in terms of saying we're not participating in that sort of Hadoop distribution business. We're going after the high margin stuff. We're going to go compete against essentially EMC and VMware. Right, well, is it them taking the high road or is it more of a marketing strategy to get them in the conversation with the likes of IBM and Pivotal? If you look at the capabilities of an IBM, I mean, Cloudera's done a lot of great work in Hadoop, but they're nowhere near what IBM can offer in terms of a comprehensive big data platform. Platform is a little, excuse me, Pivotal's a little different story. They're still kind of a group of different technologies, different companies that are still bringing it together. If they can bring that all together, they're going to have a pretty interesting platform also. I mean, they made some interesting announcements around Gemfire and some other things this week. So if they're able to actually execute on this and actually compete with those two, that'd be something because right now, it seems to me, those two players you mentioned, Pivotal and IBM, certainly have a lot more of the pieces anyway. So it seems, John, we got a case of a new CEO coming in at Cloudera with a lot of new messaging. Yeah, I mean, well, first of all, he's from HP, so we have to weigh that, what his role at HP was. But from the people I talked to, I talked with Mike Olson in depth, and Mike is very optimistic that the folks at Cloudera are very excited about this new CEO, Dave. He's got the suit mentality of building a company so that he's going to continue to transform Cloudera on their path to all their ultimate destination, which is an IPO. That's the goal, right? It's been talked about kind of in the back channels around IPO is the goal. But the markets changed a lot from our original conversation with Pat Gelsinger at EMC World four years ago where you asked him, will there be a red hat for Hadoop? He said, what was his advice Pete and Mike Olson of the CEO of Cloudera? And he said, find your center point. And so since then, if you'd noticed, Cloudera's center point has always shifted a little bit. Impala, so they almost felt like they were groping for a position to get that burst into success and have an innovation strategy and a growth strategy that was coherent and relevant. So I think since that EMC entering the market, you've seen Cloudera get their focus. I think now you're seeing Cloudera kind of bear down and the folks I'm talking to close to the company all agree and talk that it's not the same Cloudera it was when they were a small startup, they're getting their grit and grown up, they're going to find their center point. And I think what we're going to hear from Cloudera here, Dave, is they're going to look and control their own narrative and they're going to focus on their own mission. So it's going to be interesting to see if that's going to be a pivot towards Pivotal and IBM and the data warehousing market, or are they going to come in and try to win the Apache? You really can't do bulls. So I'm going to be curious to see both. Essentially, I think what Pat Gelsinger was saying at that time was we're not going to let a red hat run for a decade and run loose and get a $10 billion valuation. We're going to get a piece of that action. So I think that the fact that three years later now Cloudera's realizing that Pivotal is the real competition is probably apropos. Right, I think it's interesting because we talk to Hortonworks. They're all about, as they say in their slogan, all about Hadoop. We do Hadoop and that's what they do and that's all they're interested in doing. When you talk to Cloudera, they're clearly trying to move much higher up the stack. I mean, if they want to compete with an IBM around big data and analytics, you're going to have to start getting applications, analytic environments for data scientists, those kind of things. So that's a very different strategy than what we're seeing from Hortonworks, which is laser focused on Hadoop, Patrick Hadoop. So again, it'll be interesting to see. It's a longer term play and it'll be interesting to see if they can pull it off. So Dave, I want to get your perspective. I want to talk about the event here. The big data NYC is our Silicon Angle Wikibon produced event around the cube. We're going to have a party here. So the folks watching Tuesday, if you're in Manhattan, swing by the war, we're going to have a party on Tuesday night. Happy hour six to eight. We have special commemorative shot glasses with the cube logo on there. First time we've ever had commemorative gifts at an event. So since we're having our own event, Dave, this is our own big data NYC, we're going to have commemorative gifts for everyone who attends. This is really designed for cube alumnus, thought leaders, CIOs. Again, everything about the cube event here is all open source, all the proceeds we've received from our sponsors and underwriters are going right back into the event and the production for more great content and analysis. But our goal is to one, get the signal here in New York City what's going on with big data in all industries that are here, pharma, financial, two core industries here, and media and entertainment, but also dissect strata conference. I mean, the strata conference has evolved and morphed from kind of an interesting conference to partnering with Cloudera to a full on commercialization sell out, if you will, Dave. They've sold out everything and they've made a lot of monetization and congratulations for them. I'm really happy for them. But there's still that hype factor going on and what we want to do is understand which company's going to make it, where is the signal from the noise, a lot of announcements from startups, a lot of VCs promoting their portfolio companies, a lot of people putting that out there that they're the bridge to the future. And it comes down to, does the technology match the market requirements for the people buying Dave? So I want to get your perspective, CIO perspective, a buyer, what are they looking for? So I think that from, first of all, let me just say it really is a pleasure to be here, John. I love when you talk about the freedom tower. You go to the Wikibon blog, you'll see Jeff Kelly wrote a preview to this event, Big Data NYC and StrataConf in Hadoop World. Go to siliconangle.com slash Big Data NYC. You'll read about all the stuff that we got going this week and the freedom tower itself, John, freedom not just free like open source is free, but freedom to do with the technology and the code, what you see fit. That is a theme that I think is, you know, resonates very well. Now you asked about CIOs. I think that from a CIO perspective, I think most CIOs are still very risk averse. They would prefer, by our research, to risk lock-in to get function. So the open source world, which is the big wild card here, has to bake over time. It could take a decade, you know? Who knows, maybe even longer to really replicate the functionality that exists in more proprietary code, what Cloud Era calls sort of open core and then proprietary on top of that. They don't use that word versus, say for instance, we've been talking about the Hortonworks approach, which is really open source everything. And of course, MapR as well really started the whole proprietary thing. So you're actually seeing, I would say, John, you said they're not really pivoting. They really are pivoting, right? They really are pivoting, in my view, to much more of a integrated stack approach. So the CIO, which is, by the way, not a bad move because the CIO, as they say, is risk averse, looking potentially for more function and risking lock-in. Only about 10 to 15% of the practitioners that we talked to in the Wikibon community are dogmatic about open source. Now, this is where open source becomes such a wildcard. If you look at the way Linux, really disrupted Microsoft's franchise, look at what Canonical is doing with regard to Red Hat. And I really think that the Hortonworks strategy is going to be very, very disruptive. It's extremely hard to predict, John, what's going to happen. But I think that open source is this lever that can move huge boulders. So guys, we're going to have to get a break here, but Dave, we'll be jumping into all the analysis. Companies like Pivotal, companies like EMC, companies like HP, all here, big companies. Really, it's a developer tech conference meets business value. I think that's been the confluence of Strata and Hadoop World. And of course, it's a startup thing.  We've worked with those guys on the data, social data, want to weave them into the conversation, because a lot of the Twitter data has been part of the big data NYC discussion for some time from the financial markets, whether it's hedge funds, getting predictive analytics, among other companies here. I'll say that the backdrop on all of this is that on the eve of Apple's earnings, you're seeing business value at the front of the conversation again. But we've said that every year, but more importantly, this year, I think you're going to see the winners separate from the losers on the technology side, Dave. I really want to watch that. So on this event, we will be dissecting and reading and squinting through all the noise at the Stratoconference this year. And we will try to find out who the winners are going to be on the technology side and that business value. So this is theCUBE live from New York City. We are at the Warwick Hotel, literally less than a New York minute across the street from the Hilton behind us in New York City. We're going to be dissecting the Stratoconference as well, all activity in New York City. We'll be right back with our next guest. This is our opening evening Monday night preview of Stratoconference, Hadoop World, big data NYC. I'm John Furrier, Dave Vellante and Jeff Kelley. Be right back.


What's going on everyone? Sam here with late in sports cards. We're ripping one box of 21-22 Leaf Pearl multi sport for John A All right, let's do it John. Here we go man again 21 You can see 21-22 Leaf Pearl multi sport. Here we go dude.  They made like this throughout the years. Yeah, it's been in the past Yeah, yeah, I just I first I've heard about it Right Wow. Yeah, right.  Oh Man, what a start dude the answer TP Mike Bibby and Penny that's sick.  Oh, it's the next one. We've got a Roy Campanella two of four hollowed heroes game use bats. That's nasty One of the best catchers of all time campy, baby God, that's awesome Yeah, top three catcher of all time Brooklyn Dodgers legend one of the best nicknames of all time campy second card second card first card was absurd was a Quad auto was there All right, next one.  That's holy cow, dude Holy cow. I know look at the Erling Holland batch as well. You got the in pop is got two color.  Oh man, oh Man, that's crazy All right, I'm gonna the next one now. I'll leave pearl Ken leave pearl All right, that's gonna be a one-of-one Mario Rivera logo man The Sandman, oh I'm so jealous of this. Oh Wow Congrats John Dude that's and that's nuts That's so that's so nuts, dude.   I Think he might be number one in the world now.  I Didn't even know he was licensed, bro Oh Man, that's that's kind of insane, dude That's really insane. He's like one of the best tennis players in the world If not the best right now. Yeah, he's number one in the world right now Carlos Alcaraz and he's like what 21 22 years old. He's super young Dude, that's crazy He's 19 years old number one in the world, dude Unreal dude. Oh, he's 21 Still he's younger than me. He's the best tennis player in the world. That's sick, man, bro That's sick man. Holy cow You just turned 20 last May Oh my god All right, looks like we've got a God Aaron Rodgers 21 22 leaf pearl multi sport base card auto. Why not an a rod? That's so sick to Aaron Rodgers On the redemption Again parallels will be card and level including parallels be a war of the time. Filament could be a one-on-one Theoretically box is right. I don't want to see what's next seven of eight Julius Irving With the pearl there's just a piece of pearl in there authentic pearl.  Why not dude? Holy mother of pearl. Holy mother of pearl.  There you go. That's gonna be two of ten paylay on the pearl now. Wow On the white pearl Sheesh And rest in peace to a legend that's so sick It does not end it does not end Paylay on that one. Let's go dude to obtain correct John. It's awesome my friend. I'm so the next one That's gonna be four or five Fernando Valenzuela on the two colored patch man another legend Four of six Fernando Valenzuela.  Holy cow, dude That's so cool. So cool at last One in the rose here. Oh, it's gonna be two of eight Phil Foden very nice on the auto Phil Foden Dude, I thought I was gonna say you never see Fernando Valenzuela anywhere.  Oh man, oh Dude, that's so cool Wow, wow, wow, wow. That's awesome Richard Nixon on the cot, dude 16 of 20 Wow Dude, oh my god. Oh Man, dude, that was that was insanity again. That's gonna be your empty box there We'll take everybody take it out. I guess it's the double check, but pull it down and destroy the box So no empty as you can see in the top there John again empty on all sides here Unbelievable that that is gonna do it for a break John. Thanks again, man. That was so much fun to do We'll get them right out to you man