The narcissist will not take no for an answer, they will not accept your refusal, they will be persistent in demanding something, they won't take no for an answer. When you first come into contact with a narcissist, they can be very persistent and demanding. It's clear that they want something from you, they want your attention, they want you to admire them, they want you to validate their false self and the illusion that they are trying to portray. That is their main objective, it's to get you to notice them, it's to arouse your interest or curiosity. But if you refuse their proposal, that doesn't mean that they're just going to go away, they will be very persistent and this is due to their strong scent of entitlement. They believe that they have a right to you, they're very arrogant and audacious and they just see you as an object that exists to serve them. They don't see you as a separate human being with your own interests. They think that if they want something, you should want it too. They will not take no for an answer, even if you make it clear to them that you are unwilling to participate. They will just see it as though you're being stubborn or difficult. They will not respect your boundaries because they have no respect for themselves. They expect you to be whatever they want you to be and they expect you to see them however they want to be seen. They don't want you to have your own independent thoughts and ideas because that is a threat to their reality. They want to pull you into their world while expecting you to accommodate them and if you refuse to do that, in their minds there is something wrong with you. They don't self-reflect, they don't look at themselves to see what they've become. In their minds they are perfect, they are everything that you should want and if you don't want them, there must be something wrong with you. When you are in a relationship with a narcissist, they will not take no for an answer. They might demand something from you and you may choose to say no. You may choose to reject their proposal or idea and this will deeply offend them. They are shame-based people who are doing everything they can to avoid reflecting on their shame. So they don't see it as though you're rejecting their proposal or idea. They see it as though you're rejecting them as though they're worthless and insignificant. They see it as an attack on their character because their character is built on a house of cards. It isn't real, it has no foundation or structure, which is why they can often seem so arrogant. They have an exaggerated sense of their own abilities and importance. Because it isn't real, they're just pretending to be something they're not and when you say no to them, it's like you've denied their false existence. It's like you've denied their false reality. It triggers a narcissistic injury, which is then followed by narcissistic rage. It triggers them to self-reflect for a short period of time, which will only make them angry or upset because when you put a mirror in front of the narcissist, they cannot stand to see their true reflection. They don't want to see themselves as they actually are. They want to be seen as perfect. They expect you to see them that way. But if you disagree with anything they've said or done, in their minds that means you think they're completely flawed because deep down that's how they see themselves, which is why they abandoned who they really are and created a false self, but any time that you disagree with them or say no, it crushes their false self because the false self isn't real. It requires external validation and if you're not constantly supporting their delusion, it falls apart. They revert back to their true self and they begin to feel completely flawed. It feels like an attack and that is why they get so angry and upset. That is why they react so emotionally while a normal person wouldn't be so bothered if you said no to them. Maybe they would understand, but the narcissist cannot understand because they lack empathy. They cannot understand or share your feelings or experience. They cannot put themselves in your shoes. So any time that you try to set a boundary with them, they just see it as though you're trying to hurt or offend them. They see it as though you're just trying to make their lives difficult and inconvenient. They lack empathy. So they cannot recognize that you have your own interests and needs.  And even when they do, they don't really care. They have a strong sense of entitlement. They believe that they are inherently deserving of privileges and special treatment. So they expect you to make sacrifices for them. They expect you to give up something valued for their benefit or gain. The narcissist will not take no for an answer. They will not accept your refusal of their proposal or idea. They can be very arrogant or audacious. They believe that they have a right to do and say whatever they want. They have no respect for you. They have no regard for your feelings, wishes or rights. It's like dealing with an overgrown, unruly child. They are disorderly and disruptive. They lack organization. It is very rare that you will experience a moment of peace while you are around them. They are not responsive to discipline or control. They lack obedience and compliance. They have to be in the authoritative position. They have to call the shots. They expect you to obey them. They expect you to follow their rules and regulations. They expect you to see and do things their way. Whenever you demonstrate any form of individuality or free-thinking, that is an offense to the narcissist. That is a breach of their rules and regulations. And it will only cause annoyance and resentment, which will be brought about by a perceived insult or disregard for themselves. Because they are the biggest victims in the world, it's all about them and how life is so unfair to them. And yet they expect you to fix everything. They expect you to comply with their demands. They expect more from you than they expect from themselves. Because underneath the arrogance and entitlement, they know that they are incapable of doing anything on their own. Which is why they demand so much from you. Which is why they cannot take no for an answer. Because they know that they need you. They are constantly in survival mode. And in their minds, you are the only hope of survival. So when you reject a proposal or idea, it makes them feel like you are abandoning them. When you are supposed to be their life support, you are supposed to fix everything for them. This dynamic always goes back to their childhood. They were enabled by their parents to act in this way. They were never disciplined when they wanted something. Their parents gave it to them. Their parents rewarded their bad behavior. Which has taught the narcissist that all they have to do is cause commotion to get what they want. Which is why they will not take no for an answer. If you say no to a narcissist, it will only make them angry or upset. They will rage at you. Or they will become passive aggressive and give you the silent treatment. Or they will try to force you into submission. They will try to intimidate you. They will try to pressure you into doing something that you are unwilling to do. They will try to obtain something from you by using force or threats. Without any regard for you because they are self-absorbed and they lack empathy. They only care about themselves. Thank you for watching. I'll talk to you soon.


Hey everybody, let's look at some displacement time and velocity versus time graphs for uniform and accelerated motion. First we've got an object at rest. This is my cat. She's not moving, she's at rest. Here's what a graph is going to look like. It's a nice horizontal line. An object at rest makes a horizontal line on a displacement versus time graph. What about an object moving forwards? So an object moving forwards on a displacement versus time graph is going to make a nice diagonal line. The slope of that line is how fast the object's going. What about an object going backwards? Well, if the object's going backwards, then you're going to see a diagonal line again, but now it's going to be going downwards. The slope is a negative. What about an object that's accelerating? Well, the first thing you're going to notice is on that displacement versus time graph, it is a curve, which is going to be different from any other sort of uniform motion sort of situation. The curved line in the D versus T graph means acceleration. So you can remember straight lines for uniform, curved for accelerated. What about velocity time graphs though? So here's the velocity time graph for each of those uniform motion situations. You can all see they are horizontal lines. And the velocity versus time graph for accelerated motion, this one is going to be a diagonal line. This object was going down, so it was diagonally downwards, a slope and downwards here. Now, two other things you got to know is you can find the displacement under a velocity time graph. All you got to do is find the area. That's base time sight if it makes a rectangle, one half base time sight if it makes a triangle. For more info on this topic, check out LDindustries.ca.


Remember a hallmark card when you carry enough to send the very best Tonight from Hollywood the makers of hallmark greeting cards bring you very Fitzgerald in Edward McStorley's our own kind on the hallmark playhouse Each week hallmark will bring you Hollywood's greatest stars and outstanding stories chosen by one of the world's best-known authors They distinguish novelist mr. James Hilton Good evening ladies and gentlemen, this is James Hilton Tonight on our hallmark playhouse. We present a story ideally suited to the day St. Patrick's Day It is called our own kind by Edward McStorley and mr. McStorley is evidently the man with experience to write it for he has been at various times a farmer a sailor and a fisherman and Not only do we have this warm hearted and appropriate story But we're especially lucky to have in the starring part that superb Irish actor Barry Fitzgerald A man whose genius I have admired ever since the days many years ago When I used to line up outside the Abbey theater in Dublin to see his wonderful performances in that famous almost fabulous theater So here we are on St. Patrick's night Already to enjoy ourselves with Barry Fitzgerald in Edward McStorley's our own kind And now Frank Gauss have you a word or two about hallmark? There are hallmark cards for every memorable occasion on your calendar for birthdays anniversaries holidays Yes for a friendly greeting a word of good cheer an expression of sympathy There is a hallmark card that says just what you want to say the way you want to say it and that identifying Hallmark on the back that says you cared enough to send the very best Now hallmark playhouse presenting Edward McStorley's our own kind and starring Barry Fitzgerald St. Patrick's day a Great day for the Irish But especially so for me for wasn't it on St. Patrick's day that I got me first glimpse of America That was a long time ago. I was a young one then and so was we my nerve But we found happiness here In Providence, Rhode Island. We lived and we raised two fine sons But we found sadness too when the oldest God rest them was lost There they are now it's all over now But don't hold in go into your room have a good cry. It'll do you good That's right mom and try to get some rest do Chris Chris you're all we have left Will is in good hands now Remember what father O'Connor told you You ought to get some rest yourself that there'll be time enough for resting later We've somebody else to be thinking about now Little Willie well, he can stay at Aunt Tessie's for a while. It'll be all right there until we can make some arrangements Arrangements arrangements for what I haven't taken into the boys home get good care there anyway He'd be better off in an institution. Do you really think so? Chris would you think so if he were your own without being sentimental about it? Yes, all right Then let's be sentimental about it Now there's something the lad needs somebody to run to when the world gets too big from Not only somebody to treat his bruises But somebody to pat his head when the donors a boy needs comfort and you can't crawl into the lap of an institution I'm bringing them home here where he belongs because he secrets He's our own kind It's the matter what you need you've ruined them all man you're acting like was your first day in the foundry I'm sorry to him. I've got something on me mine little Willie again. How old is the boy now? He'll be seven years old come October I'd be glad when he's all enough to come to work and then maybe you can forget about him Well, he'll not be working here. So you needn't wait He'll have reading and writing and he'll not be a sod like his grandfather. Hey, I'm worse yet. Look you oh It's the sad I am is it at least I can read and write me your name which is more than you can do Oh, I'll tell them about that in the office. Maybe they make it president of the company Lee what's going on down here? Well, we were how we were just reminiscing about old times. We'll talk on your old time Yes, yes A man can't even talk to his old friend. I don't mind for myself, Ned But I don't like him yelling at you like that If he wasn't off home and I'd go up and give him a test to be shoveled.  Goodbye Tim everybody. Good. I Chris. Are you seeing that young school teacher again? That's a friend daily. Yeah, good willies in our class at school. No might as well have the family on the right side I'd best be getting the dishes done Time for you to go to bed will you boy?  Well, no seeing that Tim is a guest Maybe he'll read you the fun. Oh, not at all. I'd like to have you read them All right, will you sit on me laugh? Yes, well now we'll start with this one. I'm all ears. Yes, that's typical of a donkey Well, no this is here fella. This is he's a tinker with the name of a rock What's a tinker? He's a man who fixes pots and pans as good as new now this one over here. Oh, he's the bad fella. He's an Englishman It goes without saying Let me see now. Oh, she bought the pots and pans from the Englishman. Yes, Rob did There were no good until all fixed them now. They're all right Yes, but the Englishman wants to take them back now because the poor woman can't pay these sorrows I wonder if the paper knows I've got that comic strip in there Sure up you Tim Well, then a rock and the Englishman getting a fruit as you can see with a rock and actually get the best of it You know, that's enough for tonight. All right grandpa. Good night uncle Tim. Good night Willie I never heard such a comic strip before in all my life And you want him to know that I can't read or write I Don't want him to follow meet him I don't want him to follow his father or Chris who learned a little and then stopped He's got to see things too. Now. He's started the school I'd like to take him on a little trip. Take a trip You want to take a trip get all dressed up and put on tight shoes for what? Well, I don't want the boy to think that the whole of America has been squeezed into one little city called Providence, Rhode Island. Now, will we Well, we can go someplace of a slender camp. Well, there's the boats to block island and other places that live from the war Block Island that doesn't sound bad at all There's two boards that makes the trips the war worked in the mount hope the war works a bit slower But it's a it's a half a dollar cheaper. Well, then we take the ball However, the captain the mount hope is named off clarity of clarity Well, no, I think it's worth the extra half dollar to ensure a man's life getting there safely We'll take the mount hope Oh to this fellow coming along the deck with their hat I haven't seen him do a little work since we left the dock the Puzzle the person they call them Over there. Yeah. Yeah, look pretty side isn't she? Oh you? Grandpa, it's the mount home. No, this is the mount hope Willy We're on the mount hope ourselves Now try to read it again old seed you make it out any better this time I can spell it out for you if you want me to go ahead lad. Spell it out or you in T H O P E That says Mount Hope No, try again. Try again practice makes perfect as you must say. Oh good heavens that I believe the boy is right It is the mount hope and what kind of a dirty trick is this hey you you with the hat there Is there anything wrong? Well, I'm none of your tricks now what ship is this we're on when we left the wharf She was the edgemont. She probably still is but where's the mount hope then what's becoming the mount hope? That's her alongside of us there. That's her all right Ned. Well, then where's captain O'Flaherty. Who's he? He's the master of this vessel. That's who he is. There's no captain O'Flaherty aboard this ship The master's name is Reginald Crippingham, and we're bound for new London Tim Tim. We've been shy hide the takeness to England What take it is in that new London's in Connecticut just the same we've been trapped now look at you I bought tickets for the mount hope this morning to go to Block Island. Here are my tickets You can read what they say. Can't you steamer edgemont to new London, Connecticut? We don't go near Block Island. You bought your tickets at the wrong window. Don't you new London me your game is up We're going to Block Island and that'll be all of that. Not on this ship And don't make any disturbance unless you want to see the inside of the new London jail Oh, it's no use Ned. He's right. Huh? You've over 30 years younger. I'd show that buckle Now they will eat new London's nice enough just as good as Block Island And there's ice cream to be had there. There's none at all on Block Island. You know ice cream that fancy that Now what makes you look so sad? You couldn't read it You couldn't read the sign on the boat. Oh, well never you have made a mistake. That's all you couldn't read it I I wasn't weird and miss fix Grandpa the moment James Hilton will return to present the second act of our own kind starring Barry Fitzgerald On this friend list of days when old friendships are renewed kept green and fresh as the fields of Kilkenny I don't think it would be a mist to remind you that the charm of this day lies and they get togetherness of it Not only the Irish but all America seems to become one happy family What is Thorough wrote back in 1843? Nothing makes the earth seem so spacious as to have friends at a distance Now no one better understands that truth than those who make hallmark cards and Because they understand your loneliness when you and your dear ones are separated They offer you a way of bridging the miles of keeping in touch with friendship cards And you'll find hallmark friendship cards always have a way of saying just what you want to say the way you want to Say it it's a talent that those who make hallmark cards have like their talent for making cards that are top drawer in design In materials in the careful way they're put together So when you choose your hallmark cards do as your friends will surely do look on the back for the hallmark that tells them You cared enough to send the very best How we present act 2 of Edward Maxwell is our own kind starring Barry Fitzgerald I couldn't read and Willie discovered it that day on the boat to New London He never spoke of it again But it didn't bring me the funnies of a Sunday either Not did we stand together before the picture of brave Robert Emmett that hung in the parlor Well, I read the words beneath it to him the words that I had memorized I Had to do something about it. So I went to see Fritz's girl Catherine Daly the school teacher. Couldn't I get you something mr. McDermott? Perhaps a cup of tea. No, no, thank you. No, no, I have a matter of grave importance on me mind About Willie. Yes I don't know what's gotten into the boy. He was my best student. I was certain he'd win the class medal, but Yes, I know it's all my fault my fault for not being able to help them Mr. Ailey will you Of course I will What would you suggest? Well, maybe it'd be to help in the boy if it could teach me to read and write You mr. McDermott. Yes me I'm asking you please girl to teach an old man the ABC The brown CW The brown cow just a minute your cousin Larry is here and what are you locking yourself up in this room for every blessed night? Suppress on the table Hello Ned Well, I never expected to see you Larry I thought the whole lease force would be resting up for the St. Patrick's Day parade tomorrow I had some business over in this neighborhood checking up on a few young boys have been getting into some devilment Indeed, what's out of devilment? Come on Willie take a seat at the table I'm not too hungry bunch of our lads here have been ganging up on kids from other parts of town Sounds bad. Yes, and it's getting worse a couple of days ago They picked on an old man called him names and threw mud at him go on Larry Well, one of the kids is the McNally boy Stevie his parents will take care of him. All right, but he won't tell who the others were Grandpa, I don't feel too good. I don't want to eat Can I go to bed sir? Yes? Well, you can go to bed. I'll be in later to say good night here. Are you awake lad? Yes, grandpa. I'm awake. What's wrong? No, I Was with Stevie McNally.  Stevie said foreigners and no right being over here and because Stevie said that you threw mud at an old man They're not our own kind You've always said we should stick to our own guide No, that isn't the way I said it willy our own kind is the family and the family sticks together But outside of that our own coin means all the people who are here in this country with us working and building as we are No matter where they come from or what they were before There are all kind now. You never told me that before. Well, I didn't think you needed it before I'm an old man myself willy But I could walk to any part of this town tomorrow and none of the others would lift a hand to harm me I won't do it again grandpa Honest, I believe you lad now. How about something out of bed for a minute? Okay, yeah, there's some beef left over your grandmother put in the icebox and a boy who ain't no supple Should be a good match for a sandwich Sit here in the parlour for the cold soul. I'll get the sandwich and Then Robert Emmett's picture can watch you while you eat Been a long time since we read the words under the picture hasn't it? look at them Rave Emmett that the judges condemned for train to set oil and free Will I read his words to you? If you want to well, it says here When my country takes a place among the nations of the earth then and not till then let my epitaph be written Fine words, aren't they? Yes, grandpa. Shall we read them together like we used to?  Now the first word is when that's WHEN Well, I believe in that and then we'll skip on to the money and take care take country See oh you went here while grand boy you can read you can game what made you think I couldn't But the day on the boat. Oh, I didn't have me specs as I told you You want to read it with me now? Yes, yes, all right Just once through though because we have to be up early in the morning and get seats on the grandstand To watch the same passage day parade now here we go When my country takes a place among the nations of the earth Niders should all have a heaven's sake will you look at Pete Karen on the white horse years after he came over here Would you he's getting so fat? I can hardly see the horse. Well, I'm only a human Horse because I'd I know Pete isn't that tall the weight of the money in his pockets kept him from growing full-size There's Terry donovan with the bagpipes. Did you ever see such a site nearly I did not with all the blown He does about other things you wouldn't think you'd have breath enough left in them for the pipes Look those unfaulif clovers the shamrocks and that isn't a midget.  He directed it. Oh, well. Well, I suppose if he directed it must be all right They're going to do it twice for the kids this afternoon and you and grandma can see it tonight. Ah, no I'm not much of a one-for-plays Run along and come home as soon as over here boy. You'll knock the walls down Yes, she's a pretty thing. All right. Now. What is the show about? Dancers doing jigs or reels. Oh, no. No, it was a play about a British landlord British landlord in church on St. Patty's day.  She didn't did she Willie? Where from them? Oh, what you want him to do stain for the whole British army Well, what happened then Willie the British shot the poor man in the back. Didn't they start him Murphy? Oh That's the disgrace of the Irish that's forever a treasurer amongst them Then the landlord offers to get Roy out. Ah, so that's his game Well, you might as well get that right out of his mind. She never do would never in God's world She didn't ditch it. No Roy escapes and Catherine get married. Mother. Mother. Get your coat. Come on. Come on Jim Come on where to see the play. It's the greatest play you ever heard of. It was a delight Catherine I'm glad you liked it. Oh, it was grand. Mr. Did just grab well frankly. I'm a little worn out. I better go I'll get you cool I hope you come again often since I've got a hunch you'd be a member of the family soon Oh, you're making the girl blush Ned. Well, let's be commentaries, huh? I Think I have some good news for you about Willie He came to me after the show this afternoon and beg for a chance to get back into the class contest for the reading And did you refuse him? No, I told him he'd have to make up some ground first. Did he say he would? He said you'd help him. Oh, I will. I will. Are you ready, Catherine? Good night everybody. Good night. Little Willie He'll be all right now Tim He'll do the Irish pro someday when he hangs out a sting shingles a doctor or a lawyer Willie McDermott M.D. I can see it in the mind Ned education costs money lots of it Willie doesn't need that kind of help Tim He needs the kind I've been able to give him He needs the dreams and the ambition There's a way open here in America things called scholarships He's for the working for them You know Tim This is the biggest country in the world because it's Ireland as well as America The kind of Ireland that Robert image dreamed about and It's every other country that has men like him who dream about liberty and a chance for a man to make good on his own That's all that Willie needs And that's what he's got and nothing will ever stop him. No Nothing will ever stop him in a moment James Hilton and Barry Fitzgerald will return But first is there anyone who doesn't love a bit of Blarney even if you aren't an O'Brien or a Quinn chances Are someone sent you a green-tinted card today and didn't it please and flatter you to know that someone was thinking about you? Why not spread this same font sincere kind of thoughtfulness among those you care for? Send hallmark friendship cards the year-round if you feel you haven't the gift for expressing what's in your mind Let the makers of hallmark cards take care of that Picture the please grins that would greet your hallmark cards that simply say hello Across the miles to those old school pals You only see it class for unions that favorite aunt you used to visit during vacations remember and gruff old uncle Ben how many years is it since he used to take you fishing he was no softy you would tell you But it's strange how his glasses sort of cloud up about some things like having your card to let him know you haven't forgotten him Yes, there's a host of dear ones who'd welcome a little affectionate greeting from you the kind you'll find on the legions of hallmark friendship cards See them where you buy your hallmark cards send them you'll be glad you did Here again is James Hilton Wasn't our play tonight a grand way to close a grand day a Delightful story of one of the most admirable of all human trays that of wanting your children to have something better than you had And we also enjoyed the company of one of the most delightful personalities in the whole acting profession a great actor as you have heard and A great Irishman. Thank you for being here. Barifitz Gerald. Thank you, Mr. Hilton And I'd like to thank Gilbot Barnett who played my grandson and Degrine who played Tim. I Really enjoyed myself. In fact, I like it here. Well, we're certainly glad you do Well, I might change some of the decorating around here. I think I could send a such more green, you know Maybe I'd add a couple of more halves to that orchestra Of course, I'd be sure not to change those hallmark greeting cards of yours The mighty fine cards just the way they are and so is your playhouse I've enjoyed being here tonight, and I thank you for allowing me to celebrate this happy day with you Fine to have you with us, Mr. Fitzgerald come back and see us soon again And please listen next week when we bring you a great story of the early American West Stuart Lake's Frontier Marshal starring Richard Conte in the role of Wyatt Earp and the following week It will be our great pleasure to welcome the current Academy Award-winning star Loretta Young in a dramatization of Irving Stone's book Immortal Wife and the week after that Betty Smith's novel A Tree Grows in Brooklyn starring Margaret O'Brien Our hallmark playhouses every Thursday our director producer is D. Engelbach Our music is composed and conducted by Lynn Murray and our play tonight was adapted by Joel Murcott Until next Thursday then, this is James Hilton saying good night Look for hallmark cards that are so lonely in stores that have been carefully selected to give you expert and friendly service Remember hallmark cards when you carry enough to send the very best Very Fitzgerald will soon be seen in Bing Crosby's Paramount picture top of the morning This is Frank Goss saying good night to you all and inviting you next Thursday and every Thursday to tune in one half Hour earlier and listen to the adventures of Casey crime photographer followed by the hallmark playhouse This is CBS the Columbia broadcasting system


Earn $100 a day as a teen how to make money online. Okay, so you're this teenager and you're thinking, gee, I wanna make some money. You are in a better position to make money than grownups, I will tell you that right now. And you're in a better position because you understand social media better than they do.  So what I'm gonna do in this video, you wanna watch to the very end is I've got a bonus for you. But I'm gonna give you ways you can make money online and we're gonna get started right now. Now I remember when I first started online and I was single dad, had a couple kids and I came running in the living room because I made a sale. I came in and I said, man, I made a sale, I sold something online. And they were like, that's great, how much did you make? And I said, well, I had sold it for $10 but it's not all profit. And they looked at each other like I was crazy. They thought, wow, dad, you have a pretty good job and $10 is not much money, you know? But that wasn't it. The point was that I could make some money online. If I could make a little bit of money online, I could multiply that and you can too. Now I told you about selling that product and making $10 off it, but bottom line is it doesn't even matter what the product is. I don't even sell that product anymore. It doesn't matter.  Now I'm gonna give you some suggestions in this video of things you can do to make money but I want you to understand it's all about marketing. If you think about it, the products that you buy have been marketed to you. You know, if you think about the best cheeseburger in the world, you know, you may think of some mom and pop type place that has this huge burger and grape bun and all that, but do they sell the most cheeseburgers in the world? No, McDonald's does. Is that the best cheeseburger in the world? No, it's definitely not the best cheeseburger in the world. However, what they've done is they've marketed. So I want you to kind of shift your brain away from I've gotta find the right product. That right product just matters so much and it really doesn't because the right product is probably something that's already selling out there. It's probably something that somebody has already marketed. So it's pretty easy to find stuff that's selling real well, whether it be a t-shirt or a cap or a mug or whatever the heck it is and just go out and do something similar. That's a whole lot easier to do than trying to come out with a next new thing. Okay, I wouldn't ever suggest you doing that. When I first started, I found products that were selling online and then I just started selling those same products. I found the distributor for it, I found the manufacturer for it. In many cases they would drop ship those products for me so I didn't even have to buy inventory. That's a lot more prevalent nowadays. So you can actually find a company that will do that for you and drop ship or you can buy inventory.  I started off initially with cell phone cases and I thought they would sell really well and they didn't and I bought a bunch inventory and I ended up being stuck with it but that's a whole nother story. The bottom line is you don't wanna get in a situation where you have to buy a lot of inventory. It's not the way you wanna go. But you can find a lot of products out there that can be drop ship. You can market digital products. I'll get into all that in just a second. There's a lot of ways to go about this without you investing any money. It's really just a time investment and as a young person, you probably have that time to put into it so why not do it? All right, let's get going and let's look at some of the ways that you can make money online. Number one way you can make money online is through Google AdSense. If you've got a blog, if you've got anything along those lines where you're putting out content, you can actually let Google run ads on your page and they'll pay you every single month. Google AdSense, who would have thought it? Number two is sponsored post. You can do post on your blog. If you have a YouTube channel, you can do videos on your YouTube channel for a particular brand. A lot of these brands will pay upwards of $1,000, $2,000, $3,000 for you just to talk about their products on your channel if you've got any kind of following at all or if you've got people reading your blog. A lot of these companies will pay you. In addition to that, you can have a link in there that will take the people to that particular product and you can make affiliate money too. Which brings me to number three, which is affiliate marketing. Affiliate marketing's awesome. You can go to something like ClickBank. You can go to Share Asale and you can actually put links in your blog or in your videos and when people click there, they'll go there and they buy the product and you get paid a percentage. You can also use Amazon links. If you look below in the description on this video, you're gonna see Amazon links down there. And if you click on any of those things and buy them, I get a commission, which is pretty cool, okay? But even if you don't buy them, if you click on them and you end up at Amazon, you buy something else, I get paid on that. You can do that too. Number four is freelance writing. People need blogs written. They need articles written. You can go to a site like Upwork. You can go to a site like Fiverr and you can actually put up a profile there and tell people what you will charge for writing an article or a blog or anything along those lines. That's another simple way you can make some money online. Number five is coaching. Now you may worry, you say, well, I'm really young, who's gonna pay me? If you have knowledge, it doesn't matter anymore. It used to be we looked at the people who were grown up who were older that had all the knowledge, but that's not the case anymore.  Don't worry about your age. Number six, digital products. Digital products are awesome. These can be simple things like downloads, a $7 download or a $9 download. Now that doesn't sound like a lot of money in itself. If you have enough traffic going there and you have 500 people by your $7 download, it's a lot of money. You have 1,000 people, they're $7,000. So those inexpensive things that you could put in there that earn you an extra stream of income can work out really well. Number seven, it's one of my favorites and that's online courses. That's one of my favorites, because that's one of the things I do. I have a bunch of different courses. But you can create a course on just about anything. And if you don't have any ideas on what you can create a course on, go to something like ClickBank. Look through all the courses that are there now. Look at what's popular there now. Sort through that, find something that interests you and you can do a course just like it. And how do you do a course like that? Buy their course and look at their course. Look at the structure, don't copy it, but you could do something similar and you can actually improve on it. You already know what it's selling for you, already know people are buying it. All you gotta do is do something similar. Number 10, sponsored and social shares. You know, a lot of you have a huge following and you have this audience out there, but you don't monetize it. You can do sponsored shares and you can do sponsored posts on your Instagram, on your YouTube or whatever and actually get paid for doing that. If you've got a following, which I know a lot of you do, make that available to brands so that you can make money off your audience. Now I wanna talk to you real quick about this age thing. It's really funny. I know a lot of young people will get all worried. They're like, well, how am I gonna make money as a teenager? I'm just a teenager. I'm just this age. I'm just 19 years old. I'm just 18 years old. Nobody online ever asked you your age. They just don't. Have you ever thought about that? If I go and I see a product, a t-shirt, let's say I see a t-shirt and it's being marketed on some social media platform. I don't go, wow, that's a nice t-shirt. They got my size. The price is right. I like the saying on the front of the t-shirt but I wonder how old the person is that's selling it. That really concerns me. No, it doesn't ever happen, does it? They don't care how old you are. And conversely, they don't care that you've got a whole lot of business experience and that you're super smart and that you know all these things either. They're like, well, I wanna buy this shirt from that guy because he's really intelligent. They don't care. They're just buying the product. So don't get hung up on your age. And this goes for you also. If you're in your 80s, they don't care, okay? They just want the product. Age doesn't matter online. As a matter of fact, online, it's a great equalizer. It doesn't matter if you're male or female or what country you come from or how old you are. None of those things matter anymore because it's all about the product. You've gotta get in your head that it's not you they're buying, it's the product. Now, if they're buying coaching from you, if they're buying advice from you, yeah, they're gonna want some background. But this is all about how a teenager can make money online. But you know what, if you're 80 or 90, you can do the same thing too. And you may actually be surprised the audience that you draw. I have a YouTube channel every now and then I'll go in and look at my data and I'll look at who's watching my stuff. And I would assume it would be a middle-aged white guy that would be watching my stuff and nothing could be further from the truth. As a matter of fact, the majority of my audience is 18 to 24. And the rest of them is like 24 to 35. And almost nobody my age watches my videos. Makes no sense, but they just don't. Now, I'm sure I have some outliers out there and some people who actually do watch it. But bottom line is you don't know who you're gonna attract online. You don't know who your audience is gonna be. So you wanna go in and check that every now and then because it may surprise you who's really watching your content. Now, I hope this video helps you out. I hope you understand that age is not a factor. You probably clicked on this because you saw Teenager and you're like, yeah, I could do that, I'm a teenager. But bottom line is it doesn't matter. The very same things that I show you that you can do as a teenager, you could also do if you're 50. You could also do it if you live in India. You could also do it if you live in Pakistan. It doesn't matter because the internet is the great equalizer. But what I did wanna do is show you that no matter what age you are, because if you're concerned, I know when I was a teenager, I was concerned too. I'm like, well, I don't have enough experience to do anything and back then they didn't have the internet so I didn't have this opportunity. But you guys do have this opportunity and since you have it, you wanna seize it. You really do. All these things I've shown you you can do right now, just overnight you can start doing these things and start making money. It's really that simple. Don't forget also, I have a course down below that was a bonus I wanna tell you about. It's absolutely free. It's normally a $97 course. You can click and get it for free. It's down in the description. Also, if you haven't subscribed yet, please do so. Hit that subscribe button down there. If it's red, you need to click it, turn it gray and you wanna turn on all bell notifications.  Thank you so much for watching this video and I'll see you in the next one. Hey, thanks for watching my video. Don't forget to subscribe to my channel and click that little bell right there so you can be notified every time I do a new video. Also, click on one of these videos there. Keep watching on my channel.


Another group of protostomes are mollusks. Now, mollusks, you should probably be thinking, whoa, holy diverse group, and this is. This group has a lot of different kinds of critters. We have slugs, sea slugs and land slugs. We have different kinds of critters that live in shells like bivalves, snails.  I would imagine that the octopus probably yumpsulized the bivalve and then stole its house.  But cephalopods are squids and octopi and those guys have some unbelievable characteristics. But remember, I don't want you to forget where we are. Remember that in our cladogram, we're just grouping these guys all together as protostomes. So look at, this is actually, what is that thing? An octopus we found at the tide pools in Humboldt County and we actually got to see this thing change color. Like it was crazy talk. Right now it's red. You might be looking at it and going like, sure, red, Wendy. And the next thing I knew, it turned the color of the rock and I got to watch it actually happen. So when I observed this, I don't know, three years ago or whatever it was that I found this awesome little octopus in the tide pools, I thought, how does that happen? How does an octopus actually change color like that? And the fact is it has these little structures called chromatophores and take a second and visualize this for me, okay? They have these little bags of pigment inside their cells. So I've got a bag of, let's say, blue pigment or red pigment. This guy has bags of red pigment that are showing. And there are little muscles that are attached to the outside of the pigment. Can you visualize this? The bag. So here's, oh, yikes, I can't do both. Here's the bag and here's the muscle. When the muscle contracts or shortens, you can't even see that anymore. Here's the muscle contracting or shortening. It's going to pull when it contracts and shortens. It's going to pull the bag into a bigger diameter, which means the bag of pigment is going to take up more space. Can you visualize that? If those smooth muscles relax, the bag of pigment is going to shrink down and be smaller. If the bag of pigment is big, you're going to get a lot of that color. If the bag of pigment is small, you're going to get a little bit of that color. And you can see here, these little muscles, here's the pigment. This is inside a cell, and we're just going to change the size of this. Now, you know that I have to show you a video. A video that I will try really hard not to dance to because, dude, this is so awesome. They put, I don't know how they did this. They put, this is the skin of a squid that has chromatophores in its skin, and they actually zoomed in so that you can see little round circles of pigment. And then they're playing music, and it totally matches to the music, but the chromatophores, like, dance. They get bigger, which means those smooth muscles are contracting, and then they get smaller, which means the smooth muscles are relaxing. Okay, just take a minute to be amazed by this, and then in class, we'll actually watch a video of mollusks hiding, cephalopods, hiding because they've got these unbelievable chromatophores. Okay, get ready to dance. Those are chromatophores. I'm loco. I'm loco. What the heck? Dude. Oh, my gosh. There, little, big, big, and all that is, is contracting. Dude, it's awesome. This lets them change color. You gotta go look up chromatophores on YouTube, and you can literally spend the next three hours just watching all the different videos. It's fantastic.  We'll watch it in class if you don't go watch it, or if you don't come to my class, then you have to go online to find it because it's really cool. Okay, now we're gonna look at arthropods. This class is so awesome.  Arthropods are amazing. Let's go learn about them.


This is our June 2023 hyperledger presentation. So, before we get started, I want to thank the financial markets special interest group and the hyperledger foundation for their ongoing support and making this meeting possible. I think we have a really good agenda for today. But before we do that, let's go ahead and take care of some what I call house cleaning. As always, please note that this meeting is being reported and it's under the umbrella of the hyperledger foundation. So we ask that everyone abide by the antitrust policy and code of contract. And the antitrust policy states that we avoid discussions of companies specific pricing products and projects. We don't make negative remarks about other companies or products, and the code of conduct means that we treat each other with respect we never discriminate, and we communicate constructively. We really support hyperledgers policy of openness, equity and inclusion. And for new participants and I think we will have quite a few today. We welcome you and if you'd like, please introduce yourself in the chat. And if you have any questions or any specific areas of interest, let us know in the chat we want to make this as interactive as possible. So our agenda for today we've already gone through the general introduction and welcome antitrust and code of conduct will speak really briefly about some of the hyperledger community information. James will walk us through an update blockchain in the mortgage industry, and then we'll have Devin daily, the chief revenue officer from true will walk us through true what they are, and their use of AI and blockchain and q amp a. For this slide in each meeting, and that's very purposeful. This is to reinforce that we're all on the same blockchain, and now AI journey. We may be at different points along that path for today's discussion in particular and we're all still learning about AI, especially with the advent of chat GPT. Today's session is part of a series on AI and blockchain, and we'll focus on education. As I mentioned in the previous slide will continue to dive deeper into this topic. As speakers come in and talk about how they're using AI and blockchain, and we discuss actual use cases. Okay, I'm just going to go through the next couple slides really quickly. These slides are intended for those people that are new to the group and would like more information. So this slide provides a link to some of the different resources that are available through the Linux Foundation hyperledger foundation. And as you can see second from the bottom the link to our subgroup wiki to access that information you'll need an L F ID and this information will help you get that. And then lastly here's just some blockchain training. I've taken this training this is how I got introduced to the technology, and it's extremely helpful I highly recommend it. So with that, I want to turn it over to James Hendrick who's going to walk us through the state of blockchain. And Marvin, thank you very much if we can move on to the next slide. All right so starting out with community by nascom. So this article provides a high level overview of blockchain and distributed ledger technology, as well as the basics of smart contracts so excuse me everybody. And Marvin mentioned if you are new to this group. This is a great article to start with. If you're not familiar with these concepts they do give a really good quick overview to help get you grounded. The article goes on to discuss how blockchain and smart contracts can improve low management through speed security and cost efficiency. Some of the topics we've talked about prevents previously. It also goes into data science and how it can prove improve low management by providing lenders with valuable insights into borrowers and their behavior. So this is information such as credit squirting fraud risk modeling customers segmentation all of these can be analyzed and addressed through blockchain. Moving on to the next article so JP Morgan Chase is developing a chat GPT like software. You know as we all know Jamie diamond is not a big fan of crypto but he is very bullish on blockchain technologies, and what they can provide the financial industry. So the company has applied for a trademark product called index GPT this last month on index GPT is touted to tap cloud computing software using artificial intelligence for analyzing and selecting security's tailored customers needs. The technology has a range of possibilities within the financial industry. Thanks, including Goldman Sachs Morgan Stanley have been testing it for internal use. And these include ways to help Goldman engineers create code or answer Morgan Stanley financial advisor queries, but JP Morgan may be the first financial institution aiming to release a GPT like product directly to its customers. So JP Morgan they've got about three years on the trademark in order to get approval. It's uncertain yet exactly how JP Morgan will deploy it but the article does close discussing how financial isers have long feared the arrival of technology. That's going to be good enough to displace their role in markets. So far those fears have largely been yet to materialize and we don't know that we'll be seeing them. The third article we've got on here is from Black Knight. So Black Knight's been evaluating the applications of artificial intelligence to such areas analytics AVMs mass customization process improvement. The article discusses how AI and ML and the mortgage industry started over a decade ago really. And Black Knight is currently using an AI driven point of sale transforming the customer experience and delivering a highly personalized assistance when and where customers need it. So Black Knight's slow walks buyers through the next step provides a self guided process for the customers. They've also got AI and correspondent lending that's assessing the completeness of the loan packages and settling conditions when information is missing in underwriting they've got a hard automation that can be used to maximize underwriter efficiency and reduce risk. And in the lead generation space by using predictive analytics to identify opportunities and deliver a highly personalized communication again with the customers. Marvin the next slide. This is probably the biggest article we have this week. Last month just a couple weeks ago MBA education hosted an event sponsored by Adventus, there's a dentist. Marvin band to again was the moderator. We have a panelist that came together talking about leveraging AI blockchain and newer technologies and today's challenging environment. We had Leah Price from figure Mark D'Angelo PB from Finlocker myself. We started off with an overview of blockchain AI and data fabrics and what they are. The panel went on to discuss challenges or changes in the industry over the last seven years towards these technologies, and how you know CEO CTO CIOs are all evaluating these technologies and what their opinions have been. As we see them more integrated into our environment. We also talked about how companies in the mortgage industry are implementing these technologies, as well as at the recent MBA technology conference AI was a big topic of discussion there. You know one of the big focuses that groups were looking at was the risk using chat GPT proprietary data, potentially allows us chat GPT to learn from that data which can then be shared with others so there's definitely a concern about that it's really all about how it comes to deployment. Ultimately, the conversation was followed up with a discussion on data analytics. It's roughly about a 60 minute webinar it's available from both the MBA education website as well as our hyper ledger wiki. Marvin anything else you want to share about that event. I know I, other than it was, I think a very interesting panel discussion it was very interactive. Everyone on that panel was extremely knowledgeable about the different technologies. And I think what was most interesting was everyone's ability to talk about some of the challenges, and how companies can take a lightweight approach using POC's using small cases, a way of making some of these new technologies, a bit more approach. Yeah, fantastic thanks for sharing that Marvin so if you get an opportunity and you've gone our, you know, a great presentation to attend we do have a deck that went with that presentation to that has a ton of information and the appendix as well so if you guys are in need of that at all feel free to reach out. And then Marvin next slide. And then you know, last a monthly reminder this is our wiki page. Do you know take a look at the link below will drop it into the chat as well. In fact, I think I'm just dropped it in there for us. You know, save it as a favorite the LF ID that you need to set up there's instructions in the upper right again it's a free service to to set up if you're interested in any of our previous presentations over there on the left hand side. And then over the month over month presentations and there's links there to both the videos as well as the, the PowerPoint deck that went along with it. And then over on the right hand side you'll see we continue to update and add our newest articles. We're looking for a different additional information, as I've mentioned we've curated in the last year and a half well over 200 articles on blockchain. We've started to add some AI articles in there as well so feel free if you don't find anything on the, the wiki site that you're looking for. You know be a pleasure to have you reach out and we'll share what we've got. Marvin next slide. And I'll pass it back over to you. Hey, thank you James. Great information as usual. Next, I'd like to introduce everyone to Devin daily he's the chief revenue officer at true and board member of the New Jersey MBA. He's an experienced senior executive with the demonstrated history of working in the computer software industry. He's skilled and has significant expertise in banking enterprise software sales e commerce and entrepreneurship. He was also part of a panel discussion yesterday titled how AI brings trust to borrow data. This was a very informative session and I highly recommend it. We'll see if we can share a link with the rest of the team as well. So with that, let me turn it over to Devin. Hey, thanks Marvin and first let me thank everybody for joining today. We appreciate it and want to make. I want to start with, you know the concept and we're going to talk about artificial intelligence we're going to talk about blockchain we're going to talk about the two how the two can interoperate. And I think it's always important to start with the basis of both of those and the basis of both of those is data. And I start there with, I start with that one because it's the truth but at true. That is the core of our mission in helping lenders manufacture automation quality data. You know what I often say to, and I've said for years even, you know I spent the last five years at a large independent mortgage bank, where, you know, we consider robotic process automation. And it's a robotic process automation is a wonderful technology but we've got to do something about our data, because the only thing that RPA is going to do for us is help us make bad decisions faster. And so when we think about manufacturing, you know really good data. It's about leveraging machine learning to do that it's about finding documents and direct source data and being able to cross compare it. You know again when I was in the mortgage industry, not as a not as a solution provider, one of the things I was focused on was continuous QC. I was focused on that because it just seemed that we spent an inordinate amount of time at the end of the process, trying to make course corrections. And then we would incur what seemed to me to be all of these unnecessary fees might be increases in dwell time might be suspense fees, you know, whatever it equated to it just seemed unnecessary. Something I had never done before I had an opportunity to look at post closing, and my God did I get an appreciation for the mess that is post closing. And, you know, I thought I was going to be able to go in and pull reports and categorize and codify all of the various issues across all of the 10 or 12 investors and. Yeah, I was, I got an education in four hours and said my God, I have a new appreciation. And actually at the same time, I was evaluating replacing our current OCR vendor. And there were a number of reasons for that. And it mostly because, you know, the state of the current OCR technology was, there's, it's not no human in the loop, there are a lot of humans in the loop. There's a lot of humans in the loop and in order to go from a 68% per page classification accuracy to a 90 plus per page classification accuracy and by the way, automation quality data is 95% plus. Okay, that's a huge gap. And my assumption when I was a lender was only 24 hours is okay, but 24 hours is okay, and there's a lot of manual intervention so is it really any better than what I'm doing now. The basis was, I've got to get above 9095%. I've got to have that automation quality data extracted after you got to have great document classification because only then can you extract data, and only then can you compare across the document set, which will give you automation quality data. And what I found was, you know, one that's the basis because on top of that that's where you layer solutions. That's where you put your income analysis that's where you put your asset analyzer. That's where you put your post closing QC right, that's where you put your audits. But if you're doing all of those functions based on bad data, we're in the same position. So, you know, fast forward. That's one of the reasons why I landed it true. I was really impressed with, but frankly what I said to them was, what you're doing is so great, and so far advanced with regard to the rest of the industry and your competitors. But you don't have any solutions on top of it. And a year later, we're launching and our solutions as you'll see in a moment so you can go ahead and advance the slide Marvin. One thing about it is where we began and why we're so good at what we do. Our founder and CEO, Ari Gross is a PhD in computer vision, and has been doing this and is so passionate about the pursuit of excellence. You know when we get to 99 five we're at for one of our clients in terms of per page classification accuracy. He's the first one to say, how are we getting to 99 eight. There's no resting. There's no resting for us. And so that's where we began you know we began coming out of a research lab that was led by Ari. And as you can see we have a couple of fairly large clients up there. And we've pursued since then, you know a real focus on the mortgage industry. And so today we recognize 850 plus document classes, and we extract about 8000 plus data fields, which gives us, you know when you look toward the other end of the spectrum here when we talk about solutions. That was the basis and some of you may have seen this. I think we presented this as digital mortgage before I was part of the company. What we at the time called our audit tool where we can certify across documents that the documents are consistent. And then if there are inconsistencies we can throw those up to be manually attended to right, and then we can take those documents the authoritative source documents and compare them to date in the loan file. So we can do a really thorough audit. When you think about that for a moment we were really using that and still using that in a post close QC world. Our objective in our strategy going forward is to still start there because I think one of the tenants that we have to begin with is like any technology, especially any new technology we've got to experiment we've got to have a proof of concept as Marvin mentioned, once we prove it, that's going to help us with what the really difficult part is and the difficult part is not technology. It's the humans that use it. It's the organizational change it's the process change. And if you're not comfortable or you question the technology and or you don't have internal champions, stop. So, begin with the proof of concept and our recommendation, because it can be so powerful is start with post closing QC. Because in post closing QC now you can add a layer of intelligence coming out of there, right, on top of all of what I just mentioned in terms of that data analysis and data validation and data audit. And we do that via business rules. So we can ensure that you know the note rate is the same from the authoritative source to all the other documents and when it's not, we throw you an exception, right, and we do that via business rules. Why do I mention that because when a business rule fails that begins our quantification of where the issues are in your manufacturing process. So now within a couple of months I can give you insight you've never had before. And now we can sit with lenders we do is say listen here or where your defects are right here's where we started here's the proof of concept, and you've done the manual review and you trust the data. So now let's shift left. Now let's go into the origination process and catch these. We would treat it like a manufacturing process right. It is a manufacturing process. Let's shift left and deploy this solution to catch it at the point of entry. You know we all know the 110 100 rule. It takes a dollar to collect and correct. It takes $10 to correct it and $100 when you're done. So why spend $100 and in the mortgage industry it's more like 10101 thousand dollars right. So let's spend all that money and post closing QC, when we can identify where our defects are and where they're being injected into the process and shift left right solve the solution at the point, and that solves a couple of things, and you can advance the slide. It, it solves for the organizational change working backwards let people become comfortable with the technology and comfortable let them have assurances that they're catching this in a way and at a speed that humans never could. And we're not here to replace humans. Right. That's not what this technology about you know we talked about earlier replacing financial advisors. I think we're going to replace financial advisors. I don't think we're going to replace loan officers but what we are going to do. They're going to be the intelligent operators of AI right Leah price we talked about Leah earlier she educated me I talked for a while on this and Leah said oh yeah there's a term for that it's called prompt engineer. And those are the people with the domain knowledge that asked the really smart questions to drive the artificial intelligence. And so we're not replacing humans. We're bringing them to their highest and best use based on their knowledge, you know, no longer are we staring compare folks no longer we document classification or data entry people. We're using our higher order skills. So, what does the technology do and at true how do we specifically do it. So OCR technology has been around for years at true we have our own OCR engine but most importantly we've deployed machine learning machine learning to learn documents but machine learning also to learn from where we still have humans in the loop. You know, as I mentioned before, we have a bunch of customers when I tested the platform out of the box it was 96.7% accurate. The same. It still means that I've got to touch 3.3% of the documents. Now that's vast different than touching 100%. It's an even, you know, and a vast difference from touching, you know, 30% of the documents. That's a 90% reduction. In fact, but how do we do it. Right. We don't use OCR technology the way it used to be. We use contextual classification, which means, you know, for a guy, you know, not a guy in computer PhD and computer vision that's me. It's, I explain it the way, you know, Ari explains it to me it's the way a human reads the document. We have a large corpus of mortgage related terms, we know what documents. They occur on. And so when we're reading those documents we can say this is an income statement. This is W2. This is a closing disclosure. This is an LA right and we classify accurately and then can extract the data. Right. The other thing that we're actually seeking a patent on right now is something we call provable correctness.  And that you can improve over time humans tend to, you know, reach a ramp of performance there's a lack of domain knowledge to domain specific knowledge. My performance doesn't improve with machine learning and with machine learning, my performance can dramatically improve over time and dramatic over time can be 95% accuracy to 98% accuracy right because that's still when you think about that. That's a 60% improvement from, you know, 95 to 98. And then we have our machine learning models. And one of the things that, you know, there are a lot of mortgage lenders out there, and we have some as our customers will say, I'm not necessarily, you know, I don't really want to contribute what you learn for me to everybody else in the industry. And that's a valid statement. And so what we deploy is we deploy both our global catalog what we know, and then our local learning catalog which is what we know about you and what we've learned through you. So you keep your learnings. And we have our global learning catalog and that's the way we assuage those concerns. So, let me pause here for a moment. Are there any questions about how we do it specifically. And obviously I can't go into everything but if not then let's roll them the next slide. So, Devin, I do have a question because what you said on provable correctness really stands out to me because I spoke with a bunch of OCR companies and every time they come in and make their pitch for one of our clients they their pitches, give me 10,000 samples, two months of time and $100,000 in fees and then I'll train my software for your problem.  So what you're talking about it is something different. And I want to understand that difference and I think you're getting to it when you start to talk about the technology but I wanted to have that context. Yeah, and I think, you know, I think what makes the difference is a true we're focused on the mortgage industry. So we you're not going to incur that cost. We have a global catalog, and our global catalogs probably going to match 90% if not 95% of your needs right here and have custom docs we need to recognize of course you are. But the vast majority of the docs, you know, we're going to recognize right out of the box so there's no no learning opportunity that we need to do. Yeah, unless you have custom docs. And we do that rapidly we can actually give our local learning engine to our clients. So there's no professional services engagement, unless you say which a lot of customers say, hey, I don't really have somebody that does that and you know we're really busy closing loans over here so can you do that, but we actually enable our clients through the local learning to actually recognize the needs themselves. And over time, right. If it's their humans in the loop, right, their folks who are saying hey, you know we have a threshold, like any other AI engine or confidence level. If it doesn't meet our confidence level it goes to a human for review. When that human recategorizes that we learn from that action so we, you know we take that into account next time we make a decision. Okay, that answers that answer your question more. Yeah, absolutely thank you. Let's let's advance the next slide. So, we had a little overlay here so I think we talked about this, you know, already we have a mortgage specific AI, we are BPO free, right, but, you know I want to be really accurate here I think one of the hallmarks and, and some of our customers and prospects have said to us one of the things you know that's refreshing talking to you, you're not telling us where you're going to recognize everything and you're not going to touch anything that's just not true we're not we're not there yet with the technology. We do have customers we have one MI customer who's 99.5% accurate and granted they've been a customer for three years, but we started out about 94% accurate for them right and they deal with you know they touch a lot of loans. We do have we do have BPO partners we don't have our own BPO shop because we don't need it, quite frankly. So, 100% BPO free 100% BPO free is going to get you about 95% of the way there, you still need people to go in and do the exception processing. Hey, the threshold that you set for this CD was 75%. It came in at 74%, somebody's got to review it. Yes, it's a CD. So we'll learn from that the next time. In our documents we talked about we have really robust technology it's not just OCR we use machine learning. And, and again we've been vested five years specifically in the mortgage space. One of the sexier things about this is because our classification is so good our data extractions of really high quality and a lot there's a lot of data coming out. The challenges that our clients have and this is a good challenges. I'm getting so much data from you that I never got before. How can I leverage it. You know how can I use other forms of AI predictive analytics to understand what customers are my ideal customer profile to maybe help prioritize for my LO is what prequels they should be following up with right. And to look at our servicing couple this with our servicing data and develop models, you know, to understand risk in the portfolio. So the next challenge I think for customers when we start to deploy this is, how can I monetize all of this additional data to further improve my operation. You know the next slide. And these are just some results I do want to point out the 300% increase in underwriter, underwriter productivity was specific to MI. We've talked about one of our MI clients there. The data captured, you know, 8500 data elements, really rich sources. The other thing that helps us do those 8500 data elements is to a really fine grained audit across the documents and across the documents to the data in the loan file, and we do that really rapidly. Next slide.  We're finding a lot of use cases obviously in MI, but also with servicing loan onboarding as well as correspondent lenders. You can go to the next slide. And here are the solutions that we're working on.  We're going to do a partnership with clear capital will work on our collateral analysis and asset analysis, and then later this year we're also going to have a fraud detection. I'll say it again, you know, we're really fortunate to have been founded by Ari Gross, who's one of the world leading experts on font detection. This is such a specialty, but that is so important in fraud detection, right, not only how the documents and images line up but the font and the minute differences between the font so we're going to have a significant advantage when it comes to our fraud detection solution that comes out later this year. So, you know, we're looking at the solutions the loan verification the compliance suite compliance or true quality automation. And we talked about our audit and QC here so I think what's on the next slide Marvin is kind of where we go next and this may be, you know what, you know, where we started kind of overall discussion about AI and blockchain if that makes sense. All of this information really ties together well from an OCR AI and blockchain perspective. I was actually in an MBA technology forum meeting about three or four months ago, where they said that the IRS had actually come to them and they were working on an OCR problem. They were trying to trying to digitize a lot of the tax information that they received, and the problem that they're grappling with is their accuracy threshold was about 6566% and then the people within the MBA we're saying that from an industry perspective. It's more 9095%. So, what do you think are some of the key steps that a company or an entity like the IRS needs to go through in order to get their accuracy from 6065% initially to what we're seeing as an industry. That's that's a great question and it's, you know, while true has focused on the mortgage industry, we do have tax solutions, you know we have really large tax tax practices that use our, you know, use our technology so we're familiar with that space. I think it's about a combination of technologies right. It's tough to start from a generic platform. It's not where you don't have any industry knowledge, but the IRS is certainly in a great position because they have volumes of data, right that they can rely on and back test on. I think it's really just about, you know, deploying the right resources, I mean, you know, years ago, I got in introduced to unfortunate to live fairly close to Princeton University. And I got introduced to several and you know if you know anything about the applied mathematics field Princeton University has the best program in the world. And I got introduced to 12 PhDs were focused on applied mathematics, and these are the guys that solve problems, you know it's the PhDs and applied mathematics that can solve those problems, you know, and, and by the way, going from 65 to 95% is a mammoth task. Believe it or not going from 95 to 98 is an even, it's probably an order of magnitude even larger than the 65 to 95 so they're probably going down the wrong, going down the right track I should say forgive me if any from the IRS is listening. It's a big problem and it's going to be time consuming and I think it's, you know, it's about bringing in people with domain knowledge. You know the right domain knowledge to get that done. It is a mammoth task. And, you know, perhaps, you can refer me to those folks after Marvin. I'll see if I can dig up their information. One of the limitations that they were facing is that they could not use any type of BPO services because what the people in the group thought was okay if you're trying to go from 60 to 65 to 290. You will need not necessarily an army, but people to actually support that step. But that's not something that I think the IRS as a government agency can can utilize so I think that was one of the problems they were grappling. And you know what, here's what I'll tell you about that. We face that problem and we face that problem currently, as we think about expansion. And I'll just say maybe expansion a little bit north. There are laws stricter than those in the US with regard to privacy information and data can't be moved outside of the country. So what true has developed because we have this large purpose of knowledge about words and phrases used on us residential mortgage documents, we've started this automatic redaction process. So, potentially you could use a BPO process if it's 100% automatically redacted. And then you have to determine right if it can't be 100% automatically redacted. So what's the nature of the information that I'm giving out and is it coupled on this document or across documents with any other so I think that's a that's a problem that can be solved pretty readily. I think we'll have that solved in the next three months. Great, that's a very encouraging to hear. And you mentioned using word recognition, because going back to the MBA webinar. One of the things that we'd mentioned with the MBA audience is from a chat GPT perspective. It's built on I think 100, 176 billion tokens. Those are letter combination word pairs so in the word recognition approach that true has. How does that compare in contrast to the token approach that chat GPT uses. Are they compatible or why misunderstand the whole process. You don't misunderstand it all you, as a matter of fact you've nailed it and those tokens can be applied and associated with multiple documents. And then it's about the relative use that that one token compared with the other token to make, you know, the likelihood that this is an income statement versus a pay stub, right. Yes. And so you're 100% right in drawing an analogy to it it's just that the, you know, the body of knowledge the language model is a lot smaller for us. And quite frankly that's why we just did a test against Google and AWS with regard to pay stubs and w twos and our results were stellar. And it's because of that contextual classification. Great. Are there any questions from the audience. I have a ton of questions and I'm really excited about this topic and and I saw the presentation that you guys did yesterday but I don't want to monopolize this discussion since we do have Devin here so if there's anyone else that has any questions by please feel free to type it into the chat or just to log into the discussion. Okay, if there aren't any other questions from the rest of the team I did want to get your thoughts Devin on how blockchain ties into AI and what you guys are doing around true I when I was speaking with Devin at the MBA tech conference, he says that blockchain can be a basis for that common understanding of all of this mortgage information if you start with and this is going to be a crossover simplification. So if you start off with correct data and a common understanding and that forms the basis of the blockchain, and then apply AI to that, you have a robust technical solution. Yeah, yeah. I'll say a little bit differently and I'll draw a direct analogy to what my objective when I was still, you know, on the lending side of this was, you know I talked about continuous QC. When you think about continuous QC right if I can take my continuous QC efforts and somehow guarantee and certify the actions that I've taken. And create a fast way to audit it, aren't my loans, you know I used to say, you know, maybe flippantly aren't my loans worth more, you know, on the secondary market, right. And so you begin to ask that question and say, Well, how really could I engineer that and I think I think the answer is in in Ari's answer, and the way I thought about it was in the way I think about blockchain. You know I've heard a lot of in you and I talked about this Marvin, I've heard a lot of people say I'm going to build an application on top of blockchain and and to me that's the antithesis of blockchain right you don't want to build an app keep keep your keep your applications built on the proper technology and and for for me. The continuous QC and that rapid audit capability was the ideal use of blockchain and here's what I mean by that. We're using documents, we do intake of direct source data. Right, maybe I'm using form free maybe I'm using plaid I'm getting or I'm getting income data from a document. We do a fraud check on that document projects fine. Right, we hash that document, we take that hash, and we store it in our system of record but we also put it on the blockchain. We extract data from that document. We take that data we put a hash on it, we store it in both places, and we create a link between the document and the data. We make a decision and run a calculation. We do the same thing that same process step three create hashes store them separately. And now we've created is this auditable trail. Right, we make a decision based on those calculations do the same thing. So, at the end of the process, what we have is really a completely auditable and certifiable process that says, I got this document or this data from this place at this time the borrower uploaded at the borrower, you know signed into form free and got the data this one, however we obtained the data, we certify that process we store it. We extract data from it, maybe it's just an XML file or JSON file or we've extracted it from a document. We run calculations on it, and that whole process is certified.  When you begin to, you know, use TPR firms, there can be a significant cost there. And so, is there a way to say to the TPR firm, I've done a lot of the work for you, or this third party has done a lot of work for you, is there a way to lower that overall cost, not only lower the overall cost but make this a lot faster. Right. So at the end of the day, the TPR firm is lowering their labor cost. The lender has, you know, certified this loan and perhaps can get, you know, to cash flow a lot quicker and start producing a lot higher quality loans that are potentially worth more on the secondary market. If they're worth more, I'm certainly getting, I'm certainly speeding up my cash flow. And so that's the way I think about, you know, and obviously that's the inner play of AI and blockchain because we're using AI and machine learning to do all of that data certification and data on it. So it makes sense. Absolutely. And Devin, the way you just described that the interaction of blockchain and AI, the use of hash, the how to expedite processing, and I can't help but think that you must have been sitting in on our design discussions, what we were building out our EOC, because you pretty much articulated the problems we faced and then the approach we decided to take. So I think that's what a lot of people, at least those working within the blockchain industry are realizing that that's the best way to utilize blockchain is to minimize that processing cost, use that technology in a more efficient fashion. So yeah, that was great. Yeah, great architecture.  You know, three years ago was hey I'm building something on blockchain and here's a check for $10 million. Yeah, yeah, I think people have just driven a lot by that even though it was probably the wrong decision. Yeah, and now we're trying to use that same type of approach with AI. I've been pitching AI I haven't gotten the check for five or 10 million yet but I'll let you know how that goes. All right. Yeah, let's have one question. Don't laugh at this but I've always wondered about this. I joined sorry half hour late. But looking at this today and sticking to mortgages, why can't I get a mortgage today in two minutes and close in three minutes. What stops us considering considering I have a, I do have a credit score like most people do. So, I don't today understand why I can't get a mortgage, not that I'm in that market, but why couldn't I get a mortgage in two minutes and then bring in a new technology like AI chat to GPT with all its flaws that I've been looking at. Is that the expectation in the future I mean what is it vested interest around that closing cost of $57,000 or what prevents that from happening, because I can get a car loan that fast but I can't get a mortgage. Yeah, yeah. Mortgage has got a backed asset the card to drive it off. And a car's mobile, right. I can smash it in five minutes. So, why is what prevents that from happening today. So, I don't think the objective is wrong. I think, you know, the two minutes or even two days is going to be, you know, quite some time before we get there. There are still parts of the mortgage industry and I know you're going to find this surprising that are pretty archaic. You know, we've got a lot of counties. And Marvin sent me a document on that that I read through and I saw that on there about the paper. Just wondering, sorry for interrupting but yeah. No worries. I was just going to say we've got 3400 counties. The first challenge would be to digitize all of them. You know, and even I think the top 500 counties are responsible for 93% of the mortgage transaction so maybe let's just start with 500. And I think that's the ground title and I think, you know, the appraisal process I know the GSEs are really laser focused on on the appraisal process. Appraisal automation and firms like Clear Capital that I mentioned before, you know that we're going to partner with are focused on expediting that as well. I think we've got several years but I think we've got, you know, and everybody uses that that analogy about, you know, getting the car loan. And is legal, the legal system locked into this also to because obviously the attorneys involved in a lot of the stuff and closing costs whether it's commercial or residential are they do they somehow under the evidence that they are sticking a wrench into this. I don't know if I'm going to get in trouble for saying this but there's a lot of people with their fingers, you know that drive the cost up under the ages of let's protect the consumer. There's a lot of people that in the middle of all of these transactions, particularly at the back end and you know, not just particularly at the back end but during you know during the process as well. Right. Interesting. And Jeff, if I could take a stab at answering that question. And I'm going to go back to part of the discussion that you guys had. I think that a significant portion of the origination costs is due to compensating the sales people. So my personal theory and I'm probably going to get into a lot of trouble with this is a lot of what's preventing a two minute or even more one minute loan closing process is the social inertia that's built into the current system. If I'm a mortgage broker and I'm getting and I'm going to just make up a number $1,000 per loan. I'm going to invest in my best interest to make sure that I'm a part of that process, and continue to work with you for better or for worse, to get that loan to where I wanted to go, as opposed to something that's completely automated. Let me give you an example about 10 years ago, I was working on an online loan application for a bank that I can't mention the bank, but it was for high net worth individuals to get loans, unsecured loan that you have a credit score of 820.  Okay, if you're a high net worth individual, I as your banker and incentivize to give you a loan for up to $20,000 to buy a racehorse to buy a car to buy whatever because at a credit score of 820, you're going to pay back that loan period. And so we built that application, and that was up and running and still is up and running. But the problems associated with a mortgage are several magnitudes more complex, because of the social inertia of the loan brokers of the different parties that are part of the process, and honestly, a lot of the risk associated with it. And if there's any type of fraud, losing 20,000 is significantly less impactful than losing 500 or average cost of a house now 350,000 400,000. That's why I think we're still not seeing the two minute loan. Honestly, I don't think we're ever going to see that I would be happy with the 24 hour long. James anything you want to add or shoot me later on. No, no, actually I would tend to agree with you Marvin, you know getting down to a two minute loan I think would be fantastic. The reality is I see the process being shortened down to two and three days over the next you know several years. But I don't know that until you know, much like I think Devin brought up until you get all these title companies and others, you know integrated into a, you know similar platform or connection methodology. I just, I don't see that we get to a point where you've got pretty much instant instantaneous approval. Yeah. And one last thing that I want to add in this, we actually build a proof of concept for a loan origination process, where we could get through and this was simplified through the entire loan process within a two hour window, using integrations using APIs. So, technically, it's feasible. It's not the technology that's the problem. It's the process and the regulation. And for the amounts that we're talking about maybe it's a good thing that it's slow. I mean, I'm really impatient. I mean, I'm a tiktok user anything that's three minutes long, I can't pay attention to that so I need a loan, I need a loan quickly. But I think right now it's still a good thing that it does take at least a week or a couple days. Yeah Marvin you may want to move on if you do that's fine if not, I can add a comment to this if. Yeah, definitely add a comment. Alright, so Jeff I'm an old mortgage technology writer I've been writing about the industry since 97. And we sometimes forget that this is a risk based process. So, Devin, what did you say you guys are like 97% accuracy on those docs now. Yes, that's awesome. That's awesome. So that means only 3% of the time you're going to miss and Marvin's right. The loan is about $350,000 so if you do 100 loans. Three of them are going to be bad now when you make a good loan is a lender you're going to make what two grand well in today's market the profitability is actually very close to zero, but on a bad loan, you're going to pay back the entire amount. On those 3% you've now lost a million dollars, how many loans do you have to make to make that up. You're not going to make it right. So remember this is about. This is about risk averse business people trying to make sure they don't lose money and so it's never going to be a two minute loan. It might be a two day loan is more likely going to be a week. So in today's paper world, I interject. What's the, what's the miss rate on, I've heard horror stories are even around title clearance and a closing when somebody goes back later says you couldn't do that. How often does that happen. Every time. That's what you know that's why what Devin's talking about today is so exciting because in a paper based world we have errors all through the process and as Devin pointed out, the longer it takes to solve it the more expensive it becomes. And if you look at JD powers surveys of borrowers, the thing that makes them the angriest is being getting to the closing table thinking it's all done, and it's not done. But the changes in trade change that a lot because now you've got to read disclose everything and wait another three days, and that punched a hole in that problem, but it gave borrowers another reason to be upset. The point of it all is is we're not lending against cars, we're lending against a very expensive very massive asset, and it's not the lender's money that there's given away right at somebody else's money. It's a public system that's never going to be like a car loan, but with software like what we've heard about today from Devin and with other blockchain applications. It'll get a lot better than this. Right, right. Thanks. That was great commentary. I'm so impatient at technology I press great people every time I'm going to a star like why do you have to do this, why do you have to do this, try for me can't do this. I don't understand this.  I went into my Verizon. I was just in the office and I was just setting the phone up like why can't he pay through crypto. I don't get it. Yeah, but you must be a tech company. Why don't you have a black chain. There's rise of a black chain. What's a black chain. And I just, what's with you people you want. I can give you a credit card.   Yeah, exactly. You're a great advocate for technology. May I, may I add something else because Rick was saying that it's a risk averse industry. I also would say it's a lazy industry in the sense that usually they, unless the, unless fanny or Freddie push or bring up bring out anything that says that they should do this. They tend to just keep on doing things the same way. And then they're not so prone to innovate. So, only if they're forced to move because in during the pandemic during COVID, they were they had to update how they were closing. They could have done that way before because the technology existed way before but they never did it. They didn't do it until they were forced to because people couldn't go to sign. So if they couldn't go to sign they couldn't close business. And only then did they implement that part and now they're working with it but they would never have done it before because no one was forcing them to do it. There's also a bit of that in the mix right. That it's an industry. It's not the regulations. I think it's, it's, they use that sometimes they use that as an excuse. I think they don't, they have an aversion to innovate also. This is going to be maybe a first for me. I'm usually not very sensitive, Miriam. I think we're of like minds of this I'd probably use a different word or a different phrase I'd say that the mortgage industry is filled with fast followers, not necessarily and I can't, I can't take credit it was a former boss of mine who said described as firm as as fast and I think the mortgage industry by and large with the exception of a few are fast followers and because they are risk averse. They really don't want to do anything that Fannie and Freddie or all the GSBs haven't proved as of yet right. It's, it's very risky to do that.  Yeah, yeah. I'm just trying to be nice. I think those are some great points and everyone this, this was a great discussion I think those of us and I think it's almost all of us that have been in the industry for a while we understand that that challenges of working within the mortgage industry and that's why we're here hopefully that we can help change the industry and make some money from it so I want to get back to you Devin was there anything else you wanted to cover on the presentation. Okay. Were there any other questions for Devin or from AI or to perspective. Okay, I want to go ahead and because we just have a couple minutes left. We're going to talk about what we're going to try and cover in our next in our July meeting and I'm trying to get to the right slide excuse me in our July meeting we're going to have a, another company come in, and this is going to be I'm trying to share the screen here. So we're going to have, excuse me while it's loading, Sanjay Kumar Nishant he's the chief operating officer from in pain, and in pain provides a blockchain platform using hyper ledger fabric that brings the different parties together on to a blockchain, a single blockchain platform. And let me get to a brief description really quickly here. So in pain is a blockchain enabled structured finance platform that gets lenders issuers investors and other parties, like services trustees and rating agencies onto a shared data platform for these transactions that uses artificial intelligence and blockchain for a seamless flow of data with zero reconciliation, complete transparency and provenance of asset and data. So, you know that that's a big claim. I've spoken with Sunday a little bit. It sounds like they have a real interesting solution so hopefully people on this call can join us for that one as we continue to delve further into AI and blockchain. And with that we're at the top of our hour but does anyone have any other questions or comments before we all sign off. David, we get your slide back. Absolutely. Thank you. Presentation today. Thanks for coming and sharing with the group. Thanks for having me Marvin we appreciate I appreciate the invite and everybody for that extend that forever one of true as well. Yeah, that that thank you Devin this meeting will be recorded Jeff and it will be posted on to the hyper ledger wiki. So all of that information will be there and will post his, his, excuse me, Devin's presentation there as well. Well, it's actually my wedding anniversary today so I snuck in here for half an hour while she's out 200 hours. Okay. It's not that that joking. I better get back. Okay, well happy anniversary. Thank you Jeff. And thanks everyone for attending I appreciate it. Thank you. Thank you Devin it was great presentation.