The following is a presentation of TFNN. The morning market kickoff with your host, Tommy O'Brien. Good Monday morning everybody. I'm Tommy O'Brien, company alive from TFNN. Hope you had a great weekend out there. Thanks so much for starting your trading day off here and we picked things off. Quite the acceleration on Friday, right? You talk about an acceleration, man. Things looking, especially Rosie approaching 1 p.m. Eastern time and then you had quite a sell-off to the tune of more than 65 points. It was a rocket ship to the downside overnight on a Fibonacci basis. We got right back to about the 3A2. We're almost right back to that level right now, that sell-off on Friday. You got markets in positive territory. We have a small amount of S&P companies. I think we got nine S&P companies coming out this week as we're nearing the end of earnings season and we get CPI data out Thursday, which will be a big economic data point beyond that. We got schools starting in Florida, man. Schools started today. Today, some schools starting Wednesday. We got orientation for one child today. We got one child already in school that's starting today. August 7th, man. So the summer, pretty interesting. We go forward. Most people have a good month left in the summer. Not in Florida, man. Florida, schools kicking it off today and we got markets in positive territory with the S&Ps right now up by about 19 points. That's 410th percent of the positive. We're trading at 4517. NASDAQ 100. Not quite back up to that 3A2, I think. Let's check it out. Didn't look like quite the bounce that the S&P was having. No, pretty similar. Check it out. Yeah, right at about the 3A2 of that sell-off from Friday. NASDAQ 100, up by half a percent right now. The Dow up 310th percent and the Russell up by about 410th percent. Crude pulling back a bit. Quite the volatility last week. We spiked down to $79. We make it up to $83 towards the end of action on Friday. We're just under that number this morning at $82.15. Higher crude prices though, right? You take a look at the daily. You're talking about higher prices higher than anything we've seen going back basically nine months. That's gonna start putting an impact on the headline number on the CPI. We get that number on Thursday. Just look at where we are from about a month ago when we had about a 67 handle towards the end of June. Everything in July was positive prices continuing that trend as we come into August. We jump over to the gold contract right now. Gold off about $1 at 1974. Gold with some volatility recently especially with the currency action going on. And we jumped in notes and bonds. You could say that was the story last week even the last couple of weeks, right? Even the last few months, man. This pullback from 116, 117 on the dot to a price point on Friday before we surged higher 109.24. Now we have the jobs number on Friday and things escalated pretty remarkable in terms of how this happened with where notes and bonds were versus where the market was, right? It was a one-way trip in notes and bonds, man. 830 volatility and then we trade to higher price and lower yield. We claw back some of that last night but we're pushing the highs this morning right now and markets are nothing like it. Remember the market rollover started at about 12.45, one o'clock in the afternoon. Pretty interesting in terms of how those came down. That's your 10 year right now, 111.04. You jump over to the 30 year. You got the 30 year right now down about two ticks, 121.27. Nearing the price action we saw towards the end of Friday as well, you got to jump over to the dollar index as we're talking yields and bonds. Dollar index, similar action, right? What's happening here? When we're getting lower yields, we're getting a weaker dollar. If you got lower yields, you're gonna have less demand for the dollar to go after those yields. That's the correlation right now and what is happening, that's helping the market a bit. So the market right now is liking lower yields, weaker dollar, stronger market, S&Ps up by 20. You got the dollar index at 102. You back it up on the daily. It's been quite a run though from where we were about a couple of weeks ago, man. You were down below 100. We drive up to almost 103. We're trading at 102.04. So it'll be interesting to see as we go forward. We got about six weeks left until the next Fed meeting, excuse me, in September, coming towards the end of the summer of course, and it's gonna be interesting to see where the data drives the dollar, drives yields as we go forward. We wrap up this earnings season. We come into September. We come into another Fed. And before you know it, we're gonna do an earnings all over again. Now, a little bit bigger picture. We've been talking about the student loan payments, man. They start in two months. And boy, the more I keep thinking about it, right? Three to $500 from every single person that's paying those is the average. We're talking about I think 27 million payers. It is an obscene amount of money folks that's gonna be sucked out of the economy. I wonder if that's really gonna start to hit things. I wonder if we come into the holiday season and it's gonna be kind of the perfect storm of events, right? In terms of really some of the lag, hitting with the Fed, consumers getting hit on that front as well. And then consumers getting hit on the student loan payment front, which really is gonna hit discretionary income for those that that money mattered. And there's a lot of people, man, we're 400 bucks, 300 bucks, 500 bucks a month. Very difficult for that not to matter for a majority of Americans to put it lightly. That begins October 1st, remember, man. So you got people planning, I'm sure, as you come into August, September, right? Those first payments starting in October. If you're thinking about planning for the purse, making plans, maybe you start tightening up towards the end of August, September, and you're really gonna have to tighten up when those monies start coming due in October. So it'd be interesting to see what happens in the final quarter of this year, right? We're only into August 7th, but September is next month. October, November, December, those payments have started. We got three full months at the end of the year, and we go from there. All right, where else we're going? Let's check out the VIX as we kick it off this morning. Quite a spike on the VIX last week. We're off of the lows. We're at a level of 16.78, quite the spike on Friday to 17 and change. And we're just under that level this morning as we get a little bit of a lift in the market to 16.76. Yeah, so man, Dan and the Dan talking about the Tesla CFO stepping down. Chief Financial Officer. A little bit of negative action, probably on that news right there, right? Drops about three bucks back to the lows of Friday. Elon and Zuckerberg, they're gonna fight it out in the cage match. They're out there like two teenagers battling it out on social media over the weekend. I exaggerate a bit, but not that much. But yeah, CFO stepping down. Gotta be pretty interesting running the finances of a company for Mr. Musk. Can not be easy working for him, I imagine, on many accords. Tesla, a little bit lower this morning. Let's jump around to some of the fang stocks as we kick things off. Amazon, quite the acceleration last week and they're gonna be higher this morning as well with the market. We're up to 141, you're up almost 10% on Friday's action. Strong numbers for Amazon shares. Apple, a little bit of a different story last week. You're gonna catch a lift with the market this morning to 182.87, now think about it, man. This is how quickly things can roll over, folks, okay? Apple had a one-way trip, man, from January 1st. That trip is over, okay? At least for Apple. That was quite a decisive break. I don't even have to put the channel lines on this trend to show you where the trend is because it's so well intact in terms of where you were on January 1st. You were well-defined all the way up to basically the highs coming into August 1st and that has been a decisive break. Now, as our man, Bud Rolfs, used to say, love him, miss him tremendously always. He was the channel master. If you're unfamiliar, folks, did a show at TFNN, had a newsletter, he was all about channels, man, taught us all so much and he would say it's gotta test that channel. Boy, to test that channel on Apple, man. I mean, you gotta get back up to almost like 195 to test that channel. Seems tough to do, but not impossible, even on the lower boundary, right? Maybe 194 or something like that. And I'm even taking the acceleration this thing had in March. Not even cherry picking the lows of January, but pretty well-intact channel line there. For Apple, you break decisively out of that channel line and we're trading this morning at 182. Get back up there, you're talking about $12. We'll finish up this point when I get back. I was gonna say, folks, you're already $16 off of the highs. $16 for a company with almost 16 billion shares outstanding. Stay tuned, folks, we'll be right back. If you're looking for potential trading setups in the stock market, then Rocket Equities and Options Report is a newsletter you should try. Tommy O'Brien delivers options and equity trades when the markets present them using a combination of fundamentals and technicals. Sign up for Rocket Equities and Options Report today with a 30-day money-back guarantee so you have nothing to risk. 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Visit TFNN.com and try Mastering Probability 30 days risk-free today. TFNN, educating investors. TFNN has launched the Tiger's End, hosted at Discord. TFNN has been educating traders for more than 20 years with live programming hosted by a variety of professional traders during market hours, the Tiger's End, available to all tigers and tigeresses for just $1 for the year. There's no catch or added costs when you join our community of traders. Sign up today and become a part of this educational community of traders. Just visit the front page of TFNN.com. Welcome back, folks, finishing up that conversation with Apple. We got markets in positive territory here, so Apple trading this morning at 1.82. You see the well-defined channel line, man. The entire channel, not even the line. In terms of the acceleration this thing had since March 2nd, what's so interesting here, you just extend that channel line to the left. And it pretty much kicks off from where you were on January 4th. You got a little bit ahead of itself, you back test that line, and you're at 181. Now, the reason why I mentioned how quickly you could roll over the type of money we're talking about, man, 16 billion shares outstanding. Every $10 is $160 billion in market cap. And you're talking about $16, what are you talking about? You're talking about $250 billion in market cap, shaved off Apple in a heartbeat, okay? That's talking about from where this thing was, August 1st, it's not even a week ago. And you got a quarter trillion dollars shaved off a company's market cap. So be careful here, folks, at some pretty lofty levels in markets across the board, some pretty lofty valuations as well. Trucker yellow, they go bankrupt. After debt labor woes pile up there, it's Y-E-L-L, right? What's their symbol doing?  There's probably gonna be some liquidation value in this thing. Ever since they talked about going bankrupt from 43 pennies up to $5 last week, you were chopping around $4.50 on Thursday. You almost pushed that level on Friday this morning. $2 seems more feasible for that company. But around 99 years, unfortunately, nearly 100 years, yeah, I saw 99 on one of the other articles I was reading. Assets of $1 billion to $10 billion there. They got 12,000 trucks, I think. 30,000 employees, 22,000 of those teamsters. And yeah, they've been battling it out with the unions. Unfortunately, they couldn't do anything in terms of the unions. The company, there's been a lot of concessions from the unions, that's what they're gonna say. Going back to the last 10 years, this company almost went BK in 2010, I believe it is. This is the journal article I was reading this morning. I had the Bloomberg article up there, truck or yellow files for bankruptcy. They're gonna liquidate. The company's chief executive says yellow's closing after the chapter 11 filing, costing some 30,000 workers their jobs. And they got a lot of debt out there, man. Yeah, 22,000 position by the teamsters. 12,000 trucks is what they have out there. Huge trucker did business with Walmart, did business with Home Depot. They swallowed several rivals years ago. They got union concessions over the past 15 years. There were cuts in there as well, securing a government bailout in 2020. I think that was $700 million that they got that's on their books right now. Big acquisitions in the early 2000s, slow to integrate the business. But pretty interesting, right? These big companies, when they buy other big companies, man, they gotta get it down quick or things happen. Yeah, in 2010, the company averted bankruptcy after the teamsters agreed to take cuts in pay and benefits. So, you know, the devil's always in the details.  They're gonna be in this conversation, get ready for them. The teamsters took pay cuts and in pay and in benefits in 2010. Okay, so it's not like it's been a story of the car makers or something like that where you had, you know, what? We all remember the stories, right? Of just room-sweller people that the car makers couldn't fire because of the contracts and all that stuff. Not exactly what's going on this time. But the teamster did block companies proposed operational overhaul this spring leading to lost business with customers and difficulties in refinancing about $1.3 billion in debt that matures in 2024. Yeah, so the battle has been on, nonetheless, yellow, they're going BK and I read in here as well that, yeah. Since the 1980s, bankruptcy filings by trucking companies have virtually always ended in liquidations even though most companies seeking chapter 11 protection seek to preserve their businesses as going concerns. Chapter seven would be the full liquidation but that's probably the way it's going. That's how truckers usually go. The last failure of a large trucking company, 2019. Caledon, sell Caledon and less than truck load carrier, New England Motor Freight. Yeah, chapter 11 in liquidated. The strategy in such filings is to retain some control over the disposal of assets lost in a seven liquidation. So they file a reorganization chapter 11 but really that's just to give them a little bit more control and when you're running a trucking company it seems like the real deal there is if there's a path to reorganization you would have got it done. Once you reach bankruptcy it's basically a liquidation. All right, let's talk a little bit of crude. Yeah, we talk about it. We got CPI coming out on Thursday. All right, and that'll be an important data point of course, rising oil prices are bad news for drivers and the Fed. Now, crude prices are up more than 18% over the past month. You're gonna see it at the pump, man. I mean, we've been talking about it here, right? A lot of bright people have been talking about it of course, there's your price of crude. People love to tout the headline number of inflation and boy it has been helped tremendously by the slide that oil has had for the last year. Since June 9th of 2022, man, we have been experiencing lower prices up until about the last month. Absolutely remarkable pullback we've had in crude. Of course the Russian invasion of Ukraine, a huge accelerant of that but look where we are on the gas prices, man. National average 387 as we're coming into August, usually a pretty big travel month, final month of the summer, even though we kicked things off back in Florida already. But yeah, we were pushing above $5 of those war spikes. The beginning of the war, we were just under where we were at actually made it to a low of about $3.20 but we're pushing $4 right now folks. That's a rise of, on a percentage basis from where we are talking about more than a 20% rise and the price of gas there that is gonna start weighing on the consumer prices. Now here, consumer price index changed from a month earlier. You have the overall number in pink. You have the core number in brown here and you can see the dramatic shifts that we've had to lower prices but you don't have to go that far back to see how far overall was driven higher when you had crude prices spiking and you may be coming to those lines again, man. Yeah, crude prices as they say will likely buoy overall price pressures heading into the Fed's next meeting in September when many investors expect to pause and interest rate increases. Not sure the Fed is gonna blink over $82 a barrel crude though. Not sure that's where they might start caring about the impact of that price on consumer prices because listen, if crude's at $85, do you really think if Chairman Powell raises the interest rate to 5.75% that that's gonna bring it down? No, that's not the way things work, all right? So yeah, and it's a great point, G-bolts, in terms of he says, funny how energy costs are not really a component of CPI and it goes into all aspects of it, okay? In terms of they are a component but then there's core components that don't have as much variation and then where the Fed likes to focus is on what their policies have an impact on, okay? And you can argue that and I'm sure Chairman Powell not gonna speak for him, okay? And I'm no fanboy of his as in I think, you know, he's got a really tough job, okay? One way or the other, man, you can make the argument that he was late and inflation soared. Well, many people have said that it wasn't gonna come back down and it has, okay, to some degree but what they wanna do is they wanna have an impact on the prices and crude, there's so many other variables besides fiscal policy like that that monetary policy I should say that they're just not going to matter as much when it comes to rate hikes which is where the market really is concerned most of all I think. All right folks, markets in positive territory, S&Ps by 18 in the green, we're coming back for the open, stay tuned. Building wealth trading in the stock market seems impossible to most people. They think it's too volatile and risky. Most people aren't going to take the time to educate themselves on how to do it right but you're not most people are you.  All TFNN subscriptions, books, software and courses are available at tfnn.com and I'm even going to tell you how to get them for less. 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That high 45.22, we're just over that price level at 45.22.75, we're just above the 382 and the pullback on Friday's action trading at 45.23. NASDAQ 100, we're up by 101 points. That's 2.30% just under the highs we had pre-market at about three in the morning. Dow up almost 200 points well above where we were pre-market, 35,349. The Russell gives it up on the open. Russell, the weakest index right now, only up by about 110%. You see the sell-off, the Russell had, let's put this back to a five minute chart just so you can see the open there. Little bit of a sell-off of the Russell, pushing the overnight lows versus the Dow catches a bid to a certain degree, 35,365. NASDAQ's small bid and the S&P, small bid as well, man, relentless market. Still sitting at 45.22. A literal stone's throw away from all-time highs. Pretty remarkable across the board. Let's jump around to some of the fang stocks as we kick things off, Amazon shares. They give it up a bit, with them only up about 6.10%. They were pushing a little bit higher on the open. Apple shares, they give it up as well, down 4.10%, right? Look at that, Apple negative today on a positive market day. You jump over to some of the other magnificent seven, Google shares up by 8.10%, Microsoft shares up by 7.10% this morning. We jump over to NVIDIA, they catch a bid up by 1.5%. We talked about Tesla, the CFO stepping down, they trade lower, they claw back some of those losses. Tesla, only down by about 4.10% right now. All right, and what else do we got going on? Let's talk about, yeah, this one's an interesting one from Fast Food, giving up on dining in. So McDonald's Burger King want big investments in eating areas, prompting pushback from franchisees. Don't like spending money, folks. And I'm not, listen, that's a business owner, okay? Especially when you're told by corporate, right? Because many times they are managing a budget like any small business, a franchisee, look at this channel in McDonald's, and be careful here, man. We're talking about channels, all right? Talking a lot about channels, that's your three-year weekly, let's see. That's your five-year weekly, look at this. I keep going back, look how well-defined this channel line is, man. Okay, this is going back five years. You got out of work during COVID, let's back it up all the way. Yeah, basically been in a well-defined channel line since the lows of 2015, man. Not exactly parallel. You could probably shift this up a little bit. Maybe you get some parallel nature there, with degree, but you get the point. We are trading off of that level. You've done it on a couple occasions, almost hit the top when you started that channel in the beginning of 2018. But coming out of COVID, nonetheless, McDonald's, okay, they are positive by three-quarters percent today, and I found this article interesting, because they're talking about how much money they want people to spend for dining areas that nobody goes to, basically, right now. Americans are eating their burgers, fries, and nuggets at home, in their cars, at the office. When's the last time you're saddled up to the fine dining area of Mickey D's? I can't remember, decades. When's the last time I had McDonald's? Probably stole some chicken nuggets from the children within the last week or two that we got at the drive-thru, okay? I'm not the only one. Less than 10% of visits in most US McDonald's restaurants are dining customers. Surprised it's actually even 10%, okay? Now, I know sometimes the kids, there's a Chick-fil-A around here that's got a nice play area. I think there's a McDonald's that's got a play area. That's what I remember, man. I remember going to your remember one, talking about the 80s, man. Remember the 80s? Listen to me. Remember when they had so many great cool play areas built into the McDonald's and all that stuff? I used to love it, man. Nonetheless, that's the only time it actually makes sense in my head that you hear that people going to McDonald's, whatever it be, they have a play area, especially if they have one inside, because in Florida right now, folks, it's extremely hot in August. I don't complain too much. We got it made, all right? You just gotta be by the water or something if you're outside or have some air conditioning. Across U.S. fast food chains, diners ate 14% of orders at a restaurant in the first five months of this year, less than 21% before the pandemic. It's a 33% decrease if you're playing percentages on percentages, which is always a little bit deceiving at times, but nonetheless, quite a drop. In June, diners ate 14% of their fast food orders at the restaurants dining area compared to 22% in 2015. Just massive shifts in our life in terms of how we go about our life and the shift toward basically drive-through, right? Or online delivery to that degree. Now, you don't need big dining areas, okay? But McDonald's and other chains are developing new restaurants centered around drive-throughs and carry-out with very little or no dining option, but here's the kicker, man. Still, fast food restaurants are reluctant to give up on the dining room. They want their restaurants to look modern, inviting, and fresh. McDonald's, they pledged to spend billions of dollars to help US franchisees pay for digital kiosk, modern furnishings, and other improvements, beginning in 2017. They now expect US owners to renovate dining rooms every 10 years. That's a fast one, man, every 10 years. Duncan Steve, perfect, man, hang it out. Listen, you're gonna have it made, man. Mickey D's, they're gonna be some fly. And listen, on a longer-term basis, okay? You see how, do you remember when all those McDonald's was dilapidated? Right, Steve, you must, man. So our man, Duncan Steve, Mickey D's Tommy, he says, I'm 64 and I go there once a week or two for the Wi-Fi and a copy, coffee, no people in there really. And what's so interesting though, do you remember when they were also outdated, man? When they were just the, that's really where the tail end of those big play areas, right? Yeah, the play areas, they weren't everything super dated. And what did they do? They came in, they forced the franchisees to update. McDonald's generally look super modern these days and they've overtaken that stigma of being this trashy old fast food place. So you see the benefit of it, but they want them to spend about 300 to 350 grand every 10 years on renovations. And folks, let me tell you, man, you know it, 10 years flies. Can't believe how fast it goes. And after 10 years, stuff can still look okay. Okay, that's where things get interesting here. Yes, you want to keep it fresh, but I could see how there'd be some pushback when you're spending 300 grand every 10 years to basically redesign the inside of your restaurant. And they talk about here, they got to throw everything else out. That's basically what you got to do. Now, these are coming for you, okay? The kiosks, all that stuff. Yeah, you better believe order and pay here. Don't talk to a real life employee. McDonald's has been on a tear. The real estate plays real as well. Burger King spending millions of dollars to encourage U.S. owners to renovate their locations as well. But what I found so interesting here is that you're talking about 350,000 and a full update can be as much as 750, but probably if you're on the ball and you're pushing it every 10 years, you're talking about 300 to 350 grand per a franchisee. That's the money they're putting in that they're expected to freshen up their dining rooms, front counters and bathrooms with approved designs every 10 years. So McDonald's not messing around, man. Longer term as an investor, probably a good thing for that brand, for that company, because you just see what happens when it goes to the wayside. But as a franchisee, you can understand there'd be some pushback, man, when nobody's coming inside, and basically it's probably more of like a brand issue. Right? You want to have them be the fresh company. You need everything up to date, even if nobody's coming inside the store. And they're probably right. Nonetheless, Donald's doing well. Market's doing well. S&Ps, up by six tenths percent right now. NASDAQ, up by about half a percent. Dow charging higher, catching a bit on the open. Up by eight tenths percent. Stay tuned, folks. We'll take a look at some of the other equities. Moving this one over right back. 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They're having some problems, man, with their numbers down by 9.2% for Tyson and we jump over to Tyson. Come on, where are we? There we go. They're gonna close four plants as chicken business slumps. Meat processor, $417 million quarterly loss, lower sales, it's a tough business going on right now for Tyson, man. A loss of $417 million, that's $1.18 a share. That compares to a $750 million profit a year earlier. Quarterly revenue declining, 2.6% to 13.1 billion. The results were lower than what analysts had expected. They're down in the pre-market. They're down 35% over the past 12 months. You have a shrinking supply of cattle driving up the price that meat packers such as Tyson paid to secure that livestock. You have a glut of chicken and pork on the market and lackluster consumer demand have been weighing down wholesale prices that resale the retailers and restaurants pay. They're trying to cut costs. They're gonna shift production from four of the chicken plants to other facilities by the middle of 2024. And this is where I write, you see things like yellow where they say they were slow to implement the changes when they would acquire a company and combine those companies. It's remarkable how long it takes these companies, right? It's gonna be a full year. They're gonna shift production from four chicken plants to other facilities by the middle. And listen, I'm not, I mean, I can't imagine the logistical deal to shift four production plants out of business, take all that business, put it into other areas. They estimated the total charges of three to 400 million related to the closures declined to say how many workers would be impacted. That's basically only a loss for them, right? But in May, Tyson shut down two chicken facilities in Arkansas and Virginia, laying off roughly 1,700 workers. So if they shut down two, it's 1,700. They said in April, they're gonna eliminate 15% of its senior leadership positions, 10% of the corporate roles. They're really trying to trim it, man. And yeah, not the numbers they were probably looking for. We take a look at a longer term chart back to the depths of COVID yet again. And the only thing I will say is if you love this stock at 100, you're gonna love it at 50, right? Yeah, tug and cheek to say the least. We back it up on a big one, monthly chart. And yeah, you are back too. Let's see if Fibonacci base is where this thing is back. Been quite a pullback. And Tyson's gonna be fine, folks, okay? Chicken's not going out of business. Chicken's one of the healthiest things you can eat, folks. Really is. Great source of protein. A lot of times not a tremendous amount of fat. Fat can be healthy as well. But chicken, you gotta give it some good spices or some good marinade. The only thing about boneless, skinless chicken breasts. Super healthy for you, a little bit bland. That's why you get no fat in there.  Chicken, very healthy for you. Interesting, because if you watch the program, I think it was a month or two ago, right? I was talking about how beef is in such a problem and how everybody's eating chicken and no one's eating beef. Well, the only thing I'll say is those trends have been in place. And the market has shown some of those trends because you had Tyson chopping around $10 to $20 over the period of 2003 to 2012. And yes, you're back to 50, okay? Things got a little bit out of whack, I think, at the COVID highs, et cetera. Nonetheless, seems like they're trying to write that ship, taking their licks right now. And yeah, maybe that's an area you start to scale in if you're looking for a position, if you're a short-term trader. At least you get you back against the wall, man. $50 looks to be on that chart somewhere where it has some level of support. Doesn't mean you won't get below it. Just in May, we were down to 47. But nonetheless, as you see, back to basically that area of $50 for Tyson on weak numbers for Tyson. And they're trying to write that ship by short closing plants, saving money, et cetera. All right, what else do we have going on? Well, folks, a week from today, we got our man, Teddy Kegstad. And this reminded me, like, I'm looking at candles, okay? And, you know, Teddy had talked about when we were talking about this last Wednesday, when he was talking about this webinar coming up, a week from today, August 14th, 4 to 5 p.m. It'll be a 60-minute webinar. It'll be live. You'll be in there with Teddy. This is a standalone webinar, folks, okay? So you're not signing up for Teddy's Tiger Forex Report. You're not signing up for recurring subscription. The cost is $97. It will be archived. And Teddy's gonna be talking about candlestick patterns for stock and option strategies. You can click on the front page. You can read about what he's gonna be talking about in there. Okay, and talking about what patterns he uses for stock and option strategies in today's volatile trading environment. See how to implement various trading strategies based on the patterns that develop in the market. They can be applied to stocks, ETFs, and options, using these methods, maximize your trading capital. So these patterns, Teddy's written a book about candlestick charting, folks. And, you know, sometimes it's the basics that really are the most important thing that you learn. And I remind myself of that a lot. We've been talking about channels a lot today, right? Channels are some of the most powerful indicators out there. And everybody, it seems like uses candlesticks these days because they graphically and visually represent so much data. And that's the cool part about so many formations on a candlestick is that the reason why the formation makes sense is because it's a visual representation of supply, demand, data, market behavior. So check that out. It'll be $97. You can sign up. A week from today it will be archived. I expect we'll have a good turnout there. It'll be live in the discord room. So that'll be good. We talked to Teddy, of course, Wednesday at 40 past the hour. So we'll talk to him in about 48 hours from right now. All right, what else we got going on? We talked about Mickey D's. We talked about Tyson. We talked about trucking. Yeah, we got property loans. How about it? Well, let's take a look. They're talking about AMC and the Den. And yeah, you talked about high risk trade, right Dan? A little bit of volatility today. Up to 519, you're back to 506. The news out there, Barbie's done a billion dollars. Not bad. Take a look at the longer term chart though, man. You're up by 2.5%. I don't know. It's interesting to see how this business goes forward. That's a daily for you. You were down at what, four bucks on June 23rd. You're up to 506. And as you said, something about risk, right Dan? Rightfully so. Yeah, because for the risk tolerant, yeah. Another solid box office weekend AMC. And it has been. That's quite a number for Barbie. You got Oppenheimer out there as well. Some of the biggest numbers they've seen recently. But boy, this chart's looking a little skittish even for all that. Now you jump over to Warner Brothers Discovery. Barbie's their movie. They've had the acceleration off of their numbers as well. You're up to 1420, basically flat this morning, even though you have a positive market, right? There are bigger fish going on than one movie man or one weekend in this whole industry, is my take on things. I mean, you jump over to AMC right now. You jump into the earnings tab. Is that right? Do they have their numbers tomorrow? Is that right, Dan? Maybe they do. August 8th. Do they? Let's see. Yeah, they're not out yet. They're gonna have their numbers tomorrow. So AMC's out with their numbers tomorrow. And they are a $7 billion company right now. Really interesting to see if anybody goes for the brick and mortar play that's online. You probably wait for them to go BK and pick up a few theaters, imagine. Imagine you let them go BK, right? And then you try and go in there and buy the cream of the crop in terms of maybe you're trying to segment out what you're buying. Maybe you don't want all their theaters, right? As opposed to paying $7.5 billion for that business right now. Stay tuned, folks. One more segment, we'll be right back. TFNN has just launched their new trading room, the Tiger's Den, hosted at Discord. TFNN has been educating traders for more than 20 years with live programming hosted by a variety of professional traders during market hours. And now they are expanding their reach with the Tiger's Den, available to all tigers and tigeresses for just $1 for the year. There's no cash or added costs when you join our community of traders. 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When you subscribe, you'll get a weekly report from veteran day trader Larry Pesavento on stocks you need to pay attention to and you can trust Larry's analysis. After all, he's got 45 years experience as a day trader. Larry will also provide daily charts, videos, and data on the key markets that he's tracking. Expect notifications from Larry on market movement you need to act on at any time. First time subscribers also get a 30 day money back guarantee. If you're not satisfied, let us know and you'll get a full refund within 30 days of signing up. Subscribe to the Fibonacci 24-7 newsletter today. TFNN.com, educating investors. Don't forget, you can listen to TFNN live on your mobile device 24 hours per day. Go to TFNN.com and hit watch Tiger TV. That's TFNN.com and hit watch Tiger TV. Welcome back folks. We got the S&Ps up by 22 points. Pretty much just shopping around where we started the program off at. We're up by half a percent in the S&Ps. NASDAQ 100, a little bit weaker maybe than the S&P. Pretty similar though. They're both up by about half a percent. Strengthening the Dow, weakness in the Russell. Dow catches a bit, up by 7 tenths percent. Russell catches an ask, you could say. Catches a sell as they're in negative territory. Only major index. You got crude right now. Trading a little bit lower during the session as well. Crude pushing the pre-market lows of 81, 82. So much for 82 dollars. We were at 83 overnight. You trade through 82, we're at 81, 82. You jump over to gold. Gold off about $5 right now at 1971. Great day to try out a subscription to the Gold Report folks. My dad puts out new issues on Monday morning. Check that out of course. We've had some action on gold. You jump over to the dollar index right now. Dollar index pulling back from the highs we had pre-market, 102 and 09. Basically right near 102. You jump over to yields right now with the 10 year negative by two ticks. Trading at 111, 02. And yeah, I mean we could jump around. I got a couple more articles out there. Interesting, this one I was reading this morning from Bloomberg. And it's talking about the government shutdown threat builds in post downgrade fallout. So you got the Fitch downgrade of course. The Fitch ratings downgrade to double A plus, right? Is that how you call it? Double A plus. And what's that gonna do? Well, that's gonna push, it's gonna embolden potentially. Okay, that's the word they use in here and I like it. Congress left for an extended August recess without resolving simmering conflicts over spending. And what do they got? Federal funding runs out after September 30th. Yeah, so we got another cliff looming out there. And guess what, man, politics, they're coming. How's that gonna change things, right? We got a presidential election in what, 15 months? That's like tomorrow in the world of politics, man. It's already kicking off as we knew it. We'll see where that goes. Keep your eye folks. We got CPI on Thursday. That'll be an important one. We got crew prices back under 82 bucks, but crew had seen some strength recently. Check out Teddy's webinar coming up a week from today. And have a great Monday, folks. Stay tuned. We got our man Basil Chap. He is coming up live next. Steve Rhodes at 11 o'clock, fast market at 12. Our man Larry Pezzavento live at one. My dad, Tom O'Brien, live from free till four. Appreciate you starting your training day off with me, folks. Stay tuned. We got the S&P in positive territory. We got Dow strength, Russell weakness. Have a great Monday, folks. We'll see you tomorrow. Stay tuned for Basil.


Ladies and gentlemen, this is the one before last session. And precisely because of that, we are going to fly in the extra atmosphere with U2, Madam Minister Sarah Al Amiri, who is the Minister of State for Advanced Technologies. And she is the chairman or chairwoman of the Space Agency of the Emirates. So Madam Minister, thank you very much for being with us. And I think it's also an opportunity for the two of you to meet with Philippe Baptiste, who is chairman of the President, CEO of the Centre Nationale d'études spatiales. And I think your presence also at the end of this WPC is of special significance because space, the space dimension, was important in the Journée Francaise, the French day of the opening of the Universal Expositions of Dubai that was yesterday. Perhaps you will tell us two or three words on that. But I would like to ask you for the discussion one simple question. Nowadays, of course, everybody is interested in space. It's also a matter of technology. Space is a matter of the future, the world. Economy is going to be based on technology. And all technology today depends on space in some way or another. But the intriguing aspect of this is that, of course, the biggest countries, for them, it is a major strategic issues. But middle powers also want to be part of this adventure. And they also want not to be entirely dependent on the biggest power.


Namely, there is outer core and atmospheres of giant planets. They're inside of the atmospheres of Jupiter and Saturn. What happens there is that the interior of the planets is very hot. So the atmospheres are heated from the inside. And they're also cooled from outside by a radiative cooling because the heat is just radiated into a cosmos. And so quite suddenly, there is a convection going on there. So-called deep convection because the layer, the atmospheres are quite thick. So far, our understanding of the deep convection mainly comes from the numerical models. There were some earlier lab experiments and some theories which kind of predicted the behavior of those flows. But nowadays, we mostly look at kind of general circulation type numerical simulations of the atmospheres. And there is core as well. So there's nothing wrong, of course, with numerical simulations except that they're quite dissipative, in fact. So if you take the eckman-Nam, a typical eckman-Nam of the simulations, it's about 10 to the minus 6. So eckman-Nam is the ratio of viscosity to Kerourez force, 10 to the minus 6. And this is just kind of the newest, the latest, numerical simulations, 10 to the minus 6. Before that, the eckman-Nam would typically 10 to the minus 4, 10 to the minus 5. So the dissipation is quite important in those models. So the researchers, they need to overforce their models. So the heating fluxes, there must be many orders of magnitude higher than there really are in the planus. To be able to get the velocities, which are comparable to the real velocities in the atmosphere. By the way, the eckman-Nam in, for example, in the Earth core is on the order of 10 to the minus 15. So there is still nine orders of magnitude to go. So we can talk about the validity of the numerical, the results of numerical simulations only hoping that in the asymptotic case, they give us the right picture. So there are two types of general numerical modeling of planet Earth atmospheres. There are shallow water type versus deep convection kind of dynamics. Shower water models usually are used to generate zonal jets, just like those bands on Jupiter or Saturn from mainly two-dimensional like turbulence. But they have difficulty to give the equatorial jet, for example, on Jupiter or Saturn. Those equatorial jets, they are prograde. So they rotate in the same sense as the planet. And so they rotate faster than the planet. Or you can call them cyclonic as well. Deep convection models, they don't have any problems with reproducing the equatorial jets. So the question we ask ourselves is, can we offer some kind of general theory which would just add to understanding of what's going on. There's some basic physics. And perhaps in the future, contribute to some kind of parameterization of this small-scale convection in the atmosphere so that it can be used in those general circulation models. Because we can't really hope that we can resolve all those fine scales in numerical models anytime soon. So this talk is mainly about theory. But before I'll talk a little bit about numerical, sorry, laboratory experiment. And we needed to do this experiment just to convince ourselves that one of the key elements of the theory actually works. When I start talking about lab experiments in oceanographic meetings, I sometimes feel like a dinosaur. Because not many people do this rotating tank experiments anymore. But anyway, I'll try to convince so that in this case it can be useful and even be superior to numerical models. So we have a rotating tank here. It's 1.1 meter in diameter. And it rotates very fast. The period of rotation is 2.6 seconds. It's just a big thing. And it rotates very, very fast. So the surface of fluid, of course, is parabolic. And we insulate, thermally insulate the tank, except at the top. So the top is just open to the atmosphere. We can heat water by heater at the bottom. And also when we heat water, it starts cooling from the top, evaporates from the top so it cools from the top. So mainly what we did is we heated water before the experiment. And then during the experiment, we just observed the effect of cooling from the surface of this warm water. Warm water has an additional advantage that its viscosity is very low. It's about three times lower than the viscosity of water at room temperature. So we can actually reduce segment number to very low values. It's about 10 to the minus 6. So in this sense, we are doing better than numerical simulations. And also, well, if you would like to do this kind of simulation, it might be challenging because it was three-dimensional flow. And the scales of the flow are very small. So when cooling happens, the very small, very small dense parcels at the surface appear and sink.  So if you look at this picture, you can see those dots. And they're about 5 millimeter in radius or diameter. So we also have a thermal camera here. And you can see the temperature field in the tank as well. So we use a new method of measurements in our lab experiment. Because in lab experiments, it's usually a problem to measure velocity field with reasonable resolution. So we use the altimetry just like the altimeter used by oceanographers, the satellite altimetry. So we measure the elevation of the surface. So the elevation eta. So any flow within the tank creates pressure field. Pressure field creates the surface elevation. So the certain variation of the elevation is called eta. Here, and this is what we measure. We use optical method. So it's decoded by color, the gradient of the elevation. So the parabolic surface of the tank is basically used like a telescope mirror to amplify those tiny perturbations. The perturbations are very small. They're 100 microns. So you can't really see them by naked eye. But using this method, you can. K. So we measure basically pressure field, the gradient of pressure, because the surface elevation converts into pressure. So what can we do about velocity? So this is rotating fluid. So if we just start with the equation of shallow water to the equation. In the first approximation, of course, we can obtain the geostrophic velocity. So we ignore the time tendency and the non-linear term, because the Rosman number is quite small. And we get the geostrophic velocity. Geostrophic velocity is just a combination of the gradient of the surface elevation. So it's direct measurement of geostrophic velocity, if you like. And then we can do another approximation by plugging in this into the original equation. And so we get two additional terms here, which comes from the time tendency and the non-linear terms. So it's a geostrophic approximation. So we measure the pressure gradient and we can obtain velocity in a geostrophic approximation in our experiments. So the resolution is quite high. We resolve just half a millimeter resolution. And we get lots of data, hundreds of gigabytes, typically. So in this sense, it also can be compared to numerical simulations. So this is our analog computer, if you like. We also used, to our surprise, this altimeter method didn't work for some specific aspect of our experiment. So we needed to use something else to get real velocity, not quite geostrophic, real velocity. And we use PIV. So you're probably familiar with PIV, it's particle image velocity. You just put lots of tiny particles in the water and you follow the motion. How passive?  So I would say they are quite good in this sense. There are lots of literature on PIV which kind of confirms that. And in our case, we have reasonably sure about that. So the poly-italian particles, they float just above the surface of water, actually. So this is a typical movie from the experiment. It's not much to see, but if you look closer, you can see those tiny particles at the surface. And you can see variation of color. And you can see what's going on, some small vortices. And if you look even closer, you can probably see the zonal jets which develop there too. And there is another movie. This is from thermal camera. This one doesn't play. But anyway, it's the same thing. So you can see all those variations of temperature.  So this is what we see. This is the total field relative to vorticity here. It's normalized by planetary vortices. So this is basically a Rosbin number here. You can see it is below 1. And the average Rosbin number is about 0.1, 0.2 perhaps. So if you zoom in into those vortices, so this is what you see, those typical vortices on the order of 1 centimeter and below size, we can resolve the velocity, et cetera. And this is the azimuthal velocity. You can see kind of like zonal jets. They are not well formed, like those jets on Jupiter and Saturn, because we are not quite there in the regime for those jets to form. But they are there. So for comparison, you can see the polar regions of Jupiter. This is a very recent picture, which was obtained by these flybys of the spacecraft above Jupiter and Saturn. What you can see here is again those tiny vortices. They are everywhere there. On Jupiter, they are a bit bigger. On Saturn, they are very tiny. Also on Saturn, there are other interesting features like very intense polar vortex in this structure, too. But I am not going to talk about them here. So this is what happened in the laboratory tank. We have those perturbations of cold elements which sink. And when they sink, so they sink along effective gravity. Effective gravity in the lab is regular gravity, which was centrifugal force omega squared r here. So it's not aligned with the rotation axis. So rotation axis is vertical. Effective gravity is not. It varies with radius. So the parcels sink not vertically, but at an angle. But the Taylor columns, they generate. They generate Taylor columns by emitting inertial waves. They are, of course, vertical, or rather aligned with the rotation axis. So this is the key feature of the experiment and the theory as well, so that the gravity is not aligned with rotation. On the planet, it's just regular gravity. Well, effective gravity centrifugal forces have included that, too. And of course, it's not aligned with rotation as well. But in a different sense. In the lab, it goes like this. And on the planet, it goes in the opposite sense. But it doesn't matter. OK, so when the parcels move in the water, it turns out they move with constant speed, with constant velocity. Because they emit inertial waves, so they experience drug. There were some previous papers, starting from paper by Steuerson in the 50s, where people studied the motion of objects in the rotating fluid, objects moving along the rotation and across it. And they showed that there is a drag force experienced by those objects, either solid objects or fluid or liquid objects, doesn't matter. And eventually, those objects, they move at constant speed, which means that the buoyancy force experienced by those parcels, by those blobs, they are transmitted to the bulk fluid by the mechanism of inertial waves. So this is a completely inviscid mechanism. So it doesn't depend on the equipment number. And it results in this forcing of the bulk fluid. So and then we can write down two equations for the general circulation in the tank, which are just regular equations of motion with the cadaverous force here, a little bit of dissipation kind of relief friction here, and this extra forcing in the right-hand side. So we neglect it. Not in reality here, it's human that the Rosman arm is small. And this gives the circulation like this. So this is azimuthal velocity u. It's proportional to this extra forcing. And there are also some radial velocity, but it's quite small because this relaxation time is quite large. Another effect which those parcels moving in the water generate, they distribute some temperature field. They move it around. So there might be some variations of temperature in the tank. And then we can calculate thermal wind, geostrophic thermal. So this is the geostrophic part of the flow. This is a geostrophic part of the flow here, which is given by the external force. So the total flow is the sum of those two. I think I need to accelerate. So you can compare this with the Ekman theory in the ocean. So when wind blows above the surface of the ocean, it transmits momentum to the water. And then this flow spirals down, so famous Ekman spiral. But the end result is very simple. The transport, the integrated velocity over the depths, is actually to the right of the wind and proportional to the wind stress. So the same exactly the same as here. But the force is coming not from the surface of water, but from within due to those inertial waves. And this is what we measure in the experiment. So we can measure the thermal wind using our thermal camera. So thermal wind is not very large here. It just goes around zero there. But we also measure this circulation here. This is a velocity function of radius of the tank. So it's approximately linear. And we can compare it with theory. What theory is predicting? So theory, we need to know this parameter, which is the product of the buoyancy force by F. F is the concentration of those buoyant parcels in the water. So we don't really know it. But we can estimate it by measuring the delta T, temperature, RMS temperature variation in the tank. Again, using the thermal camera. And we put some coefficient C here, order of unity coefficient here. And it turns out to be from all those measurements, it turns out to be 0.75 in the experiment. So now we're reasonably sure that this force actually works. And it drives water in the laboratory tank. So this force is due to misalignment of gravity and rotation. So let's now look at the spherical planar. So let's say we release particles from the inner surface, or so buoyant particles from the inner surface, or heavy particles from the outer surface here. And let's see how they move. So we have the results of all those theories there, which gives us the nice analytical expressions for drug force experienced by those parcels. So there is drug. It's different for parcels moving along the rotation axis and across. There is corollaries force. And there is also lift force, which acts sideways on the parcels. So if we put them all together and invert this expression here, we can calculate the velocities of the parcels. So we get this nice expression. So we get the radial velocity. So this is cylindrical coordinate system, by the way. So the radius here is z-axis like that. And while theta is collated here, we will be using it too. Anyway, so we get these expressions for the velocity of the particles. So we will be using vr and vz, mainly, just in the medidonal plane of the plane. We don't care about how parcels move in the azimuthal direction. So the interesting feature of those velocities of vr is that they are different by a factor of 2, exactly. So this theory, of course, is without magnetic field. But there is also a similar theory by Moffat and Loper. They include magnetic field into consideration and did similar analysis towards Sturzen did. And they obtained the coefficient for the case of magnetic field, for the teroidal magnetic field, actually. And it looks quite similar to here. And also, the same thing, it's actually a bit mysterious to me. But maybe there is some simple explanation to this. So the velocities are still different by a factor of 2. So it works for cases without magnetic field and with magnetic field. So now we can easily write down the equation of motion for particles, dr dt, dz dt equal to velocity, which is given analytically. Velocity depends on the gravity. Gravity varies like g sine theta. Theta is the co-latitude, the components of gravity. The r is that component of gravity. So you can easily see that the trajectories of particles are parabolas. So they start from inner sphere or outer sphere and they go along parabolas like this. And we can obtain this coefficient from initial conditions depending on where you release the particle on the inner sphere or outer sphere. So now we know how particles move within the atmosphere. So when particle moves, they carry the buoyancy with them and they also impart this force on the bulk fluid by emitting inertial waves. So now we can see what effects of those particles, particle motion is there. So we can easily write the flux of the entropy here. So we use entropy here for the atmosphere because entropy of the adiabatically rised particle is conserved. Or we can, in the Boussines clue, it can be just delta rho, it doesn't matter. So we can write it like this and express in the form of buoyancy. So this G-A-I-G-A expresses how particles focus into. So if we release particles uniformly from the inner sphere, for example, they rise like this. So they focus towards the pole of the planet. So this is the focusing factor. So they carry, for example, these are warmer particles. So they carry warmer temperatures towards the pole. So all these values can be calculated. Well, these are transcendental equations. So we can't really give them analytical formulas, but they're very easily calculated using some simple MATLAB or Mathematica software. So we can plug it in and calculate the exact trajectories of the particles and kind of bulk average temperature of the fluid. So the only thing, the parameters that we need to know is the buoyancy with which the particles start. So how do we guess that? We can look at, for example, thermal images. This is a thermal image of Saturn, the polar image. So again, we can, again, use this idea about the RMS temperature variation here. So the typical variation of temperature is about one degree divided by T is about 100 degrees Kelvin at the top of the atmosphere. So you can get some rough idea about the buoyancy of those of those parcels. They're kind of distributed buoyancies. So this is the factor F here, again, which is the concentration of those parcels. We can't really say anything about what's happening in the deep interior, though. So we just guess that perhaps it's proportional to this. So we introduce these coefficients, A1 and AI and A0, just to three minutes, OK. And OK, so these are the same formulas. So again, we have thermal wind and this circulation due to forcing. And this is what we get. This is for Jupiter. This is the total circulation of the atmosphere. And this is the top of the atmosphere where we can compare our profile predicted by theory with the measured profile. So the red line is our theory. And measured profile is the black line. So it's quite good. Well, we use a little bit of adjustment because we don't really know about the inner sphere. But still, it works quite well. And the same for Saturn. So it gives nicely the equatorial jet. And we also can do similar thing with the Earth's outer core. Earth's outer core is very important for dynamo problems, of course, for the generation of magnetic field of the Earth. And it works in the same manner, although the boundary conditions are a bit different. So I'll skip that. And so this is the profile it predicts. It has two anti-cyclonic jets at the tangent cylinder here. And it looks like this kind of behavior is actually observed in the data on the variation of the magnetic field of the Earth. This kind of strong jet going in the anti-cyclotic direction in the Earth's outer core. So I'll just put these conclusions here. Thank you.


That's a how what's your nation? This is going to be my first talk in Chinese no Unfortunately, definitely not not there, but Yeah, that's pretty much where my Chinese ends. I can order my bubble tea, but So I'll survive also. Let's put it like that Yes, thanks so much for joining. I'm Thor. I help developers built so I Worked for a little company called super base. I know some folks Already said they they've heard of it. That's great I have So the the QR code on the left is just the link to that page where I just have the resources Basically, I'm using for this talk and then on the rice you can ask questions Kind of throughout the talk, you know as you kind of think of them you can put them put them in a slido Yes this is gonna be interesting because I Don't have a microphone stand and I'm gonna Should I sit down? Should I stand? Not entirely sure We'll figure it out as we go along Now super base. Yes, open source. That's kind of the theme of the night So we called it an open source meetup Where the open source fire base alternative if you want to put it like that? It helps with the SEO You know a lot of people search for fire base. So I think that's that's sort of the main thing there Probably the same with notion With SEO as well But yes, so basically You know it comes from we love the developer experience of fire base, right? If you've worked with fire base getting started is super easy Eventually it might get a bit hairy But you know, it's it's kind of the idea of taking the developer experience from fire base and applying it to a relational database and postgres open source relational database Been around for I think over 30 years, you know three decades Fun fact actually like It precedes github, right? So the actual management of the postgres project is kind of via email there's actually an interesting blog post about sort of how You know proposals get merged into postgres. It's it's quite interesting because it's not on github But yes, so basically the database is the foundation of everything So that's postgres and we're pretty much all in on postgres because postgres is so powerful It basically is like using a cheat code So we're using things like authentication. So that's also We're using a server an open source service called go true was originally built by Netlify Back in the day for jam stack sites. We've kind of forked off of that And the authentication Actually sits on top of the database. So all the user data lives directly within an auth schema within your database And then we're using the JWT with the role-level security policies, which is a native concept within postgres To actually restrict access to data Which means that we can put kind of this auto-generated API layer on top using an open source project called postgres Yeah, it's confusing with the names, but rest get the idea postgres And that actually allows us to automatically generate rest APIs. We also have GraphQL APIs built in Into postgres with a postgres extension And so that's the other exciting part of a kind of the postgres ecosystem there All right, I probably Probably need to be faster. Okay, let me see if I sit down and kind of move the Thing every once in a while Let's see Did that work? Okay, sweet Yes, Twitter if you're not following us feel free to do that, but actually while we're here So we just launched a super-based vector on On product hunt so let's everyone go to product hunt and maybe upvote Let's go while we're here. Maybe we can does that work Oh Yes, so if you go there and you do that while I tell you about Postgres vector so Who's familiar with kind of building a i applications embeddings vectors kind of all that all that fun stuff Anyone no one Yes back there lovely So Basically, you know the idea is that you take kind of something like human language or vision and turn that context into a Vector and Did everyone manage to scan the QR code then I can go and show you some visuals that help with the explanation Okay, very good So if we go if we go here Yeah, let's maybe look at this So there's a blog post that explains kind of you know working with embeddings and Vectors in in Postgres and so basically it's kind of this idea here where You're kind of translating, you know something like human language and the context of human language into Basically just numbers kind of a vector and if you look at this here, so we have kind of this is just two-dimensional, right? And say we have something like the cat chases the mouse and the kitten hunts This is a rabbit rodents there we are Contextually that is fairly close to each other, right? You have a cat a kitten A mouse a rodent and so if you translate this into kind of the vector space This context will be fairly close to each other, right now if you have something like I like ham sandwiches That is fairly far away from kind of the the previous two statements And so if you if you put this into this vector space What you can then do is you can perform vector similarity search Or kind of proximity search where you're basically looking, okay? For two vectors to be close to each other then there is some contextual relationship and so basically now if you're putting, you know language or Images if you can translate that into kind of this vector space And you can store these vectors and then perform You know kind of the search on it Then you have you can build fairly fairly powerful applications and so PG vector is a postgres extension that allows you to store vectors of different dimensions Right within postgres and actually create indexes on top of that data on top of those vectors And then perform kind of the search operations Within within postgres And so if you're working with something like open AI Where you can turn Kind of that context like human language or images into vectors You can then store that into the database using Postgres or you know super base vector, which is basically a managed PG vector postgres Kind of bundle now obviously To kind of represent something like visual context or human language You need a lot more dimensions than just these two and so you actually have like vectors with you know Depending on the model, you know more than 500 dimensions and obviously that is that's kind of very big numbers But we'll just use this for kind of visualization visualizing this If you look at kind of the docs so we have Some documentation Super base vector sort of the soft launch that we've done now We have the the vector store that kind of allows us to store these embeddings using postgres and PG vector There's a python client library called vex That kind of takes care of the indexing Creating of the collections Performing, you know queries. So if you've worked with something like pinecone, which is you know kind of a Dedicated vector database sort of specialized on you know vector data It is kind of fairly similar to that experience where you're creating collections and you're querying querying those You're kind of absurd things into your collections. Well, we'll look at an example in a bit But kind of the exciting part with With PG vector is that you can actually you know because your data is sort of within Where is it? Is it this? No, that's not anyway, you can go through the docs yourselves, but The idea is that you know you have both your vector data as well as your you know normal relational data Or your files. So we also have Unstructured data like file storage kind of as part of the super-based stack. So everything sort of is co-existing Within the same database the same the same ecosystem And all the parts are open source. You can use them separately or You know, you can kind of bring all the different bits and pieces together and sort of that's kind of the philosophy within super-based open source Modular so like each piece you can you can take on its own self-host or you know, obviously then our business model is we hosted for you and We manage Kind of the hosted database service for you and that's kind of where we make make the money Okay, great, so let's actually dive into some examples and you know, I'm I personally I'm a JavaScript fan But I hear kind of in the in the machine learning space people tend to use Python What what I find exciting about kind of this Python example is that I managed to build it without you know Being very proficient in in Python. So I think That's sort of the great thing here So it's it's this link the the image search with open AI clip. So it's an open source Model and the the interesting thing here is that it turns Images as well as text into the same vector space. So this means that you can perform image similarity search, right by comparing The two vectors of you know images for example, but also you can do Text to image or image to text so you can kind of translate between these different Environments and so this is kind of what what this looks like so We're basically putting The image data as well as the the text data into the same vector space and you can see here We have an image of two dogs in the snow Which then the text of two dogs in the snow they those two vectors are very close to each other Because you know the context is kind of the same Same there and so what we can do is We have this example. It is on github. It is linked in the example as well And what we can do is we can run a super base Locally, which is pretty nice. So because it's all kind of open source. It's already running. So Sorry, I'm typing with one hand here. I Should say status. Yes, so We're basically running the entire super base stack locally here So we get kind of our API URL. We get a GraphQL URL We have our database URL. So if we're kind of connecting directly to the database We can we can use the postgres URL here and then also we have a super base studio And you can run that locally as well, which is quite exciting. So if you're you know somewhere where you don't have internet Well, you do kind of when you start need to pull down some Docker images So I really do that big before takeoff But you know if you manage to start the service locally and have a running you can actually develop, you know kind of offline So to speak So we have our default project here We can kind of look at the table editor. We have sort of our public schema We have a bunch of other schemas. So as I mentioned, we have kind of this auth Service that sits on top of the database. And so we have kind of all the The tables here and in a separate schema there And so if we now look at this example, let me just open it up in Here and I'm just using poetry to run this which is sort of Kind of like npm but for for Python. Let's put it like that and so here I'm just using vex so vex is our Python client for Vector handling handling vectors and embeddings And so I have here my database connection. So I'm basically just instantiating A vex client with my database connection. So this is here locally on my machine And I'm I'm actually using something here. I think this is It's open source as well. It's called orb orb stack. I believe orb Stack only found this recently And it's basically oh Okay, there we go. It's cinema mode now So it's it's Basically docker running sort of docker optimized for Apple silicon. So if you're if you have a Macbook with Like an an apple chip then actually this will run docker a lot more efficiently Then say docker desktop or something like that. And I think it's open source. So That's pretty exciting. And so you can see here. I By super base start. I have all these different services running here And then what I can do is I have my seed method. So I create my client I create a new collection that I call image vectors And we're using the open AI clip model here, which translates our images and our text into This vector with 512 dimensions, right? and then I Have my model. So we're using the open AI clip model and then I just have a couple of images just here from Maybe just remember those images. They'll be in the exam later So this is the couple images we have they're just from unsplash And so we're just encoding them here as a vector and then what we're doing is we're just using our Images collection and we're just absurding our vectors here and we can specify some metadata that we can also later use for kind of filtering in the query later on right and then we're just saying okay, we inserted the images and We are creating an index. So after we're absurding kind of the images We're basically creating an index to make the The vector proximity search kind of more efficient. So let's say poetry run seed and so We're doing all this what I just mentioned. We're generating our vectors We're absurding them into database. We're creating our index. And so now if we go back to our locally running so this is running on localhost kind of our Dashboard we now have a new schema called vex Can you see that in the back? Do I need to zoom in a bit more? And then here this is our collection that we created. So this is our image vectors. You can see we have Our IDs. These are our vectors So you can see the column type here is vector. So that's the column type that PG vector allows us to store vectors, I think of up to 2000 dimensions something like that and then we have some metadata here, which is just JSON B So we can use that to to filter lately later in our query So that that's it, right? That was pretty pretty easy. So now we can do we can perform some searches here Again, we're creating our client Now in this case, we already have our collection. So we just get kind of a reference to our image vectors collection Again, we're using our open AI clip model And so now in this case, we're getting a query string from our arguments We're encoding the query string into the same Vector space as our images and now we're just doing a query with our query vector, which is our text string Which is limiting it here to the most relevant result There's kind of other input parameters you can you can put in there And then we can also filter on kind of the the metadata where you can just say, okay We won't only want to look at kind of vectors that represent sort of check pack images, for example, and then we're just getting our result And we'll just open up on the result So this is where it comes in handy if you remembered what was in the pictures earlier because we're gonna run some searches Okay, who wants to start? I think I need to run search. What should we search for? Sorry Open field, okay All right, and so ideally now and the reason why I called these one two three four There we go. I Think that's where you applaud now, I mean Right, I think it is quite impressive because like if you think about okay You use this like on a daily basis is you use something like Google images, right? And you you can just search for like me on a bike Not me like yourself on a bike and it actually shows you all the pictures of you on a bike And but the tech behind it, right? Is it like if you think okay, how do I actually implement this? Here you go. It's just a couple lines of of Python code Okay, I mean obviously I could have been cheating here somehow So maybe should we try another one? Just throw them out Laptop well, I mean That's interesting because we didn't have anything that's close to a laptop So let's see what this model thinks is the closest to a to a laptop To be fair on your lap here. There could be could be a laptop right We don't know maybe actually maybe the model knows a lot more than Then then we do now that would be pretty mind-blowing if that was actually the case maybe fruit Let's see. We did have some grapes in there Yes, so we get we get our grapes Anyone anything else We did have a We did have a bike in front of a red brick wall Obviously if I put that in that might be a bit too easy, but Maybe if we just say vehicle I Think the bike was kind of the closest contextually to what we had is yes very good. It's like I practiced this What else do we have okay, we have So these are sort of both I guess Flowers no, I mean, oh, let's see what what happens if we put flower Anyone has an idea how this model was trained. I don't know do we get the yes, okay? I think I think that's that's fair enough Happy okay That's true like any any predictions what What's happy That is a happy remote worker working in a field Fantastic internet connection They are happy alright Yes, so the point I'm trying to make here is obviously right like the math behind this is fairly complex and you know like even if you're using something like pinecone actually This is kind of a copy of the pinecone example for image similarity search But it is quite complex even still if you're if you're kind of implementing this with pinecone But like for me, I don't have much knowledge of kind of the AI worlds I don't have much knowledge of Python and yet Somehow a fun little demo came came out right and so that's kind of kind of what we're trying to do in general We're trying to make postgres more accessible to kind of a broader audience of developers and here with sort of Super-based vector as well We're trying to make it easier to you know allow you to to build kind of AI enabled Applications Now the the I think the subject of my talk was something like building your own chat GPT Obviously, let's let's quickly end on that one Build your own chat GPT Here with Dino, so for the you know, let's go back go back to JavaScript Actually who has here worked with Dino before little show of hands any Dino fans Yeah, the super baseball. Yeah, that's true Yeah, I love Dino, they probably have the best the best swag in the in the business somehow They found someone who can draw the cutest dinosaurs and Yeah, it's just it just works I think And Yes, there we go. Okay, so we're starting up again Super-based start. We're starting our database here I probably should have maybe opened the code before before I ran this This is yeah, well, it wasn't wasn't too bad considering how many docker containers kind of are behind this So now one difference here is we're using open AI To generate embeddings Rather than kind of running the model a sort of ourselves So we're just going to the open AI API We have kind of a pre-processing step. So maybe if we quickly look at the repository You can look at kind of the the different sort of things that are happening here So we have a github action and basically the github action any time We're kind of making changes to our docs So we have like a bunch of markdown files with kind of context and we put You know that context we generate embeddings for it because what we want to do is You know if you if you use chat GPT and you asked something, you know to chat GPT It doesn't have kind of the context that you're asking this question in right and so if we know you're you know developing with super base We can actually get the relevant context from the super base documentation Give that to open AI and make sure that the answer is actually only coming from this context Meaning that you can get a lot more precise kind of answers where you know Like if I go to chat GPT and ask like what is PG vector Or no, I think the example is like if I asked what are embeddings then Jet GPT will reply with like oh in the concept of machine learning You know because it has to set all this context But if I actually provide kind of the context of this query I say okay, we're developing with super base and I asked the question now What is PG vector then it knows the context and we can provide kind of the documentation that we have And then have chat GPT actually provide kind of a concise sort of answer Okay, and so we are pre-processing our documentation. We're generating embeddings and we're storing them in the database And so we can quickly look at Where is our dashboard Here's our dashboard now This dashboard is now running our other project and so here I'm not using the Python client anymore. So this is all all JavaScript And so I just have here in my code base I have one piece of documentation Which is this markdown file called open our embeddings and storing embeddings and Postgres And so I'm just turning this into different page sections here So basically all the the different sections and then I'm just generating a vector so that I can later perform Kind of the search to find the relevant context And then lastly what I'm doing is kind of edge At runtime basically when someone is asking a query I then Take kind of the the query string here What we need to do is we need to sanitize it because open AI actually has some some terms of service where you can't like You know ask the model some crazy things that's that's against the the rules because otherwise it will get very rude Because it gets trained on all that so you need to sanitize your query. You have your moderation response So you're kind of asking basically open AI. Hey, is this an okay thing to ask? Then open AI says yes. Here's the sanitized way You can ask it and then we create an embedding here. We're using I believe this is GPT three Text embedding model and We're now then performing our We're using kind of a remote procedure call to perform our basically proximity search Using kind of the JavaScript client here So that's something that will have to make a little bit easier with vex. You saw it was it was a bit easier to do that But then what we can do is yeah, we tokenize this And then this is kind of the important piece. So we're generating this prompt where we're saying okay You are a very enthusiastic super base representatives who loves to help people. That's important So that the answer will actually be helpful Giving given the following sections from the super base documentation Answer the question using only that information and so that's kind of the crucial part where we're setting the context to be only The context of our documentation We're providing the context of Basically all the pieces of the documentation that are relevant to the user's question And then we're putting the question in here and then we're saying okay answer as markdown including related code snippets Sending that off to the open AI completions endpoint and then we're just streaming back The response, okay, and I think I'm running way over time here So Dino tasks start and you know, basically the the front end piece to this is is is fairly small But what we can do here now is what our embeddings and So if I fire this off now, we're looking at relevant information in our database We're sending that off to open AI and then we're streaming back kind of the response Now if I put in what our embeddings It just into chat GPT then it will say like, you know in the context of AI and machine learning yada yada yada It will be a lot kind of longer longer response Or I can say can I store? embeddings with Superbase Let's see if the typo typo is fine Yes Pg vector lovely and there we are that's how far I'll go and Thanks so much for joining us tonight and hopefully you get a chance to play around with pg vector And if you do, let me know what you're building. Thanks so much. Cheers