Hi everyone, this is Chih-chou. Welcome to my channel and welcome to another live stream. Today, today, we're going to continue our reading of the Iran-Contra scandal trading cards put out by Clips Comics in 19... what was the date on this? 19... 19... that's not the one. 19... 19... where's the date? Oh, it doesn't have the date here. Hold on. We'll take a look at this. 1988. 1988. The Iran contract. 1988. Okay. So we're going to continue our reading. We read cards number one to twelve here. Okay. In the last stream, last week. And we're going to continue the reading and we're going to read cards number 13 to 24. Okay. And these cards came out of this box which I have open, right? And when I cracked open this box, I realized a little insert that's usually in the box in all the trading cards that we've had, we've done the reading for from Eclipse Commons because they put out a whole bunch of political trading cards and some music and sports. The insert wasn't in there. So I grabbed another deck that I have. I believe I have three decks of these. Okay, if I recall correctly, I've been buying a whole bunch lately. Eclipse Comics trading cards and I've sort of losing track of the handful that I have or the few that I have. So I checked this one and it has the insert there. So I want to take a look at this insert. That way we've read the insert of all the cards. Okay, and then we'll start reading these. And this insert doesn't look like it has anything in the back of it, right? Usually all the inserts so far have had a front and a back with a little intro of what these cards are about. And these ones are one of the earlier sets that Eclipse Comics put out. I'm not sure which one would be the earliest. I think 1988 was the earliest. So this could have been the first set they ever put out. I actually have to look into that. Because a lot of the other cards that we're reading have been 89, 92, 93, and around their early 90s. So maybe they upgraded it because they were putting out more cards and they had a list of all the different cards they had. So let's have a read through this. The insert for this. Other current event items from Eclipse Books, PO Box, 1099 Forestville, California, not 540. 836, Brought to Light, 895, an 80-page full-color graphic documentary that reveals 30 years of covert action. Wow, drug-running-in-arms deals that robbed America and betrayed the Constitution based on the Christic Institute's explosive investigation. It's like a television documentary you carry around in your pocket and without the usual network censorship. Whoa, Jonathan Marshall, the Iran-Contra connection. What? Okay, this is the first time we're seeing this bit of info. I don't remember seeing this one. Brought to Light, full color graphic documentary. I need to remember to remember to maybe track that down. El Salvador, a house divided, a 40-page factual black-and-white comic book. Real War Stories, a 48-page color comic book about the military. $2. Now, just to give you guys a recap, we found out about all of the Eclipse Comics trading cards from the Real War Stories number two that we read and in the back they had an advertisement for the JFK cards and I don't know if the drug war trading cards were in there, too. They had the JFK cards and something else and we were like, whoa, what's this?  All these comics, all these cars where I've spent over a thousand dollars US getting our hands on some of these cards and where we've put out a few hours of reading cards and we're going to continue to do so, okay? And it was a great comic book reading, by the way, great comic book reading. It actually, I believe, also had the general Wesley Snipes War is a Racket, right? Farewell to the Gipper. 160 pages of biting cartoons by Dan O'Neill. Bush League Trading Cards, cool. We got that as well. We got the Bush League Trading Cards. Run to the Core, New York City Political Scandal Trading Cards. We have those as well and they came out in 1989, I believe, and this is 88, so this is advertising something that's coming out next year, I guess, and Run to the Core has the rookie card for Donald Trump that we're reading for. Friendly Dictators Trading Cards. We have those as well. Additional Sets of Iran Contra Scandal Trading Cards. Nice, nice. Please add $1.50 postage per order, total, and close-blop. Now, if you were a collectible investor type of person, this price right here, Run to the Core, Run to the Core, New York City's Political Scandal Trading Cards for $9.95, would have been an amazing investment. One of the best investments you could have made in your life if you bought these in 1988, 1989, because with the Donald Trump rookie card graded at $10, that's selling for around $1,000 U.S., graded at $9, selling anywhere between on the cheap side, $400 on the expensive type to around $6.50 U.S., and the odds are it's gonna go higher. Okay, so that's the insert for the Iran Contra Trading Cards. Salutations to everyone that's joining us right now. We've got Gulen, SensorTube, Eric, Shostead on SensorTube. So I'm gonna put this guy aside there. I'm gonna take the box we have here and put it over here as well, because we're gonna put the cars back where they belong, and we've got a bunch of people that joined us on Twitch as well, ZOM01. Thank you very much for redeeming the points, and I'm gonna get back into reading these cards, or get into reading these cards. Okay, gang? And I will most likely hold off uploading the individual cards until we've done all the readings, and then because I'm sort of editing these on the side and loading them up, but I still have them as private, and we'll make them go public, each card, all the sets back to back, so it'll probably take us about two weeks to upload all of these cards, all of these cards as individual readings, and as a three-sets that we're going to do. Okay? Now that we've got our little recap, intro, reading the insert done, let's get into continuing reading the individual cards. Card number 13. Oliver North. Oliver North. The antagonist, I guess you could call him. Right. Of the Iran Contra affair. The Patsy, maybe. The main note that everything went through, or almost everything went through. Right. Card number 13. Marine Lieutenant Colonel Oliver North. Marine Lieutenant Colonel Oliver North. The Vietnam veteran and Naval War college graduate joined the National Security Council staff at the White House in 1981, self-motivated and religiously anti-communist. North fit right in to the increased operational role envisioned for the NSC by the Reagan team. When Robert Bud McFarland became National Security Advisor in late 1983, C-card number 28, he handed North the Contra account, passing on instructions from President Reagan to keep the Contras alive, quote, in body and in soul, and quote. North went to great lengths to perform this, his duties, according to Noriega's aid, Jose Blando North, Jose Blando North had two meetings with Panama's strongman Manuel Noriega in 1985, even though Washington knew Noriega was involved in the international drug trade. Blandin alleges North pledged to help obtain U.S. aid to ease Panama's foreign debt crisis and lined up a PR firm called IBC C-card number 15 to improve Noriega's sagging public image. In return, Noriega promised to help in setting up two contra-training camps in Panama. A look at Albert Hakim's records, C-card number 20, shatters North's public image as a selfless patriot. Of 16.1 million in profits from missile sales to Iran, only 3.8 million have found its way to the Contras. Although he had less expensive arms suppliers, North steered most contra-business to the enterprise at inflated prices, North himself skimmed at least $20,000 in contra-fund for his personal use, and in 1988 he was indicted for defrauding the U.S. government. And again we're going to read the little bottom text, Iran Contra scandal, trading cars, text, 1988 copyright, 1988, Paul Brancato, art copyright, 1988, Salim Yucca, Eclipse Enterprises, PO Box 1099, Forestville, California, 95436. Oliver North. Look at those puppy eyes, right? And I wonder if this is all the North's rookie card. Would be cool, we should look into it. C-card number 14, C-card number 14, Robert Owen, Money Man, Robert Owen, Robert Owen, look at the mafia hat. State Department Contractor, Robert Owen, Robert Orm, former legislative aide to Indiana Senator, now Republican Vice Presidential candidate, Dan Quill, was recruited by N-C-N-S-C staffer, Oliver North, CIA Hand, John Hall, whom he had first met in Quill's office in 1983, and Contra leader Adolfo Calero, C-card number 13, 12, and 4. For the crucial role of liaison between individuals who were aiding the Contras, once on the Contras FDN payroll, Owen, known as the Courier, took up his new job as North bagman with zeal, acting as North eyes and ears, who reported on the military and financial needs of the Contras and their helpers, and delivered money, travelers checks, maps, encryption devices, and instructions to them. In 1985, with help from Calero and North, Owen's new company, IDEA, which he operated out of his home, was given a $50,000 State Department contract to deliver humanitarian aid to the Contras. Through Owen's influence, some of this State Department money was awarded to Air Match, a covert Pentagon operation, and some to Ocean Hunter, a Mendelian cocaine cartel operation with ties to the CIA's John Hall, C-cards 10, 11, and 12. Despite the fact that his patriotic zeal had entangled him with international drug traffickers and criminals, Owen expressed deep regard for Oliver North, the man who had recruited him. This was evidenced by his reading to the Iran Contra Committee from a poem written by John Hall, quote, the knowledge that on this troubled earth, there still walks men like all the North. In our lifetime, you have given us a legend, end quote, Robert Owen, Robert Owen, a bunch of fanatics, a poem about this guy. Let's read these two things together. Oops, let's read these things, these words together. Okay, what do we got? What do we got? While we look at Ollie North's face, the puppy eyes looking gleefully into the sky, right, quote, the knowledge that on this troubled earth, there still walk men like Ollie North. In our lifetime, you have given us a legend, a legend indeed, a legend indeed. Card number 15, card number 15. Let's check this out. In EPL, Presidents Contrathon, Contrathon was special guest Lieutenant Colonel Oliver North, Carl Spitz channel, card number 15, Contra fundraiser, Carl Spitz channel. In 1985, Carl Spitz channel, Chanel channel, and his associate Richard Miller began to raise money for the Contras from wealthy American conservatives. For this purpose, they created a network of tax exempt foundations with innocent sounding names such as the National Endowment for the Preservation of Liberty and EPL and International Business Communication, IBC, the standard fund raising session, the one two punch began with a slide presentation by Oliver North, which portrayed the Contras as virtuous, but poorly equipped freedom fighters, battling the godless Sandinistas Marxist Leninists. Then Chanel would conclude the event by detailing the specific needs of the Contras, including military needs, and would arrange White House briefings for contributors who sometimes got to meet the President if the donation was large enough. Chanel and Miller were not very efficient fundraisers. Large amounts of money were used to lease expensive offices, rent limousines, throw lavish parties, and pay high salaries to themselves and others. Of the 10 million dollars they raised, only 4.7 million went to the Contras. One million was used for pro-Contra publicity and advertisements in Contra aid swing vote districts. In 1987, Chanel and Miller were convicted of defrauding the IRS, using tax-exempt monies for illegal contributions of lethal, lethal aid to the Contras. Some of the peons getting thrown to the wolves. Carl Spitz. Carl Spitz. Let's look at Ollie's face. Look at this. Look at that big smile on the gap in his teeth. I bet he could whistle like that. That Heligoth posting that this dude died in 1990. Two years after these cars came out. Very cool. Very cool. I wonder if we got into a little plane and was flying somewhere. Card number 16. Card number 16. Joseph Coors. Joseph Coors. Adults. Joseph Coors. This is a Coors people. Beer magnet. Joseph Coors. Joseph Coors, president of Coors Brewing Company, is a member of Reagan's kitchen cabinet. Having joined that body in 1980, an enthusiastic anti-communist, Coors was a keystone of the Contra support system put together by the White House. His donation of $65,000 to NEPL, C-card number 15, used to purchase a small plane, was but a fraction of the overall support he gave to the Contra cause. Profits from the sale of Coors beer have funded many of the non-governmental institutions that promoted and sustained the Contra war. These include the Heritage Foundation, a right-wing think tank, Citizens for America, a grassroots lobby, and John Sing Labs, U.S. Council for World Freedom, the American chapter of WACLCard number 7. The kitchen cabinet is a group of millionaires who have financed the Ronald Reagan's political career from the governorship of California to the White House. This group has profited greatly from Reagan's pro-business policies. Its members include Holmes, Tuttle of Ford and Rexell, A.C. Rubble, Chairman of Union Oil, Henry Salvatore Oil Developer, Justin Dart of Darkcraft, Leonard Firestone of Firestone Tyron Rubber, Taft Schlupper of MCA Inc., and William French Smith, Reagan's former Attorney General. Other wealthy conservatives who contributed to the Contra contrasts through NEPL include Barbara Nyungton, $2,865,025, Ellen Garwood, $2,518,135 and Nelson Bunker Hunt, $484,500. The Coors Company. Who is this dude? Adolf Cole. Coors. Joseph Coors. Joseph Coors. Cart number 17. King Fahad. Oh, the Saudis are in. Amacontra 2. King Fahad. I believe it's Saudi, if I'm thinking about the right person. Yeah, that's him. Saudi Arabian monarch, Fahad bin Abt Aziz. Saudi Arabia's King Fahad is one of 45 sons of Ibn Saud who founded the modern state of Saudi Arabia in 1932. The Bedouin king was the largest single contributor to the Contras. Congressional investigators traced the total of 33.6 million that passed through Adolf Calero's FDN bank account. Of that amount, 32 million was received from Saudi Arabia. It was a small price to pay for continued U.S. military support for his regime. Fahad gave many times that amount to the Afghan Mujahideen, another U.S.-backed anti-communist force. The man who opened the doors to the Mullahs was General Richard Seacourt. Seacourt number 19. As the head of the Pentagon's foreign military sales program from 1978 to 1981, Seacourt oversaw sales of billions of dollars in military equipment to the nations of the Middle East. In 1981, Seacourt and Oliver North, Seacourt number 13 successfully lobbied Congress to support the 8.5 billionth sale of AWACS surveillance planes to Saudi Arabia over the stiff objection of the Israeli lobby, thus assuring King Fahad's support of the Contras. King Fahad is the world's second richest monarch with a personal fortune estimated at 20 billion. The world's wealthiest monarch is the Sultan of Brunei, a tiny oil-rich country near Brunei. Although his personal wealth is estimated at 25 billion, the Sultan gave the Contras a mere 10 million. Saudi Arabia steps in. Elliott Abrams. Elliott Abrams, card number 18. Card number 18. Elliott Abrams. Shult in the background. Assistant Secretary of State. Elliott Abrams, card number 18. Elliott Abrams, Assistant Secretary of State for Inter-American Affairs, was previously in charge of the Reagan Administration's Human Rights Program. In testimony before Congressional Committee, he implied that support for the Contra war against Nicaragua was part of the Administration's Human Rights policies. Abrams has lied to Congress repeatedly. He misrepresented his role in soliciting a contribution for the Contras from the Sultan of Brunei, C-card number 17, when the Hassan Fuss Flight was shot down, C-card number 29. Abrams testified to a House subcommittee that the US government was not involved in anyway, and later he helped concoct a story laying the blame on General John Singla, C-card number 7. A few months prior to this, he had helped win 20 million dollars in emergency aid to Honduras by greatly exaggerating claims of Nicaraguan incursions into Honduran territory. Abrams actions form a pattern of disinformation that characterizes the Reagan Administration as a whole and George Schultz's State Department in particular. From the beginning, the Administration lied about its intentions in Nicaragua, claiming that his covert actions were intended to cut off arms shipments from Nicaragua to El Salvador, when in fact they were creating an invasion force. Abrams went on to lead the Administration's highly publicized but unsuccessful drive to remove Panama's drug dealing general Manuel Nicaragua from power. Elliot Abrams. Card number 19. Richard C-Cord. USA. Richard C-Cord. Look at that salute. Let's get that salute nicely focused. We'll get him like this. Card number 19. Retired Air Force Major General Richard C-Cord. Retired Air Force Major General Richard V C-Cord's specialty is aerial logistics. He learned his trade in Southern Asia from 1963 to 1968, flying over 285 combat missions. As the airwind commander for the CIA's secret war in Laos, he was in charge of all tactical air operations. In 1983, C-Cord resigned under a cloud because of his involvement with Thomas Klein's C-Cord number 22 and Edwin Wilson, the renegade CIA officer who was convicted of selling arms to Libya. Soon C-Cord teamed up with his old friend Albert Hakim, C-Cord number 20 and started the enterprise, which was part business empire, arms deals, and part military and security operations, covert welfare and intelligence. C-Cord began working for Oliver North, C-Cord number 13, and the National Security Council 1984 at the behest of CIA Director William Casey, C-Cord number 5. Casey called the general a man who got things done. Within two years, the enterprise had five airplanes, two pilots, pilots on contract, two airfields, a boat, a stockpile of guns and military equipment, and numerous shell companies and secret bank accounts, all for the purpose of conducting covert military action for the NSC. The enterprise realized the profit of $16,100,000 on the sale of missiles to Ayatollahomeini. Out of this, C-Cord, Hakim, and Klein's each took an equal share of $2.2 million as their personal cut. In 1988, C-Cord was indicted for defrauding the U.S. governments. Iran kicks in here with weapons, right? Richard C. Gord. That's a $2.2 million salute right there. Thank you very much. And the planes and the guns and the airfields and the party. C-Cord number 20. Look at this guy. Albert Hakim with his love-on tattoo for Ali. Look at that. On his little belly button. Albert Hakim. Look at him. She innocent-looking face. That too. Isn't that cute? Who is he? C-Cord number 20. Arms merchants. Albert Hakim. In 1978, Iranian expatriate businessman Albert Hakim obtained contracts from Bakhtal Corporation, a large defense contractor then headed by George Saltz and Casper Weinberger. C-Cord number 6. The same year, Theodore Shackley, C-Cord number 24, introduced Hakim to Richard C. Gord. C-Cord number 19 in Iran. C-Cord was then in charge of U.S. arms sales to the Shah and Hakim had contacts in the Iranian military and in the Shah's secret police, Savak. The huge desperate disparity between what the Pentagon charged and what the Iranians were willing to pay enabled Hakim and allegedly C-Cord to make fortunes by skimming profits of these arms sales. After C-Cord resigned from the military, Hakim became his partner. He was the financial wizard behind the enterprise, a nongovernmental profit-making operation which aided the Reagan administration's covert foreign policies. The tangled web of shell companies and offshore bank accounts that Hakim created and controlled for the enterprise was designed as C-Cord admitted to confuse anyone who might start poking around. For instance, when congressional investigators poked into the B for belly button account, Hakim explained that he had set up his $200,000 trust for Oliver North's family out of respect to North whose radiation of patriotic love immediately penetrated to my system. This is inconsistent with Hakim's admission that he was only in it for the money. In 1988, Albert Hakim was indicted for defrauding the US government. Card number 21, The Enterprise Richard Gad and Robert Dutton, The Enterprise or Enterprise. Card number 21, Career Air Force Officers Richard Gad and Robert Dutton. Air Force Lieutenant Colonel Richard Gad retired in September 1982 after 20 years of service in covert operations. A career transport pilot, Gad had recently served as liaison between Joint Chiefs of Staff and the Joint Special Operations Command at Fort Bragg, home of unconventional warfare. Gad had learned from friends in the Army Special Operations Division, C-Cord number six, that the Army needed the black commercial transport ability. Passing up a promotion to full colonel, Gad retired and formed Samarko, which specialized in providing the Pentagon and CIA with clandestine air transport. He arranged to rent planes and hire pirates through Southern Air Transport, a CIA proprietary when Richard C-Cord's Enterprise began his Contra resupply operation in 1985. C-Cord number 20, Second C-Cord, turned to Gad to manage daily logistics. Gad supervised the construction of the secret Santa Elena airstrip in Costa Rica, C-Cord number 23. In early 1986, C-Cord, citing the five P's, pissed poor performers for no prior planning, replaced Gad with retired Air Force Colonel Robert Dutton, Gad's former superior officer. Dutton has served under C-Cord in Iran, selling weapons to the Shah and again in 1980, Iran hostage rescue mission. A cable sent by Dutton from Central America to all over North provided a lighter, lighter moment during the Iran Contra hearing, quote, sent font, can't survive on milk and cookies, end quotes. C-Cord number 22, Thomas Klein. Thomas Klein, ex CIA agent and arms merchant, Thomas Klein, card number 22. Thomas Gregor Klein's first met Richard C-Cord, C-Cord number 19, during the secret war in Laos. Klein's, as CIA base chief and long tang, supervised the creation of a secret army of Hamung tribesmen to fight the Communist Pathet Lao, Lao forces. The Hamung were traditionally opium growers with the CIA's help. General Wang Pao, leader of the Hung army, became a major supplier of opium. C-Cord's air wing flew tactical raids against the Pathet Lao, Lao, which also had the effect of destroying Von Pao's competition. Air America, the CIA's airline, transported the opium from Laos to Thailand. The CIA's secret Laotian operations were partially financed by opium profits. Klein's and C-Cord's team, up again in the Middle East during the mid-1970s, the Pentagon awarded Klein's company EATSCO, a contract worth over one billion dollars to chip arms to Egypt. According to renegade CIA agent Edwin Wilson, C-Cord who had helped to secure the contract was also a silent partner in EATSCO. As with Theodore Shackley, C-Cord number 24, this blatant conflict of interest led to C-Cord's early retirement from the military. Klein's pleaded guilty in 1983 to overcharging the Pentagon eight million dollars for which he paid fines of one hundred and ten thousand dollars. In 1984, C-Cord brought Klein's into the enterprise, C-Cord's number 1920, an excerpt in the small weapons and expert in the small weapons market. Klein's brokered the enterprise's European deals involving purchases and transportation of arms for the Contras. C-Cord number 22, Thomas Klein's and Haluk God's posting that Klein's was the only Iran Contra defendant to have served a prison sentence. That's why he doesn't look too happy maybe. The belly button dude didn't serve any time I guess. C-Cord number 23 was this guy, Rafael Chichi Quintero. Rafael Chichi Quintero. Is he Cuban? Is that Castro though? I think he's not a chicho. He's a chichi. C-I veteran. Rafael Chichi Quintero, C-Cord number 23. Rafael Chichi Quintero, C-I veteran once said that if he were granted immunity and compelled to testify about past actions, it would be the biggest scandal ever to hit the United States. An explosives expert and sniper, Quintero got his early training as a member of the C-IA's Operation 40 assassination unit, also called the shooter team. This elite group, born of secret collaboration between agents of the C-IA, the mafia, and Richard Nixon, was formed to assassinate Fidel and Raul Castro and Che Guevara. Led for many years by E. Howard Hunt, the shooter team was part of an advance unit that was left stranded in Cuba after the failed Bay of Pigs invasion in 1961. Quintero escaped from Cuba and returned to Miami, where he began working under then C-IA agent, Thomas Clines, C-Card number 22. This work included engaging in C-IA covert operations in southern Asia, Iran, and Central America. After Clines joined the enterprise, C-Card number 19, Rafael Quintero handled the offloading and distribution of weapons shipments to the Contras under Clines' direction. When John Hall's wrench, C-Card number 12, began to attract attention from Costa Rican authorities, the enterprise built a new secret airstrip at Santa Alina, Costa Rica, with help from the State Department's Elliot Abrams, C-Card number 18, Robert Owens, C-Card number 14, and Louis Thames, then U.S. Ambassador to Costa Rica, Quintero, whom C-Cord called my man, managed supply operations from this new airstrip, which was used for the shipment of guns and drugs. C-Card number 24, Theodore Shackley, looks like an erasure head from Dick Tracy comics. Theodore Shackley, former C-I-A agent, Theodore Shackley, C-Card number 24, known as the Blonde Ghost. Theodore G. Shackley played a central role in the formation of the secret team. After the abortive Bay of Pigs invasion of Cuba in 1961, Shackley became C-I-A station chief in Miami. There, he directed operation mongus conducting sabotage and assassination raids on Cuba. His deputy, Thomas Klein, C-Card number 22, helped supervise E. Howard Hunt's shooter team. C-Card number 23, From 1966 to 1968, Shackley was station chief in Laos, where he directed Richard C. Cord's air wing, C-Card number 20. Shackley and Klein's were transferred to Saigon in 1969, where for four years, Shackley directed Operation Phoenix. During those years, the program designed to neutralize communist sympathizers claimed the lives of 40,000 civilians. One of Shackley's subordinates at this time was Donald Greg, C-Card number 31. Shackley's next to last C-I-A post was as commander of worldwide covert operations, working under George Bush from 1976 to 1977, C-Card number 32. Theodore Shackley retired from the C-I-A in 1979, and he served on the transition team, which planned Ronald Reagan's entry into the White House. His involvement in covert operations did not end with his retirement, however. Having previously introduced Richard C. Cord to Albert Hakim, C-Card number 19 and 20, he served as a consultant to the enterprise until its apparent demise. In 1984, he took part in the Manutra Gorbenfar plan to trade arms for hostage C-I-A agent William Buckley, C-Card number 25 and 26. Recently, Shackley has been a lecturer on unconventional warfare. 40,000 civilians. 40,000 civilians. Let's look at his eyes. 40,000 civilians murdered.  Theodore Shackley. That's C-Card number 20. Gang. That's 24 cards. We got 12 cards to read in the next live stream of the Iran Contra scandal. Crazy informative. As I mentioned before, you could probably do a bachelor's thesis on one or two of these and do a master's thesis or a bachelor's thesis on one of these cards, master's thesis on two or three of them, and a PhD on this whole deck. We'll continue the reading most likely next week. The Iran Contra scandal trading cards. The secret team. That's where the secret team's coming in.


Hello and welcome back to the craft box. My name's Brinton today. We are here showcasing Bonnie Anne today So yeah, um Use I said that I would usually go into like the first bit of where we get it But I thought it was fitting to go to Molly bone and fight some of her own Molly bone music is lit So yeah, Bonnie Anne is musketeer She She Has three promotions one at level eight ish one at like level 30 and then one at like level 50 ish So yeah, that's I think that's how many I think She's got the same as like rat bed and stuff. So it's I think it's three And she gets all the way up to Fox Highlander She's level 70 at the moment for me And she's musketeer if I haven't said that already she's amazing and on her last Watch nice But she is actually really good. I need to start using her on my musketeer. I feel I need to She is actually a god she is really good every school can get her She's the second companion you get after your companion you get in the tutorial She is amazing and she's also a fox. So like what could you not love about her, you know So yeah, she in her last companion. She got stalker salt, which is basically Which is basically kind of like a Malay attack, which is kind of weird for her, but it's cool at the same time And if any Malay person she blocks she gets to do that as well, which is really cool. So yeah But there's also downside because she doesn't block much, but you know, yeah, there's Kampo go Kampo Kampo OP So, yeah, that's that's Bonnie and we'll just finish off this battle and then Yeah, overall, I really like Bonnie and she's been in my main lineup on my witch doctor for all the way through the game except during Valencia part two where I used old scratch car curious and Momo I'll have showcases on them as well later but yeah, so That that's Bonnie and she's amazing Really good companion I would highly recommend to use it on every single class because she is actually crazy We're gonna show off stalker salt. Actually you can show off stalker salt on that guy Sure Let's see a stock of short assault. I haven't seen them in a while actually Just kidding JK. I killed them all Okay, that's unlucky, but yeah, so that's Bonnie and I hope you enjoyed just a small way video on a Wednesday You know how it is If you if you leave suggestions down in the comments below of what companion I should look at next, please do that But anyway, thank you for watching Like and subscribe and if you've already subscribed press we little bell to know exactly when I post another video and remember craft outside the box


Thank you everybody for joining our talk today. And in fact, you have so many choices. And so we are very extra thankful for you having chosen us. And I hope it will be a very productive one for you, like that what we'll be talking about will be helpful in what you're looking for in the kind of direction of JNAI stream processors, all of these things that are happening. So our talk is about build a chat GPT data pipeline with rising wave stream processor and Cassandra factor search. It's a bit of a exploratory kind of talk to us. You know, there isn't any true integration yet, right? And then we may be doing some like building our connectors at some point, but the fact is having, you know, being working with data is actually very, very nice because things are loosely coupled. You can actually make kind of connections to it too. Anyway, so this is us and is it good? Yeah. Yes. Okay. So first of all, this is an agenda. Just run down on what we're gonna be talking about and who are we, and then just brief an overview, just assuming not everybody already know, you know, will work with JNAI chat GPT, which is kind of so popular these days. And so we just kind of give a brief introduction overview. Then we'll get into introducing rising wave and then extra DB vector are actually more like it's powered by Cassandra. So, and then we'll talk about this building a chat GPT data pipeline, giving an architectural conceptual walkthrough. And that should take up like the next 30 minutes or less now. So, okay, so quick intro of the speakers of us. Let me have Corinne introduce. Okay. Hi. Can you guys hear an echo? Do you hear us okay? All right. Okay, cool. So I'm Kareen. Let me see what it says on my slide. So I can like act. I'm like, who am I? I don't know. So I'm a consultant. I do developer relations related kind of stuff, community, endeavor, all type of stuff. My background is actually, I used to work in entertainment and music before I worked in tech, which is an interesting transition. And like, yeah, that's it. I don't know if it's that interesting. That's like, that's my life story. I mean, I'm sure it's more interesting than that, but. Great. Yeah. Okay. It's my turn. Yes. It's your turn. Okay. It's just that I realized I make sure I turn off. See, look at how you even have, she even has like her jumping with a sword, you know, like how am I supposed to compete with that? No, this is Mary jumping with a sword. Okay. So this is a quick slide about me. And rather than talking about it all, I just, you know, have pictures to represent. And I'm a senior developer advocate at data stacks. As we all know, data stacks is the primary sponsor of this conference. And thank you so much again for coming. And I'm a passionate advocate that you see me everywhere. Everywhere. Anyway, I really enjoy like outreach to the developer community, especially. I'm also Java champion. Also I run the Chicago Java users group and other users group in Chicago too. So these are kind of things that I've been doing. And also prior to this, I was doing more actually event streaming, reactive systems at IBM, also advocacy and Java space too. So that's about me. And we'll share with you our contact information towards the end of this slide show. But my interests are really into streaming, distributed systems and now moving into AI, machine learning, which I'm embracing now. That's what our company is all about these days. Okay, so, but first of all, as I mentioned too, because it may be new, you know, for everybody, not everybody has worked with Gen AI, chat GPT. So let me step through it kind of quickly to kind of give an introduction to set the stage. So about the chat GPT, Gen AI, as we all know, it came out chat GPT specifically a year ago in November, right on November 30th, 2022. That's when it came out, open AI, publish it, release it. And it has taken the world by storm, as we all know. And I think it's because all of the things are kind of more like user in like kind of enabled, right? For doing things you can just kind of bring up your open chat GPT console, ask questions. And now of course there are a lot more chat bot kind of spring up. All of the companies are also doing same kind of thing. So, but ultimately too, what I wanted to talk about is that this is all about automation to kind of making our lives easier. And it won't by no means it would take away our job. And it's actually going to be, you know, kind of peaking on our creativity to create new ways of solving problems. But always remember the bot isn't going to take us over and we'll have more kind of interesting to do, interesting things to do and make us more creative in creating new opportunities too. So let's just really quickly step through like AI machine learning, just to get an understanding and this is like the more popular way of describing, you know, in this diagram of AI is mimicking the intelligence and, you know, high level wise mimicking our intelligence and behavior of human beings. And then if you kind of peel down this onion and you see machine learning, machine learning is really about us trying, you know, to train the computer to learn from the data. So then we don't even have to actively training it. It's basically by data, then they learn from the data and then keep kind of building up your model. However, it can't go too far without more deeper way of doing things, which kind of address the cognitive level. And now it comes in, you know, the core of the onion is really deep learning and it brings in, you know, the kind of the neural networks. How does it, how our brains actually work? And that's where like all the magic is happening. That's where you can find LLMs that we will be talking quickly about. The natural language processing all comes into that in the core of this onion. And let's just real quickly talk about just what is gen AI. Is a disruptive field in AI in the sense is that now it can actually take prompts that are spoken, that kind of can be spoken language that, you know, essentially is human, very humanly understandable language as input. These are prompts. So it has the potential way of changing the way we create and consume contents too. And it uses a combination of machine learning as well as deep learning to produce the contents. And it's very, very much very kind of a, you know, disruptive ways, a new way of doing things because assuming that a lot of you are engineers and so am I was I, right? And we learn right when we program what it used to be is that you need to give input that are very strict. You need to have strict rules of specifying the data type what kind of parameters and give all the parameters. It's kind of very, not like human kind of, you know, friendly. But that's the thing is a gen AI is that you can talk to it like you're talking to another person. And that's what is really kind of disruptive about it as well as, you know, it allows us to do a lot more creative things. Yeah, so before gen AI was actually predictive AI that actually deals more with business forecast, weather forecast, those things they are learning from some past data and they're making predictions based on that. And that's kind of less still kind of doing things the old way there isn't as much of this, you know, they're probably not much like human understandable way of talking to the bot. But the thing also is that with predictive AI is that the data will expire after a certain periods of time, if you have weather forecast for five days after five days, the data is gone. It doesn't apply anymore. But generative AI can have a lasting kind of effect too. So, okay, so that's that. And then GPT. So chat GPT deals more with the chat kind of text kind of involved with that in that sense, right? As input as prompts, but essentially too GPT is like it stands for generative pre-trained transformer GPT. So it essentially takes simple prompts, right? And human language format and that's input. And then it does what it does behind the scenes is a lot of pattern matching in there. And it's called like similarity searches. And then it answers questions for the prompts and produce contents that are creative now writing and essays, writing our code and then also like designing our dress and maybe writing a blog post, things like that is very creative. And now then let's get into NLP too. That's important because it's an interdisciplinary subfield of linguistics of computer science. So the concern is to be able to process natural language data sets, right? So that's what is enabling the LLMs which I'll talk about in a second. And it's kind of dealing with kind of figuring out what the language is trying to say and then do all the work that it needs. And it uses rule-based or probabilistic machine learning type of approaches. So it enables computer to actually learn. The actual learning is happening on this kind of level NLP. And it's kind of allowing you to draw insights, right? From the documents, for example, some input not just like strictly A equals A or B equals B it actually goes beyond and creates some context around it and so. And now it gets into like LLMs, large language models. There's a type of machine learning model. So these are foundational type of model. But typically though, it takes a lot of money to produce it because we're dealing with petabytes of data or even bigger, right? Like an unending kind of, oh, her mic is out. Just scream. Mary's being tested on her. Here, you wanna talk into my mic? Okay, no, no worries. Yeah, okay. So yeah, so essentially large language models is what we kind of developers mostly work with. We're working with LLMs and that's where kind of window into all of these magic that's behind the scenes doing all of the cognitive type of work. I'm gonna just give you this mic. Thank you. Okay, thanks. All right, so, okay, LLMs, that's it. And so this is just some examples of these API frameworks that I think most of you are familiar with the Leng Cheng, Lama 2, Palm and Hugging phase of these. Okay, so now then, let's kind of get into, I just wanted to point out to the, how this, you may be wondering, okay, our title of our talk is Data Pipeline. So we are kind of concerning with data, like processing data flowing through different kind of steps within your processing. So now, like with Korean, and Korean's working with Rising Wave. So yeah, let's have Korean introduce and then we'll talk about this test case. Okay, so I'm gonna talk a little bit about Rising Wave. This does assume that you guys know something a little bit about streaming in general, streaming data versus like batch and things like that. So stream processing is a data processing approach that deals with continuously flowing streaming data. So if you're familiar with streaming systems like Kafka or Pulsar or Kinesis, as events happen, you are bringing them into the system, stream processing basically allows you to run queries and processing and transformations on the data as you're bringing it in before you even store it. So that's like the basic kind of high level thing of what is stream processing. So it gives you the ability to do real-time analysis, transformation and computation of data as it arrives. You really can't get any more real-time than that unless you're predicting what's gonna happen in the events before they actually happen, which you can eventually do. But it is basically the data's coming in, you're running queries on it and then you're storing it kind of on the downside. So this is used to extract valuable insights, detect anomalies and trigger actions based on the incoming data. Okay, I'm talking really loud.  They're like, you are so loud. How about this, too far? Okay, now I have to like, I'm just gonna go like this and make it a little bit more exciting for you guys. Types of queries in stream processing. Okay, so the types of queries you could do in stream processing, one is filtering. This involves a selection of specific data from a stream based on predefined conditions. I put examples in here because sometimes it's hard to like apply and figure out how you can use these different kind of queries, but an example of this is like filtering out log entries that contain errors that are needed for immediate attention. Another one is aggregation. It involves summarizing or reducing the data over a certain time window or key. So an example of that is like calculating the average temperature over the last 10 minutes from like a sensor stream. Then joining streams. This is personally one of my favorites just because I think it's cool. You can literally do this with like dozens of streams. So you can combine data from multiple streams based on common keys and conditions and be able to do processing on those multiple streams. So an example of that is combining user click data with product data to analyze user behavior and continuing types of queries in stream processing. Windowing these queries group data into fixed time intervals or windows for analysis. An example of that is analyzing the total sales for each hour of the day over a week. That's like windowing. Pattern recognition is identifying specific sequences or patterns in the event stream, detecting a series of login failures as a security breach attempt. A lot of like anomaly detection and fraud detection kind of things fall under this as well. And then stateful processing maintains state information to analyze data over time such as tracking user sessions. An example also is monitoring user behavior and detecting when a user session is idle. Very important. For this specific case, I'm gonna talk about one kind of use case example. Oh, I didn't even, sorry, I'm going a little ahead of myself. So what is Rising Wave? I'm like, I don't see the next slide and I'm sure it was the next slide. So Rising Wave is an open source distributed SQL database used for stream processing. So it's designed to reduce the complexity and cost of building real-time applications. So if you tried to do stream processing in the past, it can be quite a large big build. It's not easy to like kind of get started and Rising Wave is meant to tackle that. Just being all SQL based, really easy to get started and use. So essentially what it does is it consumes streaming data. So events as they are happening are being brought into the system. It performs incremental computations when the new data comes in and then updates the results dynamically. I'll dig into this a little bit more just to like make sure it's all clear, but it also has a downstream side. So Rising Wave has a storage system. So if you're running transformations and queries on the data as you're streaming it in, you have the ability to store it as well to be able to access that data very quickly. It's built in Rust. I think this is cool. That's why I added it. I was like, it's cool, right? It's very performant and it's actually, the co-founder of Rising Wave happens to be here. So I'm like, oh my God, lots of pressure. So make sure you guys clap, okay? So, and it's also SQL based and PostgreSQL compatible. And this is great because if you know SQL, you can do stream processing. So like the learning curve is like pretty simple. And it's PostgreSQL compatible. So even like the downstream side, if you want to use other types of database, you can do that as well. Another cool thing about Rising Wave is the storage and the computation are separate. So you can scale them out independently of each other as well. And it also offers you the ability to do incremental updates on materialized views. Okay, so just kind of high level. So like what is Rising Wave? Basically on the left side of the screen, I'm like, I don't know if we're mirror to the same way, but I guess it is. Otherwise everything would be backwards for you guys, but like, so events are happening, these streams are coming in and you could pull in data from like a variety of sources, all listed here. You don't really need me to name them unless you want me to. You know Kafka, Pulsar, Kinesis, whatever you have the ability to stream them.  So as the events are coming in, you're bringing the data in in real time. You can join streams, as I kind of mentioned before, which I think is super cool and helpful for these types of use cases to be able to join streams. You can do filtering, aggregations, transformations. You're basically running queries in SQL on your data. And then it has the continually updated and queryable states. And then you have the ability to, the downstream it into a storage area, message crews, databases, warehouses and data lakes. Okay, so high level overview of rising wave architecture. If you have questions, you could also, again, the co-founder is like right over there. I'm not gonna point him out, but he's right there. So the rising wave architecture, so it includes these key components. So first there is a serving layer that basically parses the SQL queries and then performs planning and optimization of query jobs. Then you have a processing layer, which performs all the computation. And then you have a metadata management service, which manages the metadata of different nodes and then coordinates the operations amongst these nodes. And then you have a storage layer that stores and retrieves from object storage like S3. Okay, so example of some rising wave use cases. I'm gonna try to run through these because I know that there's still like a lot that we have to cover. Some examples is streaming ETL, the practice of continuously extracting, transforming and moving data among different systems as the data comes in. So again, like the stream processing thing, like the big value of it is everything is in real time. Streams are coming in, you're running these queries and transformations on top of it before you're even storing it. Real-time analytics is the practicing of analyzing the data as it's generated or received rather than after the fact. I also just wanna touch on something. There's something like a thing called actionable insights where you can create basically things for your end users to be able to take action on based on like those real-time insights. And that's something that's really, really powerful that I think people need to keep in mind when they're thinking about real-time analytics. A lot of times we think about it for use internally or we think about dashboards and just everything, everything in real-time. But when you have any kind of real-time insights and you're providing that to your end users that can be really, really valuable. Then you have event-driven application, enables continuous event ingestion and processing, detects complex patterns and correlations with the stream of events. There is honestly like a ton of different kinds of use cases. There's actually two people here from Rising Wave. There's Ray's over here. He can also talk to you guys if you have questions about use cases. He knows a lot about this.  If you, and it's not the whole thing, so it's kind of kind of chopped up, but if you want to see the whole thing, there will be a QR code to be able to access the slides at the end. You'll be able to go and look at this. You can also just Google fast Twitter events, processing Rising Wave and you'll find it. Thank you. Okay. So this is an example of one of the use cases. This is basically your processing Twitter events and Twitter hashtags as they're coming in. So this is just how you build it out. So the first step would be launch a demo cluster. Again, there's a lot of things that are chopped up. I kind of just screenshotted pieces of each section. So if you want to see the whole thing, just go to that link. So you launch the demo cluster. Step two, you connect Rising Wave to the data streams whether you're using Kafka or Pulsar or whatever, multiple data streams if you want. And then you can define a materialized view and then analyze the data and then you query the results. So if you want to see the whole thing, you can just jump in there. And then I'm going to hand it over to Mary. Thank you. We are ready. Yeah, that's right. Oh, no problem. She's got a handheld, that's fancy.  Okay. So now it gets into vector search, right? We're kind of going back to talking about chat GPT, gen AI type of scenario. So what is vector database and why is it important, right? In the gen AI kind of space, right? So essentially two vector database is a purpose built database that serves up vector data type for complex machine learning purposes. And it relies on vector embeddings. You know, what is kind of making it possible is these embeddings. So kind of like a quick explanation is that if you normally, right, we're more probably trained to think about things in single dimension. So we're more used, actually more used to thinking of data as scalar type of data type, right? But then with gen AI, we need to figure out the context. So then when we do searches and storing of these data, we're not only storing that value, we're also storing more than that. So that's when data needs to be expressed in different forms. So these are like multi-dimensional data. They're vector data type. So if you're like a math person, you're aware of linear algebra. So think of that. And that's what is making the magic happens is all of these linear algebra matrix math, all of these things are making it possible. And then it also helps to do all of these similarity searches behind the scenes. So think of, you know, you have a string of data coming in, you do need to tokenize and break down your string. And then each word came out of that, we'll need to map to some numerical representation. So let's say in a database like Cassandra, and you know, Astra too, is that we actually will store the data as vector data type. And if you kind of do a selection, there will be arrays of, you know, an array of floating point numbers in it that are being stored that will represent the string that you're trying to store. So that's the idea. And then these are vector embeddings. And so they get stored. So it becomes very important that you want to select a database that have very fast and very stable fast kind of storage. So for example, like in our Astra Cassandra database, we have making use of storage attach indexing. That's a very, very, very fast too, kind of a way of doing things. And also as far as doing searches, it's concerned, that's also very important when you do queries. So we're using the approximate nearest neighbor kind of technique to kind of do selection of the data, of querying the data. So essentially too, it's a form of like machine learning, like in that space, right? When we're talking about, this is sort of like providing you with feature store, like a feature engineering type of purpose for a vector database. Okay, just real quickly too, what are vector embeddings being used for? It's not just like for, you know, you can use it outside of the context, exactly like Gen AI, but essentially too, it is very good because now you are able to store data in vector type of format. So it's good for doing searches, right? Kind of, you know, you rank all of the results by, you know, how close it is to the query string, for example, that these are called similarity searches. And then there's also clustering, and then recommendations kind of scenario. And say for example, you're doing like shopping cart, you know, at a shopping site, we want to be able to select some products that are close to what you're looking for, then it helps in doing recommendations, kind of test or use cases. As well as like anomaly detection, right? That's also kind of good to be used for. Diversity measurements, as well as like text, kind of classification for that purpose too. Okay, just a real, real kind of quick too, you know, for those of you who are math savvy, right? So this is what it looks like. And if you kind of represent your data, like in, you know, normalized vector kind of format, using like a two dimensional kind of space to describe it, you can now see then all of the words being represented as vector embeddings. And when you look at the graph, that's what they are kind of placed in, you know, in this kind of a space too. So it transformed the text into vector, they are called embeddings. And the thing is right now, you know, with open AI, the embeddings are 1,536 dimensions, right? There are certain dimensions to how much it can have. And essentially too, as you can see too, is that the version three will have more similar to version two, or actually a V1 is more similar to version two than version three. For example, if you kind of do searches, the results, that's how it's kind of determining how close your result is to what you're trying to search for. So here too, it's just another diagram that explains if you are searching for cat, dog and house, you know, to some searches. And that's what you can kind of look into the graph to see how close is your result to the search result. So you can use this kind of representation. Okay, and then one more. This one too, I'm just kind of wanting to say this gets a bit kind of deeper and show you all the layers of, you know, the searches too. And so this, I won't kind of get into all of the details here, but just to give you an idea. And also too, you know, it helps in storing to have it like be more efficient to storing it. And also you can, we also have built-in like algorithms to help with fast retrieval. So using like nearest neighbor type of embedding. So I want to point out JFactor, for example, is developed by our founder of the technical co-founder, Jonathan Ellis. So he did the JVactor and that's actually becoming very popular. It's really fast. And then make use of diskN, which is a Microsoft algorithms too that he's using for doing super fast kind of searches for vector type of embeddings. So, okay, so that's that. And then just kind of real quick, since I don't have as much time, just wanted to point out, right? You can still kind of use, go back to using traditional database to do this kind of gen AI things. But the thing though, you'd be aware of that. It's just not able to handle the complex data type that's required in gen AI type of multi-dimensional data sets, all of these things. And also too, if you want to do that, it's gonna take a long time, essentially. So it's not practical. Also, so just really quick kind of description of a data pipeline. How does it kind of brings data to the vector database, right? So normally too, you would read data from a source such as S3 bucket or a website or a Kafka topic. Then you process the data to then extract the text to vectorize it. Then, and then you will then split the text into chunks of a given size. And then you compute the vector embeddings for each chunk. And then you write the chunk to the vector database. Then after which you will clean up all of the obsolete data from the vector database. So that's how essentially, when you interact with the vector database, these are the steps that will be needed. Okay, so this actually, we only have three minutes left to just kind of give you an idea, it's more like exploring, right? Now rising wave, right? How does it work? And we feel that rising wave has this kind of strength, right, in doing some of these processing and Karin looks into this Twitter feed type of example. So let me kind of take a look. But let me also kind of point out too, for a chat GPT data pipeline kind of experimentation, we like to look at, you know, there, this is a diagram that explains, you know, you're working with large language model, we're dealing with the training phase and you know, essentially generate your LLMs and LLM is such, you know, it's limited as we all know is static, even though it deals with a lot of data, but the data could be come from a year ago. So what happens in between those kind of things, right? So if you kind of look at it, right, we use prompts to kind of prompt the bot to kind of search for things, what that we need to look for. So I'm thinking, well, using rising wave, for example, an example is using the Twitter feed, you want to search for some tags, they can be done very fast using that example that Corinne shows, you know, with rising wave. And then from there too, we can then kind of massage the data and form, kind of essentially massage the prompt and then essentially then send it to a chat bot to doing like searches that you need to. So that's kind of like the example we like to be sharing, although we just didn't have time to put it up and then maybe in the next phase we'll have this project actually up running too. But so essentially just a high level description of this data flow of the sample, you have Twitter feed and then send it to rising wave and are kind of like the feed coming from Kafka and then basically rising wave will extract maybe certain tags that you're looking for, maybe the top selected tags. And then from there we construct the chat prompts and then we'll send it over to the chat bot in which we're using extra database vector or Cassandra to kind of help manage the query and storage of these vector data too. So yeah, so I think we're pretty good in here but this, yeah we're done. But this is just another example just wanting to show you where maybe a vector data type or data, vector data and database can sit within like a generative AI like RAC application. It's a retrieval augmented generated application. So essentially kind of think of it like this thing can help with augmenting the LLM data. So LLM again, it's very limited in what it can hold. So okay, all right, good. All right, so I think it's good. God, we're done. Yeah, there's t-shirts in the back. Yeah, that's right. So wanting to kind of point out to you if you like some t-shirts, you can get some. Your data stacks t-shirts. Yeah, data stacks. And we have some, Karin, thank you. Karin put some t-shirts for us and also some information about our company, stickers at the back row. I love how she's giving me credit. She brought the t-shirts over here. I literally just put them on the chair. Thank you. So yeah, thank you. And then, okay, resources of course. And then if we run out of t-shirts, if you can't find your size, please visit our booth. Yeah, there's only medium and large. Yeah, that's right. So please visit our data stacks booth upstairs and there will be plenty of staff to help you and answer questions. And this slide deck if you are interested is here. You can reach that using this QR code and short billing. And then these are resources from data stacks. And also our free usage of Vector, our extra managed cloud platform, extra. So this is how you can get to it. And you get $25 US dollar per month for free tier usage. You get, it gets renewed every month too. So you get $300 per year. It's very good enough for personal projects. I kept it really simple. Honestly, you could just go to risingwave.com but you guys are in a fortunate situation where you have like the head of product for risingwave right there and then the CEO of risingwave right there. So if you have questions about use cases or anything related to risingwave, these are the guys to talk to. Okay, awesome. Okay, I also have a Twitch stream too. I promise to kind of get back to it in the new year. So this is my Twitch stream. If you want to follow me, I'll be doing more hands-on things on there. And if you want to participate.  Yeah, if you want to participate too, I invite you to join my stream too. That's how it works for me. Okay, so with that, we want to thank you again and this information of how you can get ahold of us and we invite you to stay in touch with us and LinkedIn, Twitter or X and then I also have a Discord channel and then Karin has her own consulting service. So if you need her help, yeah. Please reach out to her. Yeah, so. I feel like Mary needs like theme music when she comes out like you know the Alma Hustler song. Okay, but thank you. Thank you so much again for, I mean, stay with us. Thank you, thank you. Thank you.