Happy New Year, everyone. Welcome back on January 2nd. Hopefully everyone is all excited. Good to see you all back Good to see you. All right. Good. Good All right. Oh my god. Look at that. We got to make a little quick little shout out a Bud Michaels C over there. Let's give him a big round of applause. He's only up 6,500 on the SES Way to go my man. All right Give him a round of applause There we go. All right. Good. Good. Good All right, so let's do a little shout out.  There we go Look at that. That thing was just but and by the way if you guys look over here on the announcements in the morning That was actually the biggest that was the first stock that was on the watch list that I put out there at 745 okay Little too cracky. Okay. Hold on if it might yeah, I might feel a little high. Let me lower it There you go. Just a little bit better right now But yep, though the nice little move right there. Listen. We started with time. We started right around here Let me fix the time frame Yeah, like right around here I started so we came in around nine so not too bad nice a little cheapy Start it back off a little bit. I would be very a little careful about it right now I you know what there was a There was a little bit of a really big iceberg order that was sitting there and he's still actually out there at 1092 Guy was out there. She's been out there forever a lot of big orders out there from 1092 11 some big big block orders See a 40 almost 10,000 shares that we sold meanwhile. There's buys and sellers in the hundred so Looks like for you good stock. Anyway guys did pretty well on it Nice little move though before we do that talk about all these stocks because we got a nice little rally going on The market about 107 points so far, but but listen New Year's Eve was a really really good day What to get high right pot stocks took off right what happened? Let's talk a little bit about what happened on on on Tuesday, even though we were closed yesterday, but the T Lry let's just bring up some of these stocks right here. Hold on Fix my little time frame over here. So you guys kill see what we got going on there We go. So a nice little move. You could see TLR. Why nice Faustal flags everything thing just literally went from 1580 to 1760 we know this stock very very well TLR why that was one of our big home runs of last year That one did really well in Crohn's all of them going down the list look at this one 650 all eight almost Eight that one didn't well CGC another pot stock did pretty well the kind of holding kind of flat too by the way and If you look at the trend but CGC was also a really nice one that one you could see that one's still looking pretty strong I'm still throwing back on the watch list CGC that one did pretty good in ACB for all the beginner traders that don't have a lot of money and you know It's still a little deer in the headlights Maybe ACB was really up your alley. I mean look at these stocks eight dollars down to a dollar Really good looking sharp right there. Yeah, we there we go So everything was done pretty early was kind of flat still coming up a little bit too this morning ACB So I'm still a fan of all the pot stocks and I'll put that in the On our title of our YouTube channel, but it looks pretty good so far. All right. Anyway, listen, it's a new year It's time to start all over again Now what you learned in class remember we do the monthly journals and with the monthly journals you got to remember Now you want to implement that over times 12 over the year if you guys been with us over the year And now you know if you're ready to have a new goal So if your goal is that you made this, you know, you made the hundred thousand Let's just say well now your goal should be you know double that you always want to double what you make So now it's time to be that quarter million dollar trader and remember. It's not that hard to do It's only making a dollar a day on a thousand shares. Okay, so that's you got to start getting to that next level and Listen, if you didn't succeed what you want Let's say you're at a 50,000 off trader and that that's time to do 100 is the whole idea always try to you always want to double what you made from the year before all right and And it's gonna be a little choppy in the beginning get a little nervous You got a couple of swings, but you know, don't worry. It'll be patient. You'll get there So so I'm really excited for this year We're going into our election year. So, you know, we I don't know if we're gonna have that much I Would say that much of a rally in the market as much as we like unless all these China deals and the US MCA is gonna make a big factor usually take it on the second year that you'll start seeing it when when when jobs and everything else But I think we're probably I mean, I think we're still gonna go up. I mean, I think we're probably gonna hit I said this two years ago. I think we're gonna hit probably 32,000 on the Dow, you know Probably finish out next year. I think it's not gonna be as I don't know But let me tell you I tell you what we'll make this market run. Remember, I kept telling you how much haters are out there Everyone is hoping the market crashes and all I keep telling them short it Please short the market go out there and double triple down short it because you know what ends up happening If the market goes up guess what we have a short squeeze and we all know how those short squeeze work out We're all those stocks that we traded. Yeah, they'll come down crashing down But if we get a short squeeze on the Dow, could you see like 50,000 on the Dow? How crazy would that be and you know what that could happen that could happen You know a lot of people want this market to go down and if the market keeps going up They got a cover and it just literally just goes off and like I said people like oh that can never happen You know what we know could happen because we see it not once but every single day in The in the cyber group room short squeezes are the best So I just tell I'm just telling me please short it short your family's my short short your corporates I'm short everything just short it Please short short short short short short because I want to see a short squeeze that would be so much fun But as day traders, we don't care because we're in cash at the end of the day So whatever happens, we just buy it back tomorrow. All right, let's go Let's talk about a couple of stocks that are moving this morning. First of all the s SES when I throw that one on the watch list right here and As the stock's going up, you could see it the stock is moving pretty pretty nicely That basically was Michael's Michael got it made 6,500 this morning on that stock and you know just looking at it I would just be a little cautious on it because there are Where am I? There I am look at the long stock. You've got that big resistance levels right there right around eleven eleven dollars a share We need to see if that guy gets taken out, but a lot of you do remember the stock This was one of those short squeezes So here's a stock back in October. We traded it You should know really well go back to journals because if you did what you did by learning I take a note so you know how to trade the stock stock went from two dollars up to twenty six dollars Yes, that was one of our nice, you know, two thousand percent winners. Can that happen again? Absolutely, okay Absolutely, so let's have a looking fun to that one next one is plum P U L M another one that came up on my radar right here a lot of orders out here and You could see it just started making a big pop just now and You know, I'm looking at I'm trying to look here in the watch list and it's kind of it's kind of hard. There's a big seller at 128 There's a lot of big orders are out here, but it just made a big pop right here and you could see it It's got a nice little move right there. I did this halted actually It's a vaulted. I'm not really seeing the orders coming in time in sales Yeah, so just keep a close. I think it is halted. We'll see how it looks like I didn't open up yet But here's another stock a nice little cheapy. We'll keep an eye on that one that looks like a fun stock.  It is a NAS not not as that national so be very careful with it It goes tell by the tenths of a penny right there not seeing any orders out there on level three So remember that kind of gets me nervous. We'll put that on the you know Second protocol list. All right, remember you got two types of list We got the A list and the B list and the B list is that second protocol We always want to work on an A list like this stock as a VA nice move nice pre-market Stock has been so much fun for us. We made so much money with this stock in the last couple of days $1.50 stock went to $7 took a little bit of a hit came back down But it's right back where it started from so looks like it's right at the highs from from Yesterday so I mean up from yesterday from Tuesday actually nice move nice move unbelievable by look at 9 million shares already traded It's not even 9 30 9 9 15 yet Excel another one you guys have been trading you love this stock you guys did great with this one This thing has been treating us so well the last couple of days. I mean talking about a good move 80 cents to about 260 Look where it is right now. It's at 290 Level one stock too. It's a little bit easier to trade, you know, so kind of makes things a little bit easier Just remember it is a looks like a small cap to me PLX Another one that's got some good little move right here decent volume over 300,000 shares traded about 27% Spreads a little bit big right here loose body, you know, which is kind of odd You know knowing that trade that much volume, but I want to keep an eye on that one That's stock did really well from on Tuesday So we got that one on the watch list. What else we got?  To talk about writing taking a hit listen that to me when she sees not go up that fast and come down that fast That's not a short squeeze. Anyway, she's moving up pretty nicely right now So right is gonna go back on the watch list So we got a huge list to come back the day after New Year's Day to get this much and this much volatility. I think I think we have a really exciting day so far I didn't really see any good shorts out there, but it looks like everything looks more on the long side Now anything I'm missing. I did not I did not call out everyone Anything else I'm missing All right, so listen coming back from a day a day after you're starting new year Just be very careful.  But I think we'll go buy something nice. All right But in the meantime, we got a good list here. I'm pre-market We know sometimes you get a whole bunch of new stocks and we'll show up and then we'll if we see something We'll say something. All right, don't forget today. Also. We do have open house We're really excited about it. So look forward seeing everybody, you know here and you know At 12 o'clock.  It might not be for you and I was the other one. That's okay. All right So only way to find out is you got to try it. All right guys. Good luck today hacker trading We'll see you back at 12 o'clock for the open house And then we'll see at 2 30 for the afternoon meeting and see if we could trade going to the clothes, right? Good be safe everyone


Hey everybody, Matt here for Latent Sports Cards ripping 2022 Panini select football hobby. 6 box half case break number 6. Pick your team. We have Andre K Colts, Anthony P Bucks, Blaine W Seahawks, Brooks V Panthers, Christopher C Bengals, Dane B Broncos, Daniel C Cardinals, Raiders Rams and Eagles, David K Falcons, Dennis F Chargers, Edward P Cowboys Jags, Jeff M Ravens Vikings, Jeffrey H Steelers, Jim J Titans, Jim B Texans, Kim B Saints, Matthew G Bills, Nicholas T Packers, Paul G Bears, Peter B Lyons, Randy H Chiefs and Jets, Richard S Dolphins, Rory W Niners, Steven W Commanders, Tyrone F Browns, Wayne S Patriots and William D Giants. All right, let's do it. Another shield. Yeah, that'd be awesome. It's just one to go. Which team is that that's left? Just the Texans left in the next one. There's Kenny Pickett, that's rookie. Wow, 19 of 35, sweet level white. Well, that's how we want to start off. One pack, one mag. Congrats Steelers. Trevor Penning, rookie silver and Terry McLaurin. Yep, we need a mag counter up on the screen. Desmond Ritter rookie, Jomar Chase and Burrow, Austin Eckler, silver. Lawrence Malik Willis, rookie, sweet level, Jonathan Taylor. This is Chargers to 99, Sparks Jersey Mike Williams, Cam Taylor Britt, rookie silver, Wondale Robinson, A-Rod, Neon Icons, Nalyn, Tri-Color rookie, Damion Pierce, Matthew Stafford, Cooper Cup, silver, Cooper Cup, blue. That's to 199, John Runyon rookie, that is a John Runyon rookie. Luska Chanel, Sauce, Pickett snapshots and Bears to 199, rookie Jersey Otto, Vilas Jones Jr., Derek Young, and Robinson Jr., Neon Icons, J.J. Watt, Drake London, Tua, as Garrett Wilson, Tri-Color field level, that's to, I'm sorry, to the 75, and Ravens to 35, Otto White, Gus Edwards, his price. It's Trevor Penning, rookie red, Jonathan Wilson silver, and Icons silver rookie, Aiden Hutchinson, Kyle Hamilton rookie silver, Ryan Tannehill, Tri-Color, Kyler, Christian Watson rookie, Justin Jefferson snapshots, Breyer Muth and Brady, Tom Brady, and Jets to 75, that's rookie Jersey, Breece Hall, White parallel, Lockett, we have one, five, and six to go for the day, that is all we have left, Tyreek, Devin Lloyd rookie silver, Traylon Burks, Brock Curti, phenomenon rookie insert, Capri field level silver and moss, Son Haskins, Dak Prescott, red, Breece Hall, sweet level rookie, Julio silver, this is blue, Garrett Wilson rookie, that is concourse to 199, Jets, Roquan Devontae Adams, Sean Watson, Jalen Hertz snapshots, silver insert, Damon Clark rookie silver, Goddard, Najee Harris, and that is Bears, rookie Otto silver, Bayless Jones junior, Mark Jackson and Travis E.T.N. Jr., tie-dye to 25, this is Seahawks 49, Jersey Otto, Cam Chancellor, little Seattle, Brian Cook rookie, Demape, Dalvin Cook, Tom Brady silver, Jared Goff and Andrew Thomas, tricolor, David Bell, rookie silver, box number three, Falcons to 99, rookie Jumbo Jersey Otto, Drake London, you go, nice one, Falcons, that is to 49, Daniel Bellinger, rookie silver, Capri and Landjack, candy picket, phenomenon rookie silver insert, Brian Cook rookie silver, Zeke and Steelers to 75, Sparks Jersey, that's white, TJ Watt, Dustin Field silver and Brock Purdy rookie, say a Spiller and Corral, candy picket, sensations rookie insert, Kenneth Walker rookie silver, Olave, sorry, that was Olave field level rookie, that's Damon Bland, rookie blue, 149, Johan Dotson, sweet level rookie, Saquan silver, sensations Nick Chubb, Sam Williams rookie silver, Tray Lance, Silani Woods and Maroon Joe Burrow, that is to 149, Cowboys to 199, Jersey Otto, Tony Pollard, nice one, Dallas, Hendrickson, D-Hop, you know an icon, it's Kyler Elam, rookie silver, tricolor rookie, Onedale Robinson, Trevor Lawrence, Silver Hutchinson, biggest hit so far today, or in this case, it's phenomenon Garrett Wilson, candy picket, rookie silver field level, James Cook field level rookie, Mechie, there's Logan Thomas, tricolor, Forrest, Ron Bland, rookie silver, number four, number seven just has the Texans left, one team to go, my home snapshots and Packers to 99, rookie jumbo, Jersey Otto, Romeo Doves, go Green Bay, K-D York, Yonte Johnson, and tie dye, I'm sorry, to 25, Jamar Chase, Mr. Jamar Chase silver, Josh Allen, Eddie Jackson silver, is there likely, and to 25, that is tie dye rookie patch, Trevon Walker, Jags, silver, Tom Brady, neon icons, Kway Walker, rookie silver, tricolor, Chris Olave, rookie, Jordan Brooks, Cam Jergens, rookie silver, candy picket, rookie, Davis Mills, Purdy, neon icons, rookie, rookie silver, John Runyon, Marcus Jones, David and Jopu, and Trelon Burks, rookie tricolor, this is a 25, Dolphins, tie dye Otto, Cedric Wilson, Tom Brady, Tre Lance, phenomenon, Dak, Lou Matt Ryan, nice Colts color match, rookie silver, K. Dotten, there's Chris Olave, sensations, Tom Brady, Bernard Raymond, and Dolchich, Dolchich, David and Jopu, tricolor, Kevon Tibido, Vante Wat rookie silver, and Tua, all right, box number five, we have one three and six to go for the day, we have 10 spots left for the whole day and to pick your team style breaks, Patrick Mahomes, light blue, it's the 99, K. Ferguson rookie silver, Mack Jones, Alex Kappa rookie silver, Jack Jones, Tom Brady, and Titans to 99, Sparks rookie jersey, Chagosem Akankwo, Chris Olave rookie silver, biggest thing of the day, gosh, I don't know, we've seen a shield, we've seen, there's a Modsos Gardner, rookie white, 35, nice one, Jets, Alec Pierce rookie silver, we've seen a shield, we've seen a burrow one-on-one, we've seen Kenny Pickett, zebra rookie, there's Lamar Jackson, and Arod, burrow one-on-one was awesome, and Rodgers Maroon, Joe Burrow, Samir White, rookie silver, Breeze Hall, Neon Icons, and Falcons to 199, rookie jersey auto, Drake London, go another one for the Falcons, there's Chris Olave rookie, Watson, Trevor Lawrence, Silver Darius Slay, Mark Jackson, Romeo Dobbs, rookie blue, and Eagles to 49, that is auto light blue, Kenneth Gainwell, D-Hop, Kobe Bryant, rookie silver, Traylon Burks, Justin Jefferson Snapshots, Kenneth Murray, Silver, there's a Derek Stingley, field level rookie, nice Desmond Ritter, rookie tri-color, Traylon Burks, sweet level rookie, Josh Allen Silver, Traylon Waddle, and Packers to 199, rookie jersey auto, Christian Watson, nice Wyndon Green Bay, Chase K. Dobbins, Chris Olave rookie, Sean Watson Maroon, or I'm sorry, Purple, 75, Jamar Chase Silver, Justin Herbert, Kenneth Walker, rookie field level, right, no I'm sorry, that's Snapshots, Silver is the Darius Smith, there's a link, somebody wants to close out the, close out number seven, we have just the Texans left in that one, Mack Jones, Amon Ross St. Brown's Snapshots, Kevin Harris, rookie Silver and Kenny Pickett, rookie field level, says Rams 75, Jersey White, Cooper Cup, Justin Herbert, Silver and Trevor Lawrence, Jonathan Taylor, Kway Walker, rookie Silver and field level Mahomes, I'm Brady, Christian Watson, snap, sensations, Silver, sweet level Justin Fields, three more six boxers up after this, Eddie Jackson, Tri-Color and Panthers, the 299, Silver Otto, Johnny Hecker, runner hit there for the Panthers, CJ Moseley, how about a big XRC to close it out, Amon Soss Gardner, Neon Sines, Dak, Kenny Pickett, rookie Davis Mills, and rookie Blue, Desmond Ritter, that is concourse to 199, Kate Otten, rookie Silver, Blue Nick Bolton, Justin Fields, Trevor Lawrence, Silver, last pack, Adam Thelen, Tri-Color, Silver rookie, Tyreon Davis Price, and AJ Terrell, let's do a recap, we had, jerseys, Rams 75, Cooper Cup, that's, Titans, rookie, to the 99, Shagosam, Oconquo, Jags, Tideye, Patch 25, Travon Walker, and Steelers, White, TJ Watt, that was to 75, Jets, White, rookie Jersey, Reese Hall, Chargers, to 99, Mike Williams, Relic Otto's, we had Packers to 199, rookie Christian Watson, Falcons rookie to 199, Drake London, Packers, Jumbo Jersey Otto, Romeo Dobbs, that was to 99, Cowboys, to 199, Tony Pollard, Seahawks, Cam Chancellor, that one was purple to 49, Bears to 199, Rookie Ville Jones Jr., and Panthers, Johnny Hecker Otto to 299, Eagles, Kenneth Gainwell, Light Blue Otto to 49, Dolphins, Tideye Otto to 25, Cedric Wilson, Bears, Rookie Otto, Ville Jones Jr., to 199, Ravens, White Otto, Goss Edwards to 35, Falcons, rookie Jersey Otto, Drake London, that was to 99, and Steelers, that is Kenny Pickett, Rookie, Sweet Level White, 35, and that'll do it for the break. Thank you everybody, we have number seven up next.


According to the cast of Friends, it's happening. After almost 16 years of speculation and rumours... Anything is a possibility, Ellen. Okay? Anything. Yes. Right? Yes. CHEERING HBO have confirmed all six cast members will be returning for a Friends reunion. Ladies and gentlemen, this is Matt Rogers. Set to be one of the exclusive launch titles for HBO's new streaming service, HBO Max, the Friends reunion is set to start streaming in May of this year. The bad news is this will be a one-off stand-alone hour-long special, so we, unfortunately, won't be seeing a reunion season like Will and Grace. WarnerMedia have made the following statement. Series stars Jennifer Aniston, Courtney Cox, Lisa Kudrow, Matt LeBlanc, Matthew Perry and David Schwimmer will return to the iconic comedy's original soundstage, Stays 24, on the Warner Bros. studio lot in Burbank for a celebration of the beloved show. The unscripted cast reunion special, along with all 236 episodes of the Emmy-winning series, will be available to subscribers at the launch of HBO Max. Now, this means it won't be a return of the sitcom itself, but rather a celebration of the show. And it's a shame to think we may not see the actual cast back in character. But let's talk about their pay. Back during the original run of Friends, the cast earned an unprecedented US$1 million per episode. The Hollywood Reporter has reported that the cast will now earn more than double that wage, and will be receiving approximately between US$2.5 million and US$3 million just for their reunion. Now, let's look at who will be at the helm for this special. WarnerMedia has confirmed Ben Winston will direct the special and will executive produce, along with Friends executive producers Kevin Bright, Marta Kaufman and David Crane. The special hails from Warner Bros. unscripted and alternate television and full-well 73 productions. Aniston Cox, Kudrow, LeBlanc, Perry and Schwimmer will also executive produce the special. Director Ben Winston has recently been working on James Corden's Late Late Show, producing segments like Carpool Karaoke and Drop the Mic. He has posted to Twitter saying he couldn't be more excited to be directing and producing this Friends special for HBO Max. It is disappointing that it won't be returning as an actual sitcom, as I would have loved to see a more suburban side to the characters. Seeing Monica and Chandler living a settled domestic life with Joey in an apartment above the garage would be what I'd like to see. But what are your predictions? I'll be chatting with you guys in the comments. But be sure to subscribe as I'll be covering any developments on the Friends reunion. But until next time, this is Matt Rogers and that is all.


By age 30, for women, 90% of your eggs are dead. They're gone. And that's a big thing that women don't even know.  They don't have a clue that almost all their eggs are gone by 30. And by age 37 or 38, I think it's like 97%, 98%. And at 35, if you don't know this, that's actually called an advanced maternity age or a geriatric pregnancy. This is actually still a description by Modern Medicine. And women by age 35, it's an advanced pregnancy that has more risks for the child, birth defect, more risks for the pregnancy itself, more risks for childbirth and complications through all that, nevermind bouncing back, nevermind breastfeeding your child. Most young women today in America really have no idea that peak fertility is around 21, 22 years old. When you're the most fertile, the most beautiful, the most fit, the most healthy you'll ever be in your life, the most ready to have children, the best way to do it. And motherhood first, this is what this means. My grandmother had my uncle at 16 years old. Now by today's standards, that's like really young, but I can guarantee you it's a lot better than 36 or 40 or 39, like we're seeing today.


Hello, and welcome to the Rugged Rockhound. Today, we are cutting a whole bunch of rocks. All kinds of things, geodes, woods, whatever. Let's see what we got. We'll start off with a flat dugway geode. Okay. Yeah, see, they're not bad sometimes, these flat things. Pretty cool. Here we have some pink agate from Nevada. Looks cool on the outside. And... Oh. Look at that. Wow. Pretty cool. Here we have some kind of an agate jasper that we actually got over at Nephi at the petrified wood area. Whoa, look at that. So there's more to be found over there than just the petrified palm wood and other wood. Here we have some wood that fractured into several pieces. And it is very dark, but it does have some texture to it. Interesting. This is another little piece of agate that we found at the Nephi area. That was just very interesting. There was a little yellow, whatever's going on in there. Interesting. Here's some more bird's eye, gastrobot shell, the other side broke. Interesting, that shell looks like it crushed a little bit. Huh. Here we have some agate from the last Chance Road area. Where there's agate all over the place. Oh, nice. It's got some good red and yellow in it. Sorry for shaking the camera. Yeah. Nice red and yellow. Here I had a nice piece of snowflake obsidian. Decided to cut it open. Oh, look at that. That looks really nice, doesn't it? Snowflake obsidians. Sure pretty. Here's some more Vernon Wonderstone. Decided to cut it twice because it ended up being really pretty. Here we go. Look at that. Very nice. Now flip to the next. So cool. Beautiful stuff. Here's another one of those flat dugway geodes. Oh, okay. Nice patterns in this. That's pretty good. Here's another piece of petrified wood. Didn't look like much on the outside. And the inside's nice texture. But not very good color. Cool, though. Here's another nodule from Cisco and Yellowcat. Whoa. It's very britcheted. A little bit of red agate in there. Cool. Another little flat geode from dugway. The other side broke. So all we got was this side. Interesting. Here we have a geode that actually comes from the reservoir near Enterprise, Utah. Oh, look at that. Got some banding there. Some white opal in the middle. Pretty neat. Here we have some jasper. Got some nice patterns in it. Oh. Well, that looks better inside than I thought. It's like a picture stone. Yeah, it's like a jasper picture stone. Very nice. Yeah, I like this one. I don't remember where this came from. Here we have a nice little agate that fractured. Oh, look at those banding. That banding in there. It's getting dark out here. Hopefully it's not too dark. Yeah, look at that banding in there. Almost as cool as where it fractured. Look at that black in there. It's very interesting. Here we have some birdseye marble that fractured. But here's some nice pieces of it. This is kind of more just the regular birdseye. Here we have some agate from the Drum Mountains. It looked really good on the outside, so I'm very hopeful. Oh, yes. Yes. That looks good. Let me hold it like this. Oh, look at that. That is beautiful stuff. That is so nice. Yeah, I need to get more stuff like this. Here's a larger agate we found somewhere in Southern Utah.  It's got some nice patterns there. Here's some more Wonderstone from Vernon. Ooh, pretty. Not as good as some of the other ones, but still neat in its own way. And for the final one here, this is some of that bearite that gets replaced by agate down by Sisko and Yellow Cat. And, oh, look at that. Look at the red agate in this one. Oh, this is beautiful. Okay, I need to go out there and get bigger ones. More like this. Really nice stuff. Wow. That's it for today's episode. A whole bunch of good-looking stuff. I really hope you enjoyed it, and I hope to see you on the next one. Until next time, remember, there is treasure everywhere.


a very, very fortunate community because I mean it so happened that we are witnessing one of the biggest revolutions in the world which is called deep learning and very shortly in about I don't know couple of years or five years from now whatever we do all our lives would be touched in some way or other by deep learning and this extremely small community we are part of that revolution and we I mean we are contributing to that revolution and that's something that makes that should make all of us extremely proud about to be part of this great journey so one question that came up is from one of the audiences that what are they I think from this side of the audience so what are the applications is it restricted only to computer vision and there was some mention about the natural language processing also so the answer is no it is not deep learning as a science pants across multiple multiple sciences and segments and as I mentioned earlier it is going to touch each and every one of our lives in some way or other in a very very short while from now right I mean starting from the internet and the cloud I mean what do you buy what is your buying behavior what is that you take out from the shelf and you check the price and put it back what is the other thing that you take out from the shelf and you are satisfied with the price and you go to the billing counter and you decide to buy I mean that's that's all that is going to be analyzed by deep learning it is being analyzed by deep learning medicine biology particularly in the science of cancer reduction deep learning is is playing an extremely major big role like millions of CT scans are taken all over the world and all that is image huge image now if that is just imagine the amount of data that that that gets generated in all the CT scan centers only in Bangalore let's talk only about Bangalore if we can create a pattern out of the images that are getting generated and if some intelligence can go into it and somebody can predict in addition to the doctors if a data scientist like you you can contribute yourself and you can predict what this small aberration on the CT scan what it is likely to be isn't it great I mean this is something which is a big big field that is coming up on medicine biology media and entertainment something very similar security and defense okay there's a huge stadium 10,000 people are sitting there and how do the 10,000 people two people are not looking at the pitch they are looking somewhere else they are looking into the bag and they look very their behavior doesn't look right now how it's it's a 10,000 people stadium and the people are 10,000 people are sitting from 9 a.m. to 5 p.m. to witness the match so how does the the policeman or the security control room how do they identify two people out of the 10,000 who have a suspicious behavior and that has to happen real time I mean there is no way somebody can analyze the video after 10 days and go through 10 hours of data and look at each and every face and find that these two guys are looking funny no it cannot happen if somebody acts abnormal in a stadium cricket stadium that data has to be picked up that instance and signal has to go to the security control room and collective action should be taken to to do whatever needs to be done to if they are wrong people if they are not the right people so security and survey defense is a big big big focus area and last but not the least autonomous machines and just as I'm not going to go too deep into it so all our cars are going to be touched in some way or other deep learning and machine learning in a very short while from now and the software and all our cars are going to be software driven in addition to gasoline or diesel it's also going to be software driven I'll just deserve my comments on this because I have more detailed info on this moving forward so that sort of sums up the the the high level overview of what we're all deep learning is is is applied is going to be applied but this is only a partial list okay and this list keeps on growing all right okay so this artificial the race for the artificial intelligence is really really picking pace and it started as recent as 2010 on and summed up very well I mean he gave you a I mean I think he started in 1948 if I remember right and he he very very nicely gave you the the evolution of deep learning how what what what has happened over the past several evolution of artificial intelligence not deep learning artificial intelligence over the past I don't know 50 60 years but then last five years has been extremely interesting five years is a very very short period for any science to evolve the way it has evolved today I mean it's it's amazing I mean in 2010 that image that challenge was was announced and just five years from then in 2015 you name any major technical company it could be Microsoft it could be Amazon it could be Google Facebook IBM Nvidia Toyota Ford Tesla Audi you name any technologically advanced company from any field they are all into deep learning okay five years this has become the most important development in scientific development in the recent times and just gives a more flavor of what the numbers that we are talking about these are the numbers that are directly engaged with Nvidia I mean I just want to make it clear they we are directly touching these customers there are like tens of thousands who we don't work with and I'm sure that a big majority of you followed that category like 2013 we were directly working with 100 companies okay we know we knew them they knew us and we were working together and in 2015 it grew up to 3500 okay and this is the pace at which deep learning is going and there's an estimate that this is a 500 billion opportunity okay so I mean all I'm trying to tell is the gist of my talk so far is that this is a huge huge huge opportunity amazing science that is developing that is evolving really fast and we are all fortunate to be part of this now GPUs have sort of evolved to be a very very preferred platform when it comes to deep learning I'm sure all of you know that and so why is it so I mean is it I mean what makes GPUs so different from CPU we all know that CPUs are used for computation we all have CPUs doing fantastic work in on our laptops and desktops and and servers that sit in the data center so why GPUs are so preferred when it comes to deep learning there's a reason for that okay so let's look at the way a GPU has been architected and a CPU has been architected I mean let me clarify I mean there's nothing wrong in the way CPU has been architected that's not what I'm trying to tell but the fundamental physics the electronics that builds a CPU is different from a GPU because a CPU is is supposed to do something different than what a GPU is supposed to do how is it different so if you look at a CPU it has got a very finite number of cores I mean the diagrams show six cores and if you remember right the latest Zeon that we sell that we get from the market today has can go up to 18 cores one eight that is the number of maximum number of cores that a CPU can offer and it has got a large cache obviously now how does a GPU look like a GPU can have thousands of cores and the the latest GPU that we sell today can go up to 40996 4096 cores just imagine just compare the numbers I mean you cannot even compare how can you compare 18 with 4000 plus 4100 doesn't make sense to compare those numbers and so for any application which is highly parallel which can be broken down into multiple number of parallel threads and which can be processed to parallel e without having a sequential need obviously when you increase the number of cores it it'll run faster like I'm coming to whitefield instead of driving in on two lanes if I have 4000 lanes obviously there will be no traffic traffic bottleneck if I have 10 lanes it will obviously be much better than driving on two lanes now let's go back to deep learning we all know that as data scientists we all know that deep neural networks are highly paralyzed quotes they can be run on in parallel together and hence having a large number of cores to run your deep neural networks works much much faster than running it in a smaller number of cores and that is the fundamental difference between running your deep learning algorithms on a GPU and not having GPU to run your deep learning algorithms okay this is I mean once again I want to emphasize that I just I'm just putting forward the science in front of you why GPUs are so much preferred when it comes to deep learning now so we have a very interesting lineup when it comes to deep learning I mean any computation accelerated computing for that matter okay so there was one question there was a concern from a gentleman here whether it if it is if if GPUs are very expensive no they are not I mean they're very expensive if you want to set up a huge data center with thousands of GPUs yeah but that's not for you that's for the Googles and the Microsofts of the world I mean we are we have small startups we have huge number of startups and I know at least 10 faces in this gathering small startups who are harnessing the unreasonably effective power I mean that's what we tell when we talk about GPUs GPUs give an unreasonably efficient when it comes to deep learning and all startups can can make use of that say we have Tesla for the cloud which which obviously is meant for data centers and then then we have Titan X for the PC I mean you can have one two three PCs and have Titan X card plugged into it they're not expensive at all I mean if any of you are having a concern that moving on to a GPU based computation is an expensive solution I tell you you are wrong it is not okay please do not have that that sort of a concern in your mind I mean you can start with a couple of PCs with the Titan X card and now you all know that we have very recently we have announced the next generation which is called as the 1080 also which is even more powerful and then we have some specialized platforms for specific for automobile companies and then if you are into into creating intelligent devices like intelligent cameras or robots or chatbots etc then we have an embedded solution also I'll talk about it a bit later but please do keep in mind that GPU computation as a startup is not expensive I want to make it very very emphatic about it okay do not miss out on that all right okay so I mean as I mean there is another interesting science that is coming up I mean you should be aware of this also which is called as the hyperscale computing so we all know that high performance computing at our accelerated computing what do we do we have a huge problem a big problem and that problem is broken down into smaller number of threads and each thread is run simultaneously on in a paralyzed patient that is what we all know as the traditional accelerated high performance computing now hyperscale is another interesting development once again and the reason passed wherein it is not a huge problem that is broken down into smaller threads it is a large number of small problems okay typical and typically this is used for image processing and also for example I mean internet based applications and all the cloud based applications and all that is where the hyperscale data centers are mushrooming and keep in mind that even for that we have great solutions which are which is called as the I mean I don't want to really go into the models and all I just want to leave a message that we have solutions for all your computation needs irrespective of what your budget is and whatever your application is now I would like to introduce another very interesting offering which is called as the digits dev box this is a development box and this is specifically meant for deep learning training for creating your training networks okay now this is a very nice offering and it comes pre-packed with all the goodness or all the prerequisites of a deep learning of setting up a deep learning thing and you can choose to buy the digits dev box as it is from people or you can build your own dev box I mean all you need is a suitable server which can take up to four GPUs and there is a nice suite of software which is available which is provided free of free from nvidia just go to github and you can download it and it has all the nice software that you need in order to to create your own deep learning ecosystem and and look at the scale up I mean it is amazing for between for one GPU to four GPUs how good the scale up is when you're using such such platforms and then I mentioned about the Jetson TX one so this is our embedded platform let's say you are into creating computer algorithms for intelligent cameras or or you are working you you you are working on a project for for let's say retail stores etc etc you want a platform which which gets embedded onto your own device you are into robos you are into um drones etc that is where the Jetson which is our embedded platform plays a major role and I mean I don't want to go too deep into the speeds of the feeds but look just I'm sure as data scientists you all understand these numbers look at the numbers they are like unbelievably good I mean this this is the size of your credit card what are you seeing here the Jetson TX one it looks like your credit card and it is I would certainly say that it is a super computer by itself okay it can harness up to one teraflop of data I mean one teraflop of computation okay that's that and it has it it's a it's a it's a computer by itself it has got its own CPU memory GPU power etc etc so I want to ensure that you're all aware of such offerings from NVIDIA and and decide on what what you need to use and in April I mean many of you might have heard this term GTC that is the flagship I see a lot of heads nodding very happy about it that's the the flagship event of NVIDIA every year we have something called as the GPU technology conference GTC GPU technology conference that happens in Santa Clara and this April had been has been an exciting announcements multiple exciting announcements were made and the next generation of GPUs which is called as the Tesla P100 was announced at this and once again this is a breathtaking announcement and this is the the most advanced and most powerful GPU that has ever been announced so far and the science that has gone into this is amazing 150 billion transistors has been packaged into a credit card sized GPU which is an embedded GPU and it can go up to 5.3 teraflop of double pressure dual pressure and look at the the half precision which is it's it's more than 20 teraflops once again these numbers are huge huge huge numbers and in fact I mean I was fortunate enough to be there and when I heard for it the first time a few days before the public announcement was made I was taken aback I mean how can an embedded platform how can a small chip like thing can can do so much power and that is where I mean once again going back to the original question that we had I mean that is how GPUs are contributing so much to the to the science of deep learning and talking for particularly about the P100 because I mean why is it possible what is the science that that went into it okay this is what we call as the five miracles number one obviously is the Pascal architecture which which as I explained packaged 150 billion transistors in a small chip and then we employed multiple new methodologies we went on to the 16 nanometer FinFET technology and then the memory that we used is also a very interesting one which is called as the HBM to which is a high bandwidth memory to which which we this enables the chip to be built on a wafer on a which is which lies on a substrate that's what we call as a coarsed technology and then there is a very interesting technology which we announced which is called as the NV link I mean which we are so far we all know that GPUs the the bandwidth that is available to the GPUs is is given by the PC express bus right and whatever is the maximum bandwidth provided by the PC express bus that is what your GPU can do but what will happen if a GPU has to talk to another GPU what will happen if four GPUs have to talk to one another and what will happen if four of these GPUs and obviously they cannot only be talking to another they also have to talk to the two processors that are the CPUs that are available in the system so how is this communication why is it being restricted to the bandwidth of the PC express that is what is answered by this great technology which is called as the NV link technology and of course the new artificial intelligence algorithms I mean we are no longer a GPU only company we are a platform company and we the the importance that we give to generate to to the R&D that is the time and the resources that we send spend in creating great GPUs equal amount of R&D also goes into the software and we are slowly evolving into a we have evolved into a great software company also and all these the goodness that I talked that I talked about for GPUs they all come bundled with amazing improvement when it comes to the algorithms I'll be talking about it a bit later now oh I think this sort of repetition and last but not the least there is also a page migration engine this is a new technology that we have introduced on the P100 GPUs wherein you have a unified memory that is available for the CPU and the GPU that the system memory and the GPU memory are pulled together as a common pool and that huge pool is available for your computation okay no longer only the GPU memory is available for running your computation on the GPU you also can tap into the huge system memory or the RAM what we commonly call as and that is possible due to this technology new technology called as the page migration engine and so what exactly how exactly does it translate to when it comes to performance huge huge leaps in everything okay so this Tesla M40 whatever you are seeing here this was announced about I think about six six months ago six to seven months ago is when it was announced and obviously this the P100 is what we talked about and it can give three times computation and three times GPU memory bandwidth once again thanks to NV link and also five times GPU to GPU bandwidth okay now this is the type of improvement that we are talking about and this is a breathtaking pro product okay and it has already been announced and a lot of experimentation is happening it will soon be very commercially available in in I think about three to four months from now Q4 of this year is when it will commercially be available to all of you now in addition to announcing about the P100 we also came out with an announcement which is called as the NVIDIA DGX1 I'm sure if at all some of you have been following on GTC you might have some of you might have heard about it and this is the world's first deep learning supercomputer okay the world's first deep learning supercomputer it is a system by itself it's it's a full-fledged supercomputer and it's just a rackable one 3R form factor and it is specifically engineered for deep learning and it can deliver this one box can deliver 170 teraflops of half-pressure operations okay 170 teraflops I'm sure that people who are associated to the accelerator computing world you would you would you you would understand how big the type of computation is when I say 170 teraflops it's it's huge it's an unimaginably huge and it it encompasses eight tesla P100s and a whole lot of ecosystem that goes along with it and very importantly it accelerates all the major artificial frameworks that you are aware of okay so I mean this is just a block diagram which shows you what goes into a DGX1 all right so what does it translate it into I mean it's great 170 teraflops but is the physical way of me understanding it what this 170 teraflops means is to put it in very simple terms it can give you the compute power of about 250 servers that you are currently having in in your data center okay this one 3u super computer delivers the same performance of 250 servers okay without GPUs that are there in your data center so that is the equivalence of the power and so once again I would like to I mean remind you that if you are in deep learning please please do try out GPU otherwise you are losing out quite a bit and once again I mean this is GDC 2015 is when we announced the previous generation Maxwell architecture and what took 25 hours has gone down to two hours in just one year okay now I want to remind here that all these improvements are not only because of the silicon not only because of the hardware it also is because of the the amount of software the R&D that we that goes into the software that are available with GPUs okay so it's an end-to-end product family whatever we have seen so far for hyperscale computing for cloud based data center computing and for your embedded systems and dgx1 last but not the least which is the supercomputer specifically meant for deep learning training okay and this enables deep learning everywhere okay it could be a small startup using one workstation with titanics plugged into it or it could be a robos with the embedded platforms or it could be a data center with the Tesla or the car which is running around which has a specialized platform which is called as the drive px deep learning is everywhere that sort of sums up the the hardware port of it that I wanted to to tell you now let's move on to software and this is even more exciting than the hardware that we have discussed so far okay so when it comes to the deep learning SDK it is a continual improvement we keep on improving it again and again and again and and you can see some code from from the architect of tiano there the world is amazed at the type of improvement that we bring to the software that goes hand-in-hand with the hardware also so let me this is sort of a block diagram which gives you the software ecosystem that we provide with the GPUs for deep learning obviously the top applications today are computer vision speech and audio and natural language processing these are the top ones okay the maximum used ones the other others that I mentioned earlier internet and cloud and big data and medical imaging yes they are certainly there but these are the top ones and then you have a big family of deep learning frameworks that are available I mean I'm sure many of you are available are very familiar with these names that you have cafe you know your CNTK from Microsoft you have tensorflow from Google all these are very very popular they are the popularity keeps on growing and that their capabilities are also increasing and then you have so this is the ecosystem that is available and NVIDIA SDK provides multiple tools I'll be very briefly touching upon each of these tools and as data scientists and deep learning algorithm people you will be able to relate to the work that that I will be explaining we it spans across all these segments whatever could be your application or whatever framework that are using there are there are the best tools available to utilize the hardness of to utilize the power of GPUs okay and you name any deep learning framework okay they are all accelerated by GPUs and the SDKs that we provide all right so once again in GTC we announced something called us a NVIDIA compute works with this is basically the compute works is just a bundle okay and though there are four major advancements when it comes to NVIDIA computer works which is CUDA and Qoody NN and NVGraph and index okay I'll be touching upon each one of them very shortly and the big announcements or the improvements that we have made is in terms of digits I mean some of you might remember I mentioned about the digits development box which is a deep learning training system okay having capable of taking four GPUs and digits is the software that goes into the deep learning training system and then you have something called as a GAE I'll be talking about a bit later which is graphics inference engine okay graphics inference engine which is specifically meant not for inferencing okay we all know that we have been great when it comes to training how about inferencing so that is what is addressed by GAE graphics inference engine and you have an improvement on the Qoody NN which is a derivative of CUDA all right now let's start from the basics let's start from the basics I'm sure CUDA is very commonly known term to most of you here I mean what is CUDA CUDA is a parallel programming platform that is offered by NVIDIA and that is the software platform that is used as the base to build everything I mean be it the conventional accelerator computing or deep learning algorithms whatever it is it is all based on CUDA which is our parallel programming platform now CUDA was very generic so when deep learning as a science was evolving what we did is we customized CUDA and created multiple forms of CUDA which are tailor-made for specific applications that is what we are going to talk about now and CUDA as such the basic CUDA as such is undergoing tremendous improvement in performance and as I can see within a span of two years what we had in 13 and what we are having I mean we had in 2015 the performance of CUDA went up to five and a half times okay now all right okay now I mentioned that CUDA and then which is which is a very very specialized form of CUDA which is which expands to CUDA for deep neural networks okay so please make use of this and the best part is this CUDA and then is available free I mean so long as you are having your GPUs it is it is it is free it is not open source it is free all you have to do is you go to the developer site website in NVIDIA and you can make use of it and it is going to give you great performance when it comes to any framework that you might be using all right and there are multiple specialized as I mentioned specialized forms of CUDA something called as a CUBLAS which is your if you're using it for your linear algebra programming or you have CUSPARS for your SPARS matrix solvers etc and whatever and you have another very specialized tool which is called as NCCL which is a parallel programming tool which which enables scale up of your performance across GPUs when it is inside the one node or across multiple nodes also there are so many nice tools which are available which is what we call as the NVIDIA software development platform and if we wanted to ensure that you're all aware of this and please explore this and start using this all right okay now talking about GPU inference engine we all know that GPUs have conventionally been very good at training now with the GPU inference engine the GIE inferencing also has become fantastic and very advanced inferencing can happen with the help of this GIE once again this is part of the the P100 announcement that we made and watch out for this and start using this and you'll be amazed by the performance of inferencing that you are getting here and last but not the least graph analytics if you are into big data if you are if you are into finding out relationships between millions of instances how each instance interacts with the other then you have a great tool which is called as the NVGraph now I would like to once again emphasize here that all these are based on CUDA they all are formed upon one one particular software platform which spans across this so you can develop today in Titan X okay and you can the same algorithm with some recampillation can be ported on to your data center and it can be put it on to your embedded platform or it can be put it on to your dgx1 box at some later point in time you start low you start small you start on your PC or a workstation and the same algorithm can be put it on to into multiple platforms at the right time arises and the last topic that I am going to cover in a couple of minutes is what is the amazing year for self-driving cars I'm sure all of us have heard this this terminology Google car and and a lot of research that that goes into cars all and NVIDIA is playing an extremely big role when it comes to the autonomous the automotive industry also and all the major automotive companies are getting into this and they're all moving into the Bay Area to set up R&D facilities that can help in automotive in self-driving okay now obviously self-driving as many of you know is is is certainly a deep learning problem wherein the computer vision will have to identify passenger identify people who are crossing the road or it has to read the signs on the road or it has to take note of the policeman signs and act accordingly so obviously it is a computer vision problem and it's a deep learning problem and so that the new artificial intelligence driven car this is how it works so you have the NVIDIA DJX one which is used for your training which which goes into the car and there is a special platform which is called as the dry NVIDIA Drive PX which sits on the car and deep learning happens on the car I mean it it's not that it goes to the cloud and comes back the deep learning happens inside your car and the right decisions are taken and your car is driven by using localization and mapping okay so self-driving loops this is what happens doing a typical deep learning problem when a autonomous car is driven so a mapping of the terrain is happen is happening and then a localization to understand where you are I mean on and touched upon it I mean look at this call I need to understand where you are you're you're existing in a specific defined space that has to be done on on the road also and then the computer sees and then the right decisions are taken to ensure that the car is driven without the help of a driver and that is what we are talking about in autonomous driving system now it is not only autonomous driving system I mean many of you might think that how is autonomous driving system applicable in India it's going to take a long time it's also the the human machine interaction the HMI that happens is going to change a lot and all our cars are going to be more software driven in the days to come than anything else so we are going to have the next generation user experience and this is how all our cars are going to look like in a very very short while from now wherein you have a very nice user experience and you have all your cars are going to you will be able to talk to the car the car will understand what you're talking natural language processing and it'll react to your commands you can just sit inside the car and say take me to the airport and it'll be taken you'll be taken to the airport and these are all I mean happening I mean these are these are great sciences which are happening which are evolving and this is I mean I don't know how many of you are aware of this is amazing so in by the end of this year we are going to have a race which is called as the Robo race okay and all these cars are self driven okay there are 20 cars which will be self driven 10 teams 20 cars and one mandate is that all the 20 cars must be identical from the hardware point of view okay only the algorithm has to be different and who writes the best algorithm his car his or her car comes first and here in wins the race and needless to mention all these 20 cars the drive PX which is the Nvidia platform is is the brain that drives the car so great things we have talked about have a touch have I explained everything about about GP competition what do you think not sorry questions yes and this my last line not really I have not explained everything about deep you can be combination I've just touched the tip of the iceberg if you want to know more about it if you want to learn more about it I invite all of you to the first ever GTC the GP technology conference we are very happy to announce that it will happen in India not in Santa Clara in India in Mumbai on December 6th we really want to see all of you there you can present a technical paper you can present a poster or you can simply add and learn like what you're doing today I mean most welcome and last but not the least they we also are conducting an emerging company summit okay which is part as part of the GTC and present your papers take part in the competition and the awards are amazing I mean whatever particularly if you're a startup you have to take part in ECS in emerging company summit keep watching out our web space and you will get more and more information on this all right that is what I wanted to to cover and once again Mumbai December 6th we really look forward to seeing you there in GTC India okay thank you questions we have questions questions and then you can catch on the offline later hello yes hello is NVIDIA selling Digits DevBox in India I'm sorry is NVIDIA selling Digits DevBox in India as of now no but you can help you can build your own Digits DevBox which is exactly identical to what is commercially available and we can help you in building it hello yeah hi this side in last GTC 2016 we saw that NVIDIA was actually collaborating with many universities could you speak up I'm not able to hear you hello in last GTC 2016 we saw NVIDIA teaming up with many universities they were actually collaborating with US universities and there they were giving some hardware especially DGX-1 to the universities for their research work can we see similar things happening in India yes the answer is yes we certainly have very structured university relationship program one of my colleagues is managing it the answer is yes we have big plans for academic institutions in India contact us and we will give you more details yeah can we know about the contact details yes I'll give it to you just that when we have full oh you can write to me it is available on the screen just no down the email and the phone number you can write to me I'll pass you on to one of my colleagues who handles the university relationship program thank you so much yeah I think there's just time for this last question so yes yeah I'm here hi yes I'm just wondering how does your hardware compare with our human hardware of brain how does our hardware compare with what the capacity of human brain in terms of neurons etc okay how does it compare the capacity of the brain okay okay sure thanks I mean so we have multiple levels of accuracy that that that are used to to measure the the performance of a deep learning algorithm and most of the times we find that I mean I think Anand also mentioned about it recently most of the times we find that the accuracy that gets thrown out of a deep learning algorithm is even better than that of a human brain okay and I'll bring a very interesting perspective to this so one common concern about autonomous driving is that how safe it is but the studies show that when the world becomes fully autonomous in terms of driving autonomous driving will be safer than humans driving around okay yes I think should take the questions offline everybody's proceeding towards tea please be back in the auditorium by 1140 because we will start the next talk at 1145


Many times, many times. Wow, I mean, how many people can actually say that? How was it like meeting Arnold? Oh, Arnold's great.  You know, the last time I saw him was probably about, I don't know, a year, year and a half ago.  So there's a place called Sun Valley where I go quite often in the summer for mountain biking or snowboarding in the winter. And he's got a property there. So I often bump into him there. And he's just always been very nice, very courteous to me.


What's up everybody, Jason here for jaspyscasebreak.com. We're getting away the last two spots in the NFL Mixer Random Division Breaks with this 2019 Prism Football Hobby Pack Break, number 49, Random Teams. And again, guys, we do the break first, randomize the customer names, teams should be the hits out then at the end. We're randomizing only the customer names that bought in, or the original customer names that bought in, of course, and top two get in to this break, which is coming up next. Here we go, guys. Roll. Two and a six, eight times. One, two, three, four, five, six, seven, eight. Canard down to Michael P. Eight times. One, two, three, four, five, six, seven. Eight times. Titans, down to the Cincinnati Bengals. All right, again, guys. Quick little pack filler, so no trading.  Last pack mojo. Imagine that this was 2021 and we pulled her freaking out of 99 Joe Burrell out of a pack filler. But Ryan Finley though, and I remember people were buying this guy. Ugh. But hey, still auto's an auto, right? 70 out of 99. Bangles, that is going to Michael P. Kind of a bit of a teaser, but there you go. Let's just write it off about Joe Burrell. All right, so there you go, man. Switching scenes now, and we're doing the main giveaway, which is the top two spots in the mixer. Here we go, dice roll it, boom. And it is a snake eyes just two times. Top two, one, two. Boom. Michael, it's your night, Michael. And Michael at the top. There you go, you had the odds, man. Congratulations, boom. So you have the final two spots. Coming up next is the mixer.


We take our perceptions for granted. We feel that we see what it is, but is this really the case? How reliable are our perceptions? This course is about the senses, with particular reference to vision and audition, and we will explore the biology behind painting and music, and some words of great masters, from the Renaissance to abstraction. We'll discuss the ideas of artists as neuroscientists and how art reveals how the brain works. From here, we'll get into philosophy, the philosophical question of the foundations of knowledge. Plato's Allegory of Decay is actually a scientific question. Is that of how can we grant knowledge and the difference between science and belief? Those are topics with quite and rather strong political and social impact today. The aim is at bringing biology and humanities together, because that's not requiring a specific background, and it gives the opportunity to learn some neuroscience, and so to go through the works of some great thinkers, scientists, musicians, artists, but from another perspective. What I bet is that next time you step into a museum, you will do it with another eyes.


Unlatched containers, dangerously placed on articulated vehicles, have fallen over time, resulting in gloom, loss of lives and pain. With these incidents, the dreams and aspirations of many have been caught short. In this special report, plus TV Africa's correspondent, Destiny Mama, X-rays some of these incidents with a view to curbing the menace. Now the menace of containers dropping on Nigerian roads has become a thing of major concern. People die daily, records that even journalists do not have. The question is, how can we end this menace of containers falling on Nigerian roads and killing innocent lives? The growing rate of crashes involving articulated vehicles on Nigerian roads is a source of concern to pundits and particularly stakeholders in the transportation industry who could give anything to see it reduced drastically. To understand this trend with a view to ensuring control, we speak to the Director-General of Lake Estates Emergency Management Agency, La Sema and Sector Commander of Federal Road Safety Corps. They need to ensure that their drivers are not on rocks. They need to ensure that their vehicles are well maintained. And again, while they are carrying on this container, they need to tighten it down on the vehicle. And they need to ensure that the proper tones that the articulated vehicle supposed to carry is what is carrying. However, we appeal to other road users that when you have never seen these containers articulated vehicles carrying containers going anywhere, you should give them gaps. It is the fault of the man that wants to make business through a tool, through an instrument that he has failed to put in proper position. Why do we have all these problems all over? You know you want to go into all aid business. And we are not even saying don't do that. FRS is saying as long as you are still able to maintain minimum safety standard, which is as minimum as we mentioned it. Just how can this menace be completed, obliterated, knowing its negative impact on socio-economic activities, vis-a-vis filled deliveries among others? Experts advise on the need to explore the rail and pipeline transportation modes as viable alternatives. These products are carrying containers. Some of them are traveling to the north, to the east, and all of that. Ideally, we should be using the rail system to move distance. I must acknowledge what the government has done so far. There have been some investment in the rail transportation. And I think there is a decision now to construct the rail system right into the ports. We need to be able to deploy all those other ways of moving goods across the country. Rarely is one of them for fuel. We need to develop our pipelines. Commuters and pedestrians are not left out as some have fallen victims of these accidents. Some have been maimed. Others met with their untimely death. Files on these menace have been piling up over time. Most recently, a driver at Alakoda Ijora Bridge who could not run away like his lucky boss was killed in the process. The case of Chima, Naipa, and Chidema Ajoku were worked with the Federal Airport Authority of Nigeria fan comes to mind. Their family members could not hold their grief as they still wallow in agony. Chima's mother narrates her a deal. Nothing can compensate me. This is a human being that has been trained in the university. I mean, private university for that matter. Apart from that, you see that bond between mother and child, what can compensate it? So I'm not even talking of that at all. What I am talking of is how they will stop these menace so that other people will not continue to fall victim. Nigeria as a developing African country has gotten to a point where it can emulate developed countries to explore new trends of containers with inbuilt rails that latch them effortlessly yet properly to curb this painful menace which has left green memories on the faces of residents. Destiny Momo for Plus TV Africa.


Okay, welcome to Space Invaders Part 12. Again, this part of the series is being recorded five years after the original series. I'm here to kind of fix some of the mistakes I made years and years ago and to kind of address a lot of the questions that I've gotten over the years on this particular tutorial which has proven to be extremely popular. I think actually Pong is better, but I'm glad people are tuning in. So let's get going. So let's take a look at what we have right now. And it's working. In the last video, fix the player so that it moves like Space Invaders did continuously instead of a little bit at a time. As you can see, I'm not very good at this game. Yeah, okay, so that's what we have. But this is very unlike the original Space Invaders. There should be rows of aliens here in the program instead of just a few aliens. So let's go ahead and fix that. Unfortunately, it's not a difficult fix, but it takes a little bit of math, a little bit of basic math. Now we're also gonna learn, once we start putting some more aliens on the screen, it's gonna slow down. So we'll learn how to speed up the program as we go along. So let's take care of the aliens first. So here we've got where we create the enemies. And what I wanna do is I'm gonna create 30 enemies instead of five, okay? Because the original game had a ton of enemies up at the top. So this part of the code is still gonna work. It's still gonna create the number of enemies that we need. And you can see here what it does is it's gonna put them in a random location. So if I run this, this is not what we want. We're gonna have a ton of aliens. Okay, and actually it looks a little bit more like the original program just because they kind of moved weird like that because the system was so slow back then. But anyway, let's get rid of that. So what I wanna do is, actually I should have kept that on the screen, is what I'm gonna do is I'm gonna start here and I'm gonna move across and I'm gonna put 10 aliens here. I'm gonna put 10 aliens here and 10 aliens here. And I'm gonna do that with a little bit of loops and a little bit of mathematics. Okay, so now I've already done this, tested it in another program. So I have the numbers, pretty much how I like them. So what I'm doing is I'm doing enemy start x. So that's the starting coordinate of the first enemy here on top left. And I used minus 200 and I have enemy start y and that was 250 is what I had decided on. And then I got a key track of which enemy I'm putting on the screen and you'll see why in a second, okay? So again, I played around with the numbers. I found some numbers that I liked. You can go ahead and play around with them as you like. Something to consider, notice I type very long variable names because it makes my code easier to read. I could call this ESX, ESY and EN and I see all kinds of weird combinations but it really is really helpful if like me you're coming back five years later to try to understand what your code is doing. So what I'm gonna do is instead of x equals a random number, watch what I do here. Enemy start x, okay? So for the first enemy, it's gonna be minus 200. And then I'm gonna put plus 50 times enemy number. So what's gonna happen? The first enemy is actually number zero. So the first enemy is minus 200. Plus 50 times zero is zero. And then when it comes around again, the second enemy, which is gonna be number one, it's gonna be enemy start x plus 50 times one. So it's gonna move over and move over and move over and move over, okay? And so then when I'm here, I'm gonna say y equals, where's that, enemy start y. Now, I'm gonna run this and it's not done yet. So I'm gonna run it and show you what happens. Nothing, okay, because I missed something, all right? So I put them all on top of each other. So what I gotta do is at the end of this, I have to say enemy number plus equals one. So I gotta increment the enemy number each time, okay? So I'm gonna go ahead and run that again. And so you should see them form a row. Now you can see they went off the screen. So yeah, and then it's gonna come down. So we gotta do 10, then come down, do 10, then come down, okay? So you see there's a few problems with the code. So what I wanna do is I'm gonna say updates the enemy number, the number, okay? So if the enemy number equals 10, so remember, every 10 enemies I wanna drop down, okay? I set enemy, actually I don't need to do that. Basically enemy start y minus equals 50. So every 10, every 10 aliens, it's gonna drop down. Actually I do need this right now, I realize it. Enemy start x minus equals 200. And now I gotta move everything over to, actually no, I don't need to do that. I can leave that like that, sorry. And then I say enemy number equals zero, okay? So what I'm doing is I'm saying zero, actually up to nine, hits 10, come back to zero, all the way up, come back to zero, all the way over. I think this is, oh, and then I have to, here, I have to do, I don't have to do that, that's fine. I think that's all I need to do. It was a bit more, it took me a while to figure that out actually. Okay, there we go. And it's a little bit off-centered. So why don't we change that, sorry, that's bugging me. So we say enemy start x equals minus, let's try minus 225, let's see if that does it. And again, this is the process, you know, it's, yeah, that's much better. Okay, look at that, I like that. Okay, so we can see we've got the aliens and they are going across the screen. Actually it looks a little bit like the old school space invaders, just the way it's going slow. Now you see how things have slowed down, but that's okay. Now let's see if they reverse. Okay, so they are reversing and we can leave it at that. I had, oh, that, I forgot about that, I don't like that. Here's the other thing, I don't want this alien to come back on the screen like that. So I think that's probably the next thing I need to do. And interesting, all right, that's working. So this is a little bit different than how I had practiced. But anyway, so why don't we do that, let's fix that. So I'm gonna come down to, okay, if there's a collision between the bullet and the enemy, you set the bullet. Okay, what we do here is the enemy is, how can I put it, the enemy is set to a random position. Okay, so the easiest thing to do at this point would be to move the enemy like off the screen somehow and to just so we don't see it anymore. So what I'm gonna do is I'm gonna set the enemy into the center, so zero, and I'm gonna put it like way off the screen for now, like 5,000. Let's make it 10,000 just to be safe. So we never see it again. Now, this again, this isn't an ideal way of doing it, but based on what I wrote originally, this is probably the easiest. So let's run that and see if it works. I might have some problems later with that, but let's save that for later, cause we saw a few other things. All right, so we can watch this go super-duper slow. Ooh, I did it, really? Okay, I missed them all. Geez, let's try that again. I think I'm gonna miss them again.  Yeah, I'll tell you what. Why don't we go ahead and speed it up first and then we can go from there? So the reason that it is running slowly is just the way that the turtle module works. It updates the screen at its own kind of pace, and since we have a lot of objects on it, it's updating when we don't need things to be updated, et cetera, et cetera. So what we're gonna do is we're gonna use something called Win Tracer. So up here, when we set up the screen, we're gonna put WN.tracer0, and what that does is it shuts off all of the screen updates. So if you run the program now, this is what you're gonna see. Yeah, nothing. Okay, so, and it might lock up. Terminate, okay. All right, so what you have to do is inside the while true loop, way down here, you're gonna put Win.update, no zero, just nothing. Now I'm gonna run it, and now watch what happens. Watch carefully. Okay, so you can see how things now are moving just super fast, which is obviously not quite what we want. Okay, so we sped up the program and aliens are running off the screen. So what we're gonna do is basically just change the speed now. So for example, on my computer, now your computer might be different. So on my computer, we set the, we're gonna set the enemy speed to, I'm gonna try and set that to 0.2. On my computer, it's gotta be pretty low number. Your computer might be slower, it might be faster. So this number could be very, very small based on the speed of your computer. The bullet speed, I'm gonna set that to, I'm gonna try five. And the player, I'll try like minus three and plus three. It's kind of the relative speeds of everything. Maybe the bullet should be faster. Let's make it seven. So let's try it. Okay. Yeah, that's a bit more like Space Invaders was back in the day. Okay, so now keep in mind, all of the aliens are at the top of the screen. Now I don't know, this is probably gonna cause a problem later. I know how to fix it, but let's get to that bridge. We'll cross that bridge when we get to it. So, and it's still reversing, which is good. And again, some of the structures, you know, the computer, like the programming structures I used back then, five years ago, I didn't particularly, I wouldn't particularly do now, because I know more, but they worked, which is all, you know, it's always a good result. So again, with the speed, you just wanna kind of play around and find the numbers that work on your computer. But the relatively speaking, the bullet's gonna be about two, two and a half times faster than the player. And the enemies are gonna be about, about a 10th of the speed of the player. In this case, it's about .06, but you get the idea. So that's it. That's it for this one. So we have now have multiple invaders in multiple rows, which is what Space Invaders is supposed to look like. And we have been able to speed up the program using Win Tracer. And again, zero shuts off all updates and lets us control when we update it. And so what we wanna do is update it each time through the loop. And then that gives us a much smoother animation, a much faster animation as well. Okay, so yeah, so there you go. That's part 12. Part 13 will be coming, maybe not today, but definitely will be coming soon. So stay tuned.


Being a part of the Tigers, you know, it's really where I think I truly grew up, you know, in an operational unit. The Tigers have taught me about dedication and excellence and consistency, but also about how you work together as a team to achieve goals that you couldn't have done on your own. When I got here, the Tigers had just gotten back from deployment. It was just, it was all inspiring to see the knowledge that the squadron had coming back from that deployment. We won the Raytheon Trophy, so the top air superiority squadron in the entire CAF. And the quality of pilots that come out of the Tigers is just unmatched. Being in the Tigers a shy or force-based, being the largest seed unit in the United States was a lot of hard work. However, the people that worked there are very motivated and very capable. So being in that environment for my year and deploying with the Tigers definitely prepared me to take on any challenge. And that's what I love about the Thunderbirds.  You get to work with different AFSCs, you get that camaraderie, and then you get to share that with the rest of the United States. I've spent probably the most of my Air Force career shy or force-based in a total of about four years, so I'm excited to go back to the old stomping ground. I cannot wait to come back in 2022 for the Shire and Space Expo in April. It's going to be a blast. We hope everyone comes out and has a great time because we definitely will. It's going to be awesome. I hope everyone comes out. And if you see me walking around, just please say hi. All for freedom.


Quick ways to create lead magnets. Hey, are you trying to create a lead magnet? Or maybe you don't even know what one is. In this video, I'm going to explain exactly what a lead magnet is, how to create them simple and easy and get real results, and we're starting right now. Everyone, JRFishow here. Welcome to my channel. If you're new to the channel, make sure you subscribe. Click that subscribe button down there and hit the bell notification. The bell notification actually is going to tell you when I do a new video, but you'll need to click Get All Notifications. If you do that, you're going to know when I do a new video. Now, I believe that everybody should have the tools and the tips they need to succeed. The only difference between all those people out there making millions of dollars a year and you is knowing what to do and having the right tools. And on this channel, I share all of that with you. I've sold millions of dollars of both digital and physical products online over the years. And I've come across things that just plain work and things that don't work. So I like to share those things with you so you have that advantage. Now, have you already tried to do a lead magnet? Do you know what one is? Put your comments below so I can see what stage you're at. And if you have questions and I can help you out, please put those comments below. I answer all the comments. Now, I've got a bonus for you. At the end of this video, I've got a $97 Ecommerce course, but you know what? You can get it absolutely free. So just click it on the link. So that's going to be at the end of this video. I'm going to give that to you. All right. So let's get going.  You know, I remember the first time I heard this, I had no idea what it was. But really, it's just an incentive that marketers use to attract potential buyers to getting on their list. So it's actually a little piece of information. Maybe it's a PDF, you know, maybe it's a webinar, maybe it's a whatever, but it gets them on my list so that I can give them something for free value so that they can give me their name and email and I can start marketing to them. Why do you need a lead magnet? Well, quite honestly, every single business needs them now. People are past that stage of saying, well, I'm just going to join your email list and see what shows up in my email box. People don't want to get emails unless it's something really targeted towards them. So what a lead magnet does is it actually gives them a piece of information they're particularly looking for. It can be a checklist. It can be a PDF, whatever, but it actually gives them real content and real value and it will make them want to give up their email and name and give it to you. Now, the more specific you can make this, the better. If you had 10 steps to give better customer service, you may or may not get that many opt-ins. But if you say download this case study of a company that used my 10 steps to improve their customer service and see what happened with them, that's a little bit more interesting, a little bit more compelling and you're going to get more opt-ins if you word something like that. So now that you understand what a lead magnet is, let's take a look at what's going to make your lead magnet good. In other words, what's going to make people actually opt-in for your lead magnet. Now, there are seven things that you want to do that are going to make your lead magnet absolutely irresistible. So let's go through them real quick. Number one, solves a real problem. So if you can word that your lead magnet is going to solve their problem, then people are going to probably opt-in. So, you know, we sell survival food. So a lead magnet for us could be, you know, protect your home in the event of a break-in. And that is interesting to somebody.  And that's something valuable that somebody may opt-in for because we're actually solving a real problem. That's something, you know, our customers out there want to protect themselves. We know that. They're preparedness people. So if we give them something of value like that, they're very likely to opt-in for it. Number two, it can be something like promising one big win. So let's say you're in the weight loss arena and you may do a lead magnet that says, lose seven pounds in 14 days by doing these three things. Now, if people hear that, they're going to be like, well, yeah, that's a win. That's what I want to do. So I'm definitely going to opt-in for that. So think of it in those terms also. Also, number three is be super specific. So your lead magnet, let's say you're teaching people how to write sales emails. It may be something along the lines of five ways to double your clicks on your emails by using this method. And people are going to look at that and go, well, five ways. I want to know what those five ways are. So be super specific about it. And it's going to really speak to the people that are looking for that particular problem solved. Number four is the exciting part. Here's the exciting part. Quick to digest. And what do I mean by that? Make it short. It shouldn't be real long. We're not looking for a hundred page document. Nobody wants to read a hundred page document. They want something short. It can be as little as one or two page PDF. And here's the cool thing about that. It doesn't take you long to do something like that. And it gets them the result much faster. So if you can condense it and you make it easy to digest, which is number four, then of course you're going to get more opt-ins because they're going to look at it and say, yeah, I can grab this and get a result real quick. I don't have to spend two days reading a report. So don't do very long lead magnets. They don't work. Number five, make sure it has high value. You know, whatever you're giving this consumer, you're actually showing them what your company is, what you are right up front.  So make sure it has high value. I've even taken some of my courses. They're a group of videos and gave them away for free so that somebody could actually see what we can do and we can help them with their business. And the cool thing is if you help them right before they buy anything from you, they're more likely to buy from you. If you help them and get them results in advance, that's something you want to think about too. High value. Number six is instantly accessible. Okay.  Don't make them opt in and then you're going to send them an email and then they're going to have to click and then they're going to have to download it. As soon as they give up their email and name, take them to the page where that product is, that PDF is, those videos are. So make sure they get it quick. People like instant gratification. And if they have to go through two or three steps like you sending them an email and having to download it, then they may never open that email. They may never see that email and then that's where it's going to stop. So you want to put as few barriers in front of them as possible. And the best way to do that is make it instantly accessible. And number seven, so so important, demonstrate your expertise. Whatever you give them in this lead magnet, make sure it's something that really shows that you have value, that you can bring value to their life, that you can get them results. Because understand this lead magnet is kind of like that first date. It's going to be your first and last opportunity to make that first impression. So if you don't give real value, if you don't demonstrate what an expert you are in this particular area or what your product can do for them, then of course they're probably not going to buy from you later on. Okay, let's look at my top 10 types of lead magnets in order of how well they convert. Now the number one one is a checklist. Number one is checklist. People love checklist because it's easy. They can set it down, they can go through the checklist, they can mark things off, they know they got the thing done. So number one is a checklist. Here's a couple examples of some artwork for a checklist and you can see it really leads the person to want to give up their information because they want to get this result. So these are a couple really good examples right here. Now the next one I really like is called a cheat sheet.  I kind of get ahead of everybody else. Now here's an example of a cheat sheet and this one was created for bloggers and it says 52 headline hacks, a cheat sheet for writing blog posts that go viral. Now bloggers want their posts to go viral so this is a very benefit driven type of lead magnet for them. It also removes all of the, you know, having to think and come up with ideas.  It's all right here for you. The next one, number three, is a template. Templates are awesome because it allows the person getting this particular lead magnet to just fill in the form. It's already set up, it's already organized, and all they got to do is fill in the template. Now this is a really good example right here. It says, where can I send you my 72-word magic email template. So it tells them right up front that all they have to do is use this template and it's not real long. It's only 72 words and they can start getting results almost immediately. Now these are also very useful if you're an expert or a VIP in the industry because they want to know exactly how you do things and they want to copy those things. Okay, if you want to make your lead magnet even more powerful, you can actually combine it and make it bigger than what it originally is. Instead of one template, you can have a whole bunch of templates. Now if you notice Digital Marketer did this one here, it says the ultimate Facebook ad template library. So what this does is it tells the viewer that they're going to have a whole bunch of templates to do a whole bunch of different ads because they know they don't have to do just one ad. They've got a bunch of ads to do and this can help them out tremendously. Number four works really well and that's called a swipe file. Why does swipe files work so well? Well, quite simply put, the person doesn't have to do anything. All they have to do is take your file and use your file and get results from it. For example, the ultimate social media swipe file includes 72 headlines for social media posts that you can simply well swipe and you could use these headlines for your social media. Number five is examples. If they have an example of something that already worked and they can use that to help them out, they work amazingly. Now for instance, Bidsketch, a proposal software, uses a sample client proposal as a lead magnet and gives it out to people to get them on their list. In fact, you could turn one of your popular posts into a lead generating machine by optimizing it for your keyword plus examples and then adding an example lead magnet as content upgrade. The next one is a script. Scripts are awesome because they actually tell the person exactly what to say and do. For instance, if they were going on a job interview, you could actually provide them with a script for job interviews so that they get a better result and hopefully get the job. Another great example of this popular lead magnet creation by Marcus Craig is three persuasive video scripts that you can steal. Number seven is a toolkit. Why? Well, because everybody wants to know the exact tools that the experts are using. The people who are super successful in the industry, if they know their tools, they know they can get the same results. If they use the same tools. Now the next one is a web app. This is a really cool one because they have to actually log into your site to use this particular app and if they're logging into your site you get their email so now you've built your list and they have to keep coming back to your site to actually use that app. For example, Brian Harris created this web app called List Goal which is completely free to use. However, when you sign up for it, Brian gets permission to email you and he can sell you other things. Number nine is a resource list. Now resource lists are great because people can just go to that list and find the information they need on a particular subject and it saves them a ton of time and that's what this is all about is saving them time. My giant guest blogging index has been a huge hit for that very reason. For someone looking to write guest posts, all the tough research has been done for them so it makes it super easy to do. And my final one, number 10, is a calendar. People love calendars. If you provide them with a calendar, for example, if you're in the fitness industry like Bloggotties, you could offer a workout calendar. This way your users don't have to think about what exercise to do every single day because you've already mapped everything out for them and it's all planned. Now, if you're having difficulty with how to make these, I would just go to Upwork or Fiverr. You can hire somebody to make any of these for you. Now if you found something here that you think are pretty interesting that you're going to try, let me hear your comments below. Have you tried to do your own lead magnets? What results did you get? Put your comments below or if you just have questions about how to do any of this. I really appreciate you listening and as promised, there's a $97 e-commerce course you can get absolutely free. Just click in the link in the description below and you can grab it. If you haven't subscribed yet, go ahead and take time and do it now. Click that button below there, the subscribe button and next to it there's a bell. It's going to sound like this when you click it. You don't know until you try and then once you click it, you want to click on turn on all notifications so that you can be notified every time I do a video because just because you subscribe, you won't know if I do a video unless you turn on all of your notifications. Thank you so much for listening. I appreciate it and I'll see you in the next video.


We could go down here to the accounting side on the left hand side and This is our chart of accounts So we saw that one account here happens to be a cash account if you don't have a cash account you might start by going here and setting up an account that's going to be a Bank type of account. That's the key because if you hit the drop-down That's the one that you can connect to the bank as opposed to another one where you can't connect to the bank We're going to use this one basically as a as a parent account will do and then we'll put the subsidiary Accounts that we will create as we connect the bank feeds under here By the way, if you're in the business view to find where that is located We could go into the bookkeeping and the chart of accounts. That's where the chart of accounts is on the business view


That will encourage two of our seniors to go to college, so this is what it's about. We have worked on this for quite too much now, and the purpose of this style show is to raise money for scholarships. So we've invited all of these vendors and the local shops, and as you can see, the turnout looks great. And we've worked really hard. All the process will go. We're going to have two scholarships. We're going to provide two scholarships to two deserving seniors.