Alright, I'm back here for another video, and today's video is going to be on why you can't make any money in the stock market. So riddle me this. Let me know if you've heard of this one before. It's you make money 10, 20, 30 days in a row, and that one day gets rid of all those days and more, right? That one loss ruins weeks and months or even years of work, right? How many times did that happen to you? What's happened to me all the time when I first got started trading, right? So today's video, I'm going to explain how to protect yourself, how to never blow up again, and how to actually keep the money in your pocket, because it's very easy to make money in the stock market. The hard part is keeping it in your pocket. So let me talk about what I first did when I first started trading, and then now some things that I've learned along the way after eight years of trading and seven million dollars later. And again, if you don't believe me, you can go to our website, mindvestingclub.com and check out my broker statements there. So when I first started trading and got to a really, really good level of trading, I would have a $35,000 account. And what I would do with that $35,000 account is I would grow it to $50,000. And after I grew it to $50,000, I would give myself a wire out of $15,000. And that would be my paycheck, right? So now when I got to that level, I was able to consistently grow my bank account, right? Because the more you grow your bank account, the more comfort you feel. The more you don't feel like you have to trade every single day. So that also helps in case of a bad day or bad blow up or bad something, at least all your money is not in your day trading account. So I highly recommend, you don't have to go $35,000 to $50,000. You could every week, you could just pull out 50% of your profits. If you made $600 this week, pull out $300 on Friday. If you made $2,000 this week, pull out $1,000 on Friday. That way, you give yourself a paycheck and you set yourself up with a rainy day fund in case something bad happens. So that's what I did when I first started. Now, what to do now is you need to set yourself up with risk parameters, okay? So I'm going to give you an example. If you're driving a car and you're going 150 miles an hour, are you going to do that without a seat belt and without an airbag? Probably not.  So I used to trade at S&B Capital, which is a prop firm in New York City. They had someone whose full-time job was a risk manager.  So if professional prop firms and professional hedge funds have a risk manager, us normal traders, we don't have access to a risk manager, but we do have access to broker tools. So what I would suggest doing is, number one, set a max loss auto liquidation. The auto liquidation is very, very important because number one is we want to make sure that when we're wrong, the system kicks us out because how many times have we been wrong? We're a deer in headlights. We freeze and we say, you know what, I'm just going to give it a second. You give it a second and it's even worse. And then you're like, wait, I'm just going to double down. You double down to try to fix your average. It gets even worse. Now you're at the point where it's like, I'm in too deep. Now I got to wait. I'm in too deep. I'm stuck.  It's happened to me when I first started. It's happened to everyone they first started. It's just part of the learning lessons of trading. So what can you do to protect yourself? You must set a max loss auto liquidation. So what that means is this. If you make $100 a day trading, set your max loss auto liquidation to $100. That means that if you lose $100, the system is going to immediately kick you out and you're not going to lose more than a day's worth of work. The way to do this is you call your broker, you message your broker and your broker usually has the ability to set this number. That is your seat belt to start. That is your seat belt. Now, what you also have to understand guys is the reason why we want to do more than we don't want to surpass more than one day's worth of work on the max loss auto liquidation is because you have to think that trading is a game of confidence and if you lose a month's worth of gains or a year's worth of gains, you're going to revenge trade on a totally crazy different level. I'm telling you, it's happened to me. The bigger hole you are in, the more you try to revenge trade. So if your hole is only a day's worth of work, you're going to make it back tomorrow. But if you set your max loss to a week's worth of work or a month's worth of work, you're going to revenge trade and it's going to domino and domino and domino. That's number one is set your max loss auto liquidation at your broker level today to no more than a day's worth of work. Now, additionally, what you want to do is you want to set yourself up with a max size on your account. So what this means is once you hit this maximum share size, you can no longer keep adding. So what ends up happening is let's say you have no max share size.  All of a sudden, you're in 10,000 shares, stock goes up 20 cents.  You're dead, right? So you must set a max share size. In my opinion, if you are just getting started trading, you should not use more than 500 shares and the way to know when it's time to raise your max share size is when you get more consistent. When you are consistently making money and consistently getting better, you could then improve your max share size. So to be honest, guys, if you just set your max loss auto liquidation at the broker level, you set yourself with a max size. That way you just can't keep adding and adding and adding and adding because what ends up happening is if you know you have a thousand shares, max size, you're not going to go in a thousand shares in one bullet. You're going to slowly scale in. You're going to slowly get it. You're going to tell your brain.  I can't add another 900. I have to be more meticulous. I got to be more careful because you ran out of bullets. If your gun has unlimited bullets, eventually you're just going to not even care. You're just going to spray it. You're going to spray it everywhere.  So long as you have your max loss auto liquidation set up at the broker level, as long as you have your max size set up at the broker level, I guarantee you will never, ever, ever, ever blow up your account again because you are going to be protected. How many times, I've been there so many times, I've been there so many times where you just let the loss balloon and balloon and balloon and balloon and before you know it, you're in too deep and you go crazy. If I knew that max loss auto liquidation, if I knew that max size existed, if you don't have first start trading eight years ago, I would have had millions and millions and millions and millions and more dollars. It took me years and years to learn this because no one taught me. No one taught me about these seat belts, about these airbags that existed for normal guys like us, right? And that's kind of why we started MIC as well, is to teach normal people these secrets, these strategies, this process, this routine that hedge funds and institutions are using guys. So if you want to learn more about our strategies, if you want to learn more about what we do, we're running an actually really nice promotion for Christmas. So if you go to myinvestingclub.com slash Christmas, you'll be able to see how we trade, how we do things, our watch list, our one-on-one phone calls, our trading videos, our education. This is your chance to get mentored and coached by multi-millionaire traders. We've had a total of 7,000 people join MIC. Some people it was perfect for them. Some people didn't want to learn. We've helped and coached and mentored thousands and thousands and thousands of people and we continue to mentor and continue to coach because this is our passion. This is our hobby. And I wish when I first started, I had someone to learn from, someone to teach me these at least the risk parameters to set for my account to make sure that I never blew up again. So if you've blown up your account or you've lost money on your account, leave a comment and let us know what you did to fix that problem, right? Because if you haven't fixed that problem, if you're still doing the same things you're doing, you're only a day away from blowing up, right?  So in my opinion, guys, this is the best time to learn, best time to be educated.  So I hope this helps. Please, please, please set your max loss, max size, auto liquidation from your broker today. And if your broker doesn't have that, it's time to switch your broker. So thank you, everyone. I'll see you in the next video.


All right. Good afternoon everyone. My name is Mayor Murrow Weinberger, and I want to thank you all for joining us here in Burlington's City Hall. The Saturday evening shooting of three young Palestinian college students visiting Burlington on their holiday break was one of the most shocking and disturbing events in this city's history. This horrific unprovoked attack was a tragic violation of the values and character of this welcoming, inclusive community. I'm grateful that as a result of a huge coordinated law enforcement response led throughout by the Burlington Police Department that the suspected shooter was off the streets in less than 24 hours and that we were able to announce the arrest of Jason Eaton just after midnight last night. So the Burlingtonians were able and relieved to go to school and work this morning knowing that the shooter was in custody. Mr. Eaton has now been arraigned and charged with triple attempted murder charges that carry the potential of for life sentences. This case remains the top priority of the Burlington Police Department. The investigation will continue as will our collaboration with state and federal partners to give our prosecutors the strongest case possible and to ensure that Mr. Eaton is held fully accountable for his actions. These law enforcement actions while outstanding will not erase the damage done by Saturday night's violence. It is incumbent on all of us to continue supporting the victims, their families, and their communities. We are honored to be joined at this press conference by Rich Price. Rich is a longtime Burlingtonian and he was hosting the three young men during their visit and he's also the uncle of Hisham Awartani who was the most seriously injured of the three men. I believe we are also joined, although I've not had a chance to meet him directly. We've spoken by phone, but I believe Roddy Tamimi is either here or on his way. He just flew in from California this morning and he is the uncle of Kanan Abdel Hamid. Rich, and I hope you'll share this with Roddy if I don't get a chance to directly. I am so sorry that this terrible event happened to your nephews here in Burlington. Thank you for your collaboration and coordination, Rich, throughout this ordeal, including the conversations that we had yesterday. And please know that the city is going to continue to do all that we can to support you and your loved ones during their recovery. We're going to get a chance to hear directly from Rich in a minute. I hope that Burlington's Palestinian Arab and Muslim communities see in today's prompt arrest the city's commitment to justice and to keeping all members of our community safe. I have been and will continue to be in communication with Muslim leaders about how Burlington can support their communities through this time of war and high international tension. I want to thank you, Chief Murad, and the hard-working and skilled Detective Bureau led by Lieutenant Mike Belvo for once again bringing a shooting investigation to a prompt resolution. Your quick and forceful actions over a holiday weekend on the heels of the push you've been making to solve the November 12th double homicide speaks to the commitment and the character of the Burlington Police Department. I'm grateful, Chief Murad, also for your leadership creating the Chittenden County Gun Violence Task Force more than one year ago as we experienced the spike in gun violence this community has never experienced before. As you will hear in a moment when Chief Murad reviews the steps that led to the arrest of Mr. Eaton, that task force of local state and federal partners including critically both the Bureau of Alcohol, Tobacco, and Firearms as well as the FBI responded immediately to the shooting and played a critical role in the events leading to the arrest. This task force has become an invaluable part of our response to serious events at a time when we and so many other law enforcement agencies are understaffed. I'm also very grateful to State's Attorney Sarah George for working deep into the night, last night to secure search warrants and bring forward charges. Thank you, Sarah, for your partnership and leadership. And finally, I am grateful that U.S. Attorney Nicholas Karris is here with us. Not only has the U.S. Attorney been extremely supportive throughout this event, the federal government has provided enormous technical resources and law enforcement capacity throughout, and I have received direct support from the White House and the Department of Homeland Security as well as we navigated this unprecedented event. In fact, one of the reasons that this event got, this press conference got started a little bit late is that I just had an extended phone call with President Biden. It was my honor to thank the president for his leadership and caring for our community and for the victims of this terrible crime. And it was also my honor to make clear to him the critical role that federal partners have played in securing this quick arrest. We are very fortunate to have dedicated public servants working in the federal government who are committed to holding shooters accountable for gun violence despite the enormous barriers in the law and regulations that they face in trying to conduct that mission. And with that, I would like to ask Chief Murad to walk us all through the events since Saturday night that led us to now. Thank you, Chief. Thank you, sir. And hello, everyone. My name is John Murad. I am the chief of police here in Burlington. And first of all, I want to acknowledge that three young men have been the victims of an unprovoked and terrible crime and that their lives have been changed forever. One in particular faces a tremendous struggle and recovery with injuries that may be life long. They've been named elsewhere, but in my personal communications with the family and in detectives communications with the victims initially that was not something that they wanted. And so our communications thus far have not named them. But in their innocence and their suffering and for that of their families and their community and our community, that can't go without mention. Next, I want to share details about this incident and the investigation as we currently know them. And all of this is preliminary information for an investigation that is ongoing. And this is only the beginning of this investigation for our detectives, for the office of the state's attorney and for our federal partners as well. We allege that Jason Jay Eaton 48 committed three acts of attempted murder. On Saturday, November 25th of 2023 at approximately 1826 hours in front of an address at 69 North Prospect Street, we received calls that people had been shot and there had been gunfire. Officers arrived and found two men shot at that location being tended to by people from the address who had brought out blankets to cover them. It was a very cold night. Both of these men were incapacitated by that by the gunfire, but they were talking and conscious. Another individual was located on a street to the east, also suffering from a gunshot wound. That individual was transported directly to the hospital by Burlington police officers. The first two young men were treated on the scene in an emergency response sensed by our great partners at the Burlington Fire Department and then were transported to the University of Vermont Medical Center. This began now an investigation. Unfortunately, we have become all too good at this in this city. We have gone from a city that routinely saw two gunfire incidents a year to a city that saw 26 last year, and I believe this was number 16 or 15 of this year. We responded. Our detectives were called out. Our identification unit or the CSI kind of unit that we have was called out. And so was the Chittenden County Gun Violence Task Force, which was a creation of Alex Schmidt at the ATF of this police department and of partners at Milton, at Colchester, at the South Burlington Police Department. All of these members, excuse me, responded to this incident, and we began the things that we do. Evidence recovery, a canvas, a partner with Fish and Wildlife brought a canine. That canine did a track, but was most instrumental in finding additional evidence at the scene that was hidden and obscured in the grass. We recovered ballistics evidence at that scene. We recovered shell casings from a 380 semi-automatic, and that was hornady ammunition with a red tip. Both of those things aren't entirely usual. They're not uncommon, but we see nine millimeters more often than we see 380s, and that was something of note. Obviously, this was only the beginning of an investigation. The young men were interviewed by detectives. They stated that the person had not made any comments to them and had merely approached them while they were walking down the street, essentially minding their own business, and they were speaking in a mixture of English and Arabic, which is their want. Two were wearing kafias, and they had no knowledge of this individual, had not encountered him before. He stepped off a porch and produced a firearm and began discharging that firearm. We recovered rounds, excuse me, ballistics consistent with four rounds, and all three of the individuals of the young men were struck. Our detectives worked that scene. We closed the scene once we had recovered all the evidence that we could, and we began the processes to follow this through in the new day. On Sunday, we began new interviews with witnesses, with the victims who were available to us. One was not owing to medical treatment. We began recanvassing. Patrol officers, which are the backbone of any police department, had done that kind of work at the initial response. Canvases knocking on doors, attempting to see whether there were cameras in the vicinity. Unfortunately, we did not have a lot of good camera coverage as we have in other incidents in our downtown. But today on Sunday, detectives began to do that again. Again, working with detectives out of that Chittenden County Gun Violence Task Force, and with new partners too. Not only are our normal long-term partners in the ATF, but new partners as well, folks with whom we have strong relationships but don't have occasion to work as often, but were brought in by the tremendous amount of concern and attention that this case garnered at the highest levels of government. I received personal calls from both senators, from our congresswoman. We knew the governor was paying attention to this case, and we knew that it was being addressed at the highest levels as well as the mayor's phone call with the president just now indicates. The FBI brought in a number of people and were able to augment our resources as we pursued this case. At approximately, just after 3.30, excuse me, at approximately 15.38 during a doorknock canvas at the location of the shooting, members of the ATF knocked on a door that had been closed the night before. Officers that the previous night had done a sweep of that entire location for both investigatory reasons and for security. So armed officers went through that apartment building, but we did not have probable cause to go into every single apartment. So if people answered the doors, we spoke to them. And if they didn't or if the apartment appeared closed, it is in fact a holiday weekend, we had no legal authority to go into units in that moment, especially since at least one witness had indicated that the shooter had in fact left the scene heading north. And so the notion of being able to just enter apartments was not available to us. We instead did this read canvas doorknock and upon knocking on one door, the ATF agents were greeted by a man who stepped out of the hall out of the door towards them with his palms up at waist height and stated something to the effect of I've been waiting for you. The ATF agents said, why is that? And the gentleman said in some instance, I would like a lawyer. He stepped into the hallway. They moved between him and the door and said, well, why is that? He stated, I would like a lawyer. Do you have any guns in there? He stated that he had one. They asked if he had any others. He stated again, I think I would like a lawyer. At that point, he was detained. He was taken to our police headquarters by a marked cruiser of the Burlington Police Department. And we swept that location only for security, which is a different level of sweep than a search, closed the locations, secured it, and then obtained a search warrant. Working closely with the state's attorney's office and with the U.S. attorney's office to make certain that that warrant was something that was going to be useful to both if it came to being something that we would use either with the state's attorney or with the U.S. attorney. We conducted investigations during that time as well, including finding out that the gentleman in custody, Mr. Eaton, had purchased a 380 recently. We discovered that there was a, that he was, we spoke to some people who knew him. The warrant was granted by a Vermont judge and it was executed at approximately 2153 hours. And at that point, during that search, we recovered a 380 firearm and Hornady red tip ammunition. ATF tests through the National Integrated Ballistic Information Network or NIBON have since connected that firearm to the casings that were recovered at the scene of the shooting. You know, cops often say that it's better to be lucky than good, but it's really even better to be both. And I believe that's what we have here. We certainly had a lot of incredibly good work, but to go to an address and find that individual in this way too is, is a little bit more than just good work. We have a lot more to do, however. Even with this arrest, we are still working. There is more investigation to be done and that includes trying to determine motive. We still do not know as much as we want to know, but I would urge the public and you in the media to avoid making conclusions based on statements from people who know even less than we currently do. We are working hard to find out this information and we are nowhere near the end of this investigation, but we are trying to determine what we have beyond the knowledge that, yes, this person, we have full probable cause to believe committed this horrible, horrible crime. Our next step will be building that case to make certain that it is as strong as possible when we, so that we can deliver it to the state's attorney. Hi, everybody. My name is Rich Price. I'm the uncle of one of the shooting victims, the Sham Awartanis, my nephew. And I'm proud to be here with Roddy Tamimi, who is the uncle of Kanan, and Roddy, will you join me? We speak only on behalf of the family because the family can't be here. I want to say that these three young men are incredible and that's not just proud uncle speaking, but it's true.  They are committed to building incredible lives. They each go to great schools. They were our house guests for Thanksgiving as they have the past few Thanksgiving's. We had just been to my eight-year-old twin's birthday party. These three college students, who, who, if you're in college, who wants to go to a eight-year-old birthday party? But these three guys did and they came. They played with my boys. We had just come home and they were walking around the block. And this is when this happened. I can tell you that I've, I've been with them almost constantly since Saturday evening. I've been listening to them talk to one another and try to process the events. And I'm blown away by their resilience, by their good humor in the face of these difficult times. And I want to extend gratitude on behalf of the families to the leadership of Chief Murad and the work of the Burlington Police Department, the work of the federal agencies that are on the ground, to Mayor Murrow and, and his team. I moved here 15 years ago and I never imagined that this sort of thing could happen. And my sister lives in the occupied West Bank and people often ask me, aren't you worried about your sister? Aren't you worried about your, your nephews and your niece? And the reality is, as difficult as their life is, they are surrounded by an incredible sense of community. And tragic irony is not even the right phrase, but to have them come stay with me for Thanksgiving and have something like this happen speaks to the level of civic, vitriol, speaks to the level of hatred that exists in some corners of this, of this country. It speaks to a sickness of gun violence that exists in this country. And I'm proud of, even though this is devastating that this happened in this community, I am really proud and grateful to the community that is here and has rallied around our family and these boys. And I want to finally say, I'll ask Roddy to say some words, but I want to finally say thank you to the staff at UVM Medical Center. The doctors and nurses and staff have been so kind and committed to the well-being of these young men. I'm grateful to them as is, as is the families. And Roddy here, uncle of Canaan, just flew in from California. And I haven't had a chance to say hello. Hello Roddy. I don't know that I have the same words that you have prepared. But thank you for that. He speaks for myself and the whole family, for all three of the families involved. It's, I'm still processing everything. I just got here from California and walked right in as you saw. We consider ourselves very lucky at this point as far as Canaan's family is concerned, not so with the, with Hisham and Tahseen. We're really praying for them and we're still in shock over this. And as he stated, Canaan grew up in the West Bank and we always thought that that could be more of a risk in terms of his safety. And sending him here would be a, you know, the right decision. We feel somehow betrayed in that decision here. And, you know, we're just trying to come to terms with everything. At this point, I don't have much else to add. I think Rich did a great job. Thank you. Good afternoon. My name is Sarah George. I am the Chittenden County State's Attorney. I want to stress, start by stressing how infuriating and atrocious this situation is. My office joins the community in the outrage over the gun violence we are experiencing in our community and our state. We join in the outrage over the hate and inhumanity shown to Hisham, Tahseen, and Canaan. Today our office filed charges against Mr. Eaton, specifically three counts of attempted murder for the senseless, unprovoked violence against these three young men. These are three life felonies. And although we do not yet have evidence to support a hate crime enhancement, I do want to be clear that there is no question this was a hateful act. We currently live in a world of divisiveness and hate rather than inclusion and love. Our media and our social media are often filled with harmful and false rhetoric that provokes hateful beliefs and pits us against one another. When this rhetoric is coupled with a nation obsessed with firearms and easy access to the same, violence is not just predictable, it is inevitable. As much as we claim otherwise, Vermont is no exception to this. We must as a community and state stand strong against hate and divisiveness. We must see the humanity in each other and unite as one community and one state that will not tolerate this hate. And we must reckon with the reality that Vermont has one of, if not the highest, rate of gun deaths in New England. In the meantime, as our community grapples with this reality, my office will continue to work closely with the Burlington Police Department and our federal partners to hold Mr. Eaton accountable and any others who commit such horrendous violence in our home. Thank you. Good afternoon. I'm Nicholas Carrist, United States Attorney for the District of Vermont. First of all, I would like to express my empathy and condolences to the three victims and to their families and the affected communities. Rich and Roddy, your perseverance and your statements today are moving and profound. So thank you. These shootings are another example of senseless gun violence that needs to stop. I truly wish the three victims a speedy recovery and a return to as normal a life as possible. Second, I would like to offer my thanks and deep appreciation to our law enforcement partners, especially the Burlington Police Department, the ATF, and the FBI. They have all brought the full extent of their resources to bear on this ongoing investigation, including, among other things, ballistic matching technology provided by ATF and victim services and computer and cell phone analysis provided by the FBI. Finally, as for the ongoing investigation, I have no comment on the evidence uncovered so far or the charges filed by the Chittenden County State's Attorney's Office or the likely path of this case as the investigation unfolds. I can say that my office and the Department of Justice as a whole, including the Civil Rights Division, will follow the facts where they lead. The priorities of the Attorney General of the United States and my office are to protect public safety, ensure civil rights are protected, and to follow the rule of law. Following the rule of law means not making prejudgment and requires following the facts where they lead, and that's what we intend to do. Thank you. Okay. I want to thank everyone who shared their remarks, especially Rich and Roddy. Say again, welcome to Burlington, Roddy. I'm sorry it is under these circumstances, and with that, I think any of us up here would be happy to answer questions. Here in Vermont, we have one traffic interaction. We believe that he arrived sometime in the summer and has not been a resident here for long. We believe he came from the Syracuse area, and we have made contact with people who knew him there. We are working on developing whether or not there are additional records in other places, but in so far as Burlington goes, no, it does not appear that he does. It also appears that he's a very recent arrival here. So the suspect, again, it was not our officers. These were agents with the ATF, really terrific men who were embedded in this community, work regularly with our officers. They encountered him from what I understand in some substance. It was a matter of knocking on the door, appearing. He appeared very nervous. He was shaking. He produced his hands empty, stated, I've been waiting for you. Why is that? I think I need a lawyer. Do you have any weapons in there? There's a shotgun inside. Anything else? I think I need a lawyer, and at that point he was detained. His affect was remarkably, there was shakiness and a sense of concern, but also something of a lack of affect. I watched his being presented with the information that he was going to be charged with attempted murder times three, and his reaction was negative. There was very little reaction, an affect less reaction to the information that he was going to be charged with attempted murder times three. We've got a lot of people interested in some questions. I'll try to moderate, and if you could raise your hand, and I'll try to recognize you. We do not have any information on a religious affiliation from Mr. Eden. All three young men are in ICU, currently in UVM Medical Center. As Roddy said, I think Kanan's injuries are though difficult. He'll make a full and speedy recovery. Tahseen is in quite a lot of pain, and my nephew, Hasham, received a spinal injury as a result of the shooting and faces a long recovery. Okay, here in the front row with the blue sweater. So I think the state's attorney's phrase is the phrase that matters. Whether or not this is a hate crime by the law, it was a hateful act. It's one that we abhor, and anybody who steps out from a porch and attacks three, random passerby, for whatever reason, is expressing a form of hate. I'm not prepared to speak about what we learned from the individuals who know Mr. Eden. That's part of the ongoing case. We are working hard on making these kinds of determinations. Among other evidence that was recovered at the apartment is computer evidence. We will be making efforts with the great assistance of the Federal Bureau of Investigation and the incredible skill sets that they can bring to bear on looking into those devices and seeing whether or not we find histories. We know that in other instances and other places there have been writings, for example. There have been things that people have left social media ghosts and things online that have given us the opportunity to impute motive. We do not currently have that, and what we do have I'm not yet prepared to discuss at this stage of the investigation. Okay, Mike, go ahead. On behalf of our Federal partners, the Chief described the situation at the door. To be frank, Chief Murad knows more exactly about what happened at the door than I do. So if there's more to add beyond what the Chief said, he can do that. Otherwise, we're going to have no other comment about what happened there. Mike, I'm going to speak on behalf of the Federal partners here today. Okay, second row here. It was acquired legally through a federally licensed firearms dealer here in the state, and there were no flags on that purchase that we know of, and it was acquired only a short while ago, a few months ago, I believe. The back row.  Thanks. As I mentioned before, here in Vermont, we have a traffic stop in our system right now. We are working to determine whether or not there are additional criminal history acts in any other locations. We believe he came from the Syracuse area in New York. That's obviously someplace that we'll be looking at. That's a great question. There was a press release done in less than an hour from the incident. We did close the location where it happened in order to facilitate investigation. We did not have any information that made it something that was an act that might have had wider public safety implications. Look, there are times where we have acts of violence in this city, and there's always the opportunity for that act of violence to either affect the people that it's targeted, affect people who are in the nearby vicinity, or to have wider implications as well. Last, as the mayor mentioned, we are currently still working on a double murder that occurred in the beginning of November. These acts, when they happen, have the potential to hurt many people. We do not lock down an entire city for something like that. There are other kinds of circumstances. In this case, we did not have that. As I said, this was a matter of trying to figure out what we had and working as quickly as we could to address that. If you want to be recognized again, I'll come in a second. Thank you. I can answer that. We are very lucky in Vermont. We don't have a lot of these incidents occur. It's an enhancement of a particular crime that is an additional element that the state has to prove beyond a reasonable doubt. Typically, the state has no requirement to prove motive. Many jurors, of course, have their opinions on that, and prosecutors might put forth a theory, but it is not an element that we have to prove. When you add a hate crime enhancement, that changes things. It means that we actually have to prove beyond a reasonable doubt an additional element of the crime. Sometimes, that's quite clear based on things that might have been said by the defendant or things that may have been immediately present in an apartment or online, but we do need direct and great evidence to support that additional element. I would also just say that in this particular case, these are life felonies. Any in Vermont, if it is being added to a felony that is five years or more, it actually just becomes an aggravating factor to be decided at sentencing. In this particular case, even with the hate crime enhancement, he would still be facing the same penalty 20 years to life, but the judge could find an aggravating factor if the state proves that element beyond a reasonable doubt. Did that address your question for Attorney George? So again, I think one of the tragedies of this event is that Burlington is a community that seeks to be extremely welcoming and inclusive to all. It is a place that wants to care for everyone and this violence violates all of that. It violates our most deeply held values and goals as a community, so we certainly will do everything we can to ensure that this is a tragic, terrible anomaly to the experience that most visitors have, that almost all visitors have to this community. Okay, I'll try to get the people I haven't spoken yet. Liam, go ahead. I think that would depend on the particular identity being discussed. In some cases, I think it would, and in others, I'm not sure it would make much difference. Again, we haven't had to test it, so it's hard to really say for sure, but I know that it is a high burden. It is something that we have wanted to charge in the past and had not had enough to do and so have used it as a sentencing enhancement only. Okay, in the second row. Sorry, can you speak up? I believe the family's fear that this was motivated by hate, that these boys were, these young men were targeted because they were Arabs, that they were wearing kafiyas. I think that is our fear. I'll speak on behalf of my family, my sister's family. We believe in the sanctity of the presumption of innocence and due process and so we'll support the authorities as they go through their investigation, but certainly seems like that's our fear. Kinan's family shares that same sentiment exactly. Absolutely hard to imagine in this time and with everything that's happening that it was just a random act doesn't feel that way, but we are absolutely willing to wait to find out and let due process take its course. Thank you. Okay, in the back, go ahead. Great. I do want to reinforce the chief answer, the response regarding neighbors. I do just want to say again, on point with your question, since we have had this significant increase in the volume of incidents like this that we're responding to, this Chinning County gun violence task force did respond very quickly and robustly throughout the day and I think that was a big part of why we got to now in terms of the response of neighbors chief. So yes, as the mayor said, there are, I think the gun violence task force is incredibly important. It's deployment is something that we have gotten pretty good at as far as calling people out, having people respond. What you're asking about, I believe, are the neighbors who assisted and I think that that is a picture of the other side and the larger side, the more important side of this community, that you had people inside who came out and immediately began to care for these injured young men and reacted in a way that was about helping and about the same kind of positive communal aspects that are the hallmark of this community and not the actions of this individual that we are going to do our best to hold accountable. Okay, we are going to take a couple more questions. Go ahead. Yeah, the state's attorney is saying it was in 2016 or so. I don't have any information about that traffic stop and whether or not there was anything about it. Had there been something about it that caused our investigators to say, hmm, I think I would know that and I don't. So that's my answer. At this point, we don't have that information. That's all going to be part of an investigation. Although, you know, as the state's attorney mentioned, motive is important to us to know it is not necessarily a component of a charge. Our object was to determine that we have the physical evidence that associates him with this act and I'm confident that that evidence is very strong. Okay, I want to thank everyone for coming today. I want to say again, thank you to the families for being here. I think a number of us will remain around if there are further questions, but I think we'll end the program there. Thank you all to the members of the press corps from really around the country around the world for being here and we'll work to get you additional answers if you need them through the day. Thank you.


Hello, everyone. Welcome to theCUBE's presentation of the AWS Startup Showcase AI and Machine Learning, the top startups building foundational model infrastructure. This is season three, episode one of the ongoing series covering the exciting startups from the AWS ecosystem, talk about machine learning and AI. I'm your host, John Furrier. Today we are excited to be joined by Louis Sez, who's the CEO of OctoML and Anna Connelly, VP of customer success and experience. OctoML, great to have you on again, Louis. Anna, thanks for coming on. Appreciate it. Thank you, John, it's great to be here. I love the company. We had a CUBE conversation about this. You guys are really, really addressing how to run foundational models faster for less. And this is like the key theme. Before we get into it, this is a hot trend, but let's explain what you guys do. Can you set the narrative of what the company is about, why it was founded, what's your North Star, and your mission? Yeah, so John, our mission is to make AI sustainable for everyone. And what we offer customers is a way of taking their models into production the most efficient way possible by automating the process of getting a model and optimizing it for a variety of hardware and making costs effective. So better, faster, cheaper model deployment. You know, the big trend here is AI, everyone's seeing the chat GPT kind of as a shot heard around the world, the Bing AI and this fiasco and the ongoing experimentation. People are into it. And I think the business impact is clear. I haven't seen this in all of my career in the technology industry of this kind of inflection point in every senior leader I talked to is rethinking about how to rebuild their business with AI because now the large language models have come in. These foundational models are here. They can see value in their data. This is a 10 year journey in the big data world. Now it's impacting that and everyone's rebuilding their company around this idea of being AI first because they see ways to eliminate things and make things more efficient. And so now that's telling them go do it and like, what do we do? So what do you guys think? Can you explain what is this wave of AI and why is it happening? Why now? And what should people pay attention to? What does it mean to them? Yeah, it's pretty clear by now that AI can do amazing things for the captures people's imaginations and also now can show things that are really impactful in businesses, right? So what people have the opportunity to do today is to either train their own model that adds value to their business or find open models out there that can do very valuable things to them. So the next step really is how do you take that model and put it into production in a cost effective way so that the business can actually get value out of it, right? And what's your take? Because customers are there. You're there to make them successful. You got the new secret weapon for their business. Yeah, I think we just see a lot of companies struggle to get from a trained model into a model that is deployed in a cost effective way that actually makes sense for the application they're building. I think that's a huge challenge we see today kind of across the board, across all of our customers. Well, I see this. Everyone asked the same question. I have data. I want to get value out of it. I got to get these big models. I got to train it. What's it going to cost? So I think there's a reality of, okay, I got to do it. Then no one has any visibility on what it costs when they get into it. So this is going to break the bank. So I have to ask you guys, the cost of training these models is on everyone's mind. OctoML, your company is focused on the cost side of it as well as the efficiency side of running these models in production. Why are the production costs such a concern and where specifically are people looking at it and why did it get here? Yeah, so training costs get a lot of attention because normally a large number but we shouldn't forget that it's a large, typically one time upfront costs the customer's pay but when the model is put into production the cost grows directly with model usage and you actually want your model to be used because it's adding value, right? So the question that a customer faces, they have a model, have a trained model and now what? So how much would it cost to run in production? So and now without the big wave in generative AI which rightfully is getting a lot of attention because of the amazing things that it can do it's important for us to keep in mind that generative AI models like chat GPT are huge, expensive energy hogs. They cost a lot to run, right? So and given that model usage grows directly, model cost grows directly with usage what you want to do is make sure that once you put the model into production you have the best cost structure possible so that you're not surprised when it gets popular, right? So let me give you an example. So if you have a model that costs say one to $2 million to train but then it costs about one to two cents per session to use it, right? So if you have a million active users even if they use just once a day it's 10 to $20,000 a day to operate that model in production and that very, very quickly gets beyond what you pay to train it. And these aren't small numbers and this cost to train and cost to operate it kind of reminds me of when the cloud came around and the data center versus cloud options like wait a minute, want to cost a ton of cash to deploy and then running it? This is kind of a similar dynamic. What are you seeing? Yeah, absolutely. I think we are going to see increasingly the costs in production outpacing the costs in training by a lot. I mean, people talk about training costs now because that's what they're confronting now because people are so focused on getting models perform enough to even use in an application. And now that we have them and they're that capable we're really going to start to see production costs go up a lot. Yeah, Lucy, if you don't mind I know this might be a little bit of a tangent but training is super important. I get that. That's what people are doing now. But then there's the deployment side of production. Where do people get caught up and miss the boat or misconfigure? What's the gotcha? Where's the tripwire or so to speak? Where do people mess up on the cost side? What do they do? Is it they don't think about it? They tie it to proprietary hardware. What's the issue? Yeah, several things. So without getting too annoyingly technical which am I getting to? You have to understand relationship between performance both in terms of latency and throughput and cost. So reduce latency is important because you improve the responsiveness of the model but it's really important to keep in mind that often leads to diminishing returns below a certain latency making it faster won't make a measurable difference in experience but it's going to cost a lot more. So understanding that is important. Now, if you care more about throughput which is the time it takes for you to units per unit of time you care about time to solution we should think about this throughput per dollar and understand that what you want here is the highest throughput per dollar which may come at the cost of higher latency which you're not gonna care about, right? So in the reality here, John is that humans and especially folks in this space want to have the latest and greatest hardware and often it commits a lot of money to get access to them and have to commit upfront before they understand the needs that their models have, right? So common mistake here, one is not spending time to understand what you really need and then two, over committing and using more hardware than you actually need and not giving yourself enough freedom to get your workload to move around to the more cost effective choice, right? So this is just a matter of harder choice and then another thing that's important here too is making a model run faster on the hardware directly translates to lower cost, right? So Buddy takes a lot of engineers who need to think of ways of producing very efficient versions of your model for the target hardware that you're going to use. And what's the customer angle here because price performance has been around for a long time, people get that but now latency and throughput, that's key because we're starting to see this in apps. I mean, it's an end user piece. I've been seeing it on the infrastructure side where they're taking the heavy lifting away from operational costs. So you got application specific to the user and or top of the stack. And then you got actually being used in operations where they want both. Yeah, absolutely. Maybe I can illustrate this with a quick story with the customer that we had recently even working with. So this customer is planning to run kind of a transformer based model for tech generation at super high scale on NVIDIA T4 GPUs, kind of a commodity GPU. And the scale was so high that they would have been paying hundreds of thousands of dollars in cloud costs per year just to serve this model alone. One of many models in their application stack. So we worked with this team to optimize our model and then benchmark it across several possible targets so that matching the hardware that Louise was just talking about including the newer kind of NVIDIA A10 GPUs. And what they found during this process was pretty interesting. First, the team was able to shave a quarter of their spend just by using better optimization techniques on the T4, the older hardware. But actually moving to a newer GPU would allow them to serve this model in a sub two milliseconds latency. So super fast, which was able to unlock an entirely new kind of user experience. So they were able to kind of change the value they're delivering in their application just because they were able to move to this new hardware easily. So they ultimately decided to plan their deployment on the more extensive A10s because of this. But because of the hardware specific optimizations that we helped them with, they managed to even bring costs down from what they had originally planned. And so if you extend this kind of example to everything that's happening with generative AI, I think the story we just talked about was super relevant but the scale can be even higher. It can be 10 fold that. We were recently conducting kind of this internal study using GPTJ as a proxy to illustrate the experience of just a company trying to use one of these large language models with an example scenario of creating a chat bot to help job seekers prepare for interviews. So if you imagine kind of a conservative usage scenario where the model generates just 3000 words per user per day, which is pretty conservative for how people are interacting with these models. It costs five cents a session.  So from beginning of the year, there's nobody at the end of the year. There's a million daily active users and that you're alone going from zero to a million. You'll be spending about $6 million a year, which is pretty unmanageable. That's crazy, right? For a company or a product that's just launching. So I think, for us, we see the real way to make these kind of advancements accessible and sustainable as we said, is to bring down costs to serve. That's a great story. And I think that illustrates this idea that deployment costs can vary from situation to situation or model to model and that the efficiency is so strong with this new wave. It eliminates heavy lifting, creates more efficiency, automates intellect. I mean, this is the trend, this is radical. This is going to increase, so the cost could go from nominal to millions, literally potentially. So this is what customers are doing. Yeah, that's a great story. What makes sense on a financial? Is there a cost of ownership? Is there a pattern for best practice for training? What do you guys advise? What's, because this is a lot of time and money involved in the potential, good scenarios of upside, but you can get over your skis as they say and be successful and be out of business if you don't manage it. I mean, that's what people are talking about. Yeah, absolutely. I think we see kind of three main vectors to reduce costs. I think one is make your deployment process easier overall, so that your engineering effort to even get your app running goes down. Two would be get more compute, get more from the compute you're already paying for. You're already paying for your instances in the cloud, but can you do more with that? And then three would be shop around for lower cost hardware to match your use case. So on the first one, I think making the deployment easier overall, there's a lot of manual work that goes into benchmarking, optimizing and packaging models for deployment. And because the performance of machine learning models can be really hardware dependent, you have to go through this process for each target you wanna consider running your model on. And this is hard, we see that every day, but for teams who want to incorporate some of these large language models into their applications, it might be desirable because licensing a model from a large vendor like OpenAI can leave you over provision kind of paying for capabilities you don't need in your application or can lock you into them and you lose flexibility. So we have a customer whose team actually prepares models for deployment in a SaaS application that many of us use every day. And they told us recently that without kind of an automated benchmarking and experimentation platform, they were spending several days each to benchmark a single model on a single hardware type. So this is really manually intensive. And then getting more from the compute you're already paying for, we do see customers who leave money on the table by running models that haven't been optimized specifically for the hardware target they're using like Luis was mentioning. And for some teams, they just don't have the time to go through an optimization process. And for others, they might laugh kind of specialized expertise and this is something we can bring. And then on shopping around for different hardware types, we really see a huge variation in model performance across hardware, not just CPU versus GPU which is what people normally think of but across CPU vendors themselves, high memory instances and across cloud providers even. So the best strategy here is for teams to really be able to, at least they look before you leap by running real world benchmarking and not just simulations or predictions to find the best software hardware combination for their workload. Yeah, you guys sound like you have a very impressive customer base deploying large language models. Where would you categorize your current customer base? And as you look out, as you guys are growing, you have new customers coming in. Take me through the progression.  Are they big app folks? Are they kicking the tires? And then as people are out there scratching this, I got to get in this game. What's their psychology like? Are they coming in with specific problems or do they have a specific orientation point of view about what they want to do? Can you share some data around what you're seeing? Yeah, I think we have customers that kind of range across the spectrum of sophistication from teams that basically don't have ML ops expertise in their company at all. And so they're really looking for us to kind of give a full service. How should I do everything from optimization, find the hardware, prepare for deployment. And then we have teams that maybe already have their serving and hosting infrastructure up and ready and they already have models in production and they're really just looking to take the extra juice out of the hardware and just do really specific on that optimization piece. I think one place where we're doing a lot more work now is kind of in the developer tooling model selection space and that's kind of an area that we're creating more tools for particularly within the PyTorch ecosystem to bring kind of this power earlier in the development cycle so that as people are grabbing a model off the shelf they can see how it might perform and use that to inform their development process. Luis, what's the big, I like this idea of picking the models because isn't that like going to the market and picking the best model for your day? It's like, isn't there certain approaches? What's your view on this? Because this is where everyone, I think it's going to be a land rush for this and I want to get your thoughts. You guys are just- For sure, yeah. So I guess I'll start with saying that one main takeaway that we got from the GPTJ study is that having a different understanding of what your models compute and memory requirements are very quickly early on helps with the much smarter AI model deployment, right? And in fact, Ana just touched on this but I want to make sure that it's clear that OptoML is putting that power into users' hands right now. So in partnership with AWS, we are launching this new PyTorch native profiler that allows you with a single one line code decorator allows you to see how your code runs on a variety of different hardware after acceleration. So it gives you very clear data on how you should think about your model deployments. And this ties back to choices of models. So if you have a set of choices that are equally good of models in terms of functionality and you want to understand after acceleration how are you going to deploy, how much they're going to cost or what are the options? Using an automated process of making a decision is really, really, really useful. And in fact, so I think these of these events can get early access to this by signing up for the Octopods. There's this exclusive group for insiders here. So you can go talk to optoML.ai slash pods to sign up. So that Octopod, is that a program? What is that? Is that access to code? Is that a beta? What is that? Explain, take a minute, explain Octopod. I think the Octopod would be a group of people who is interested in experiencing this functionality. So it is the friends and users of OctoML. That would be the Octopod. And then, yes, after you sign up, we would provide you essentially the tool in code form for you to try out in your own, I mean, part of the benefit of this is that it happens in your own local environment and you're in control of everything, kind of within the workflow that developers are already using to create and begin putting these models into their application. So it would all be within your control. Got it. I think the big question I have for you is when do you, when does one of your customers know they need to call you? What's the, what does their environment look like? What are they struggling with? What are the conversations they might be having on their side of the fence? As if anyone's watching this, they're like, hey, you know what, I've got my team, we have a lot of data. Is it, do we have our own language model? Or do I use someone else's? There's a lot of this, I won't say discovery going on around what to do, what path to take. What does that customer look like if someone's listening? When do they know to call you guys? OctoML? Well, I mean, the most obvious one is that you have a significant spend on AI ML come and talk to us, you know, putting AI ML into production. So that's, that's the clear one. In fact, just this morning I was talking to someone who is in the life sciences space and is having, you know, 15 to $20 million a year cloud-related AI ML deployment. It's a clear, it's a pretty clear match right there. Right? So that's on the cost side, but I also want to emphasize something that Anna said earlier that, you know, the hardware soft, the hardware and software complexity involved in putting a model into production is really high. So we've been able to abstract that away offering a clean automation flow enables one, one to experiment early on, you know, how models would run and get them to production. And then two, once they are into production gives you an automated flow to continuously updating your model and taking advantage of all this acceleration and ability to run the model on the, on the right hardware. So anyway, let's say one then is cost, you know, you have a significant cost and then two, you have an automation needs. And Anna, please compliment that. Yeah. Yeah, I think that's, that's exactly right.  You're launching a new feature you expect to get a lot of usage or any, and you want to kind of anticipate maybe your, your CTO, your CIO whoever pays your cloud bills is going to come after you, right? And so they want to know, you know, what's the return on putting this model essentially into my application stack? Am I going to, is the usage going to match what I'm paying for it? And then you can understand that. So you guys have a lot of the early adopters, they got, they got big data teams they've pushed in the production they want to get it to a little QA if test the waters, understand, use your technology to figure it out. Is there any cases where people have gone into production and then have to pull it out? It's like the old lemon laws with your car, you buy a car and oh my God, it's not the way I wanted it.  Are you seeing that? Are people pulling stuff out of production and redeploying? Or I can imagine that if I had a bad deployment, I'd want to refactor that or actually replatform that. Yeah. Do you see that too? Definitely after sticker shock, you ask your customers to come and make sure that they know that sticker shock won't happen again. But then there's another more, more subtle aspect here that I think we likely touched on be worth elaborating a bit more is just how are you going to scale in a way that's feasible depending on the allocation that you get, right? So as we mentioned several times here, you know, model deployment is so hard or dependent and so complex that you tend to get a model for a harder choice and then you want to scale that specific type of instance. But what if when you want to scale because suddenly luckily got popular and you know, you want to scale it up and then you don't have that instance anymore. So how do you live whatever you have at that moment is something that we see customers needing as well. You know, so in fact, ideally we want these customers to not think about what kind of specific instances they want. What they want is to know what their models need. Say they know the SLA and on a final set of hybrid targets and instances that hit that SLA and whenever they auto-scaling they're going to scale with more freedom, right? Instead of having to wait for AWS to give them more specific location for a specific instance. So what if you could live with other types of hardware and scale up in a more free way, right? So that's another thing that we see customers you know, coming like this, like now they need more freedom to be able to scale with whatever is available. Anna, you touched on this with the business model impact to that six million cost if that goes out of control. There's a business model aspect and there's a technical operation aspect to the cost side too. You want to be mindful of riding the wave in a good way, but not getting over your skis. So that brings up the point around, you know, confidence, right? And teamwork, because if you're in production there's probably a team behind it. Talk about the team aspect of your customers. I mean, they're dedicated. They go putting stuff into production. They're developers, they're data. What's it for them? Are they on the beach, you know, reading the book? Are they easy street for them? What's the customer benefit to the teams? Yeah, absolutely. With just a few clicks of a button, you're in production, right? That's the dream. So yeah, I mean, I think that, you know, we illustrated it before a little bit. I think the automated kind of benchmarking and optimization process, like when you think about the effort it takes to get that data by hand, which is what people are doing today. They just don't do it. So they're making decisions without the best information because it's, you know, there just isn't the bandwidth to get the information that they need to make the best decision and then know exactly how to deploy it. So I think it's actually bringing kind of a new insight and capability to these teams that they didn't have before. And then maybe another aspect on the team side is that it's making the handoff of the models from the data science teams to the model deployment teams more seamless. So we have, you know, we have seen in the past that this kind of transition point is the place where there are a lot of hiccups, right? Data science team will give a model to the production team and it'll be too slow for the application or it'll be too expensive to run and it has to go back and be changed and kind of this loop. And so, you know, with the PyTorch profiler that Luisa was talking about and then also, you know, the other ways we do optimization that kind of prevents that handoff problem from happening. Luisa, you guys have a great company. Final couple of minutes left. Talk about the company, the people there, what's the culture like? You know, if Intel has Moore's law, which is, you know, doubling the performance in a few years, what's the culture like there? Is it, you know, more throughput, better pricing? What's the, explain what's going on in the company and put a plug in? Luisa, we'll start with you. Yeah, absolutely, no, I mean, I'm extremely proud of the team that we built here. You know, we have a people first culture, you know, very, very collaborative and folks, we all have a shared mission here of making iMore accessible and sustainable. We have a very diverse team in terms of backgrounds and live stories. You know, to do what we do here, we need a team that has expertise in software engineering, in machine learning, in computer architecture, even though we don't build chips, we need to understand how they work, right? So, and then, you know, the fact that we have this, this very, really, really varied set of backgrounds makes the environment, you know, it's a very exciting for you to learn more about, you know, assistance end to end, but also makes it for a very interesting, you know, work environment, right? So people have different backgrounds, different stories, some of them went to grad school, others, you know, were intelligence agencies and now we're doing, are working here, you know, so we have a really interesting set of people and, you know, life is too short not to work with interesting humans, you know, that's something that I like to think about, you know.  What's the culture like? What's the, what's the company vibe and what are you guys looking to do? What's the, what's the customer success pattern? What's, what's up? Yeah, absolutely. I mean, I, you know, second all of the great things that we just said about the team, I think one that I, an additional one that I'd really like to underscore is kind of this customer obsession to use the, a term you all know well and focused on the, on the end users and really making the experiences that we're bringing to our users who are developers really, you know, useful and valuable for them. And so I think, you know, all of these tools that we're trying to put in the hands of users that the industry and the market is changing so rapidly that our products across the board, you know, all of the companies that, you know, are part of the showcase today, we're all evolving them so quickly and we can only do that kind of really hand in glove with our users. So that would be another thing I'd like to say. I think the change dynamic, the power dynamics of this industry is just the beginning. I'm very bullish that this is going to be probably one of the biggest inflection points in history of the computer industry because of all the dynamics of the confluence of all the forces, which you mentioned some of them.  Now we have this, I mean, I wouldn't even put social media in the close to this. Like this is like changes user experience, changes infrastructure. There's going to be massive accelerations in performance on the hardware side from AWS's of the world and cloud. And you got the edge and more data. This is really what big data was going to look like. This is the beginning. Final question. What do you guys see going forward in the future? Well, it's undeniable that machine learning and AI models are becoming an integral part of an interesting application today, right? So, and the clear trends here are, you know, more and more computational needs for these models because they're getting more, only getting more and more powerful. And then two, you know, seeing the complexity of the infrastructure where they run, you know, just considering the cloud, there's like a wide variety of choices there, right? And being able to live with that and making the most out of it in a way that doesn't require, you know, an impossible to find team is something that's pretty clear. So the need for automation abstracting with the complexity is definitely here. And we're seeing this, you know, trends are that you also see models starting to move to the edge as well. So it's clear that we're seeing, we're going to live in a world where there's no large models living in the clouds. And then, you know, edge models that talk to these models in the cloud to form, you know, an end to end truly intelligent application. Anna. Yeah, I think, you know, our, we said it at the beginning, our vision is to make AI sustainable and accessible. And I think as this technology just expands in every company and every team, that's going to happen kind of on its own. And we're here to help support that. And I think you can't do that without tools like those that are coming up. I think it's going to be an error of massive invention creativity. A lot of the format heavy lifting is going to allow the talented people to automate their intellect. I mean, this is really kind of what is going on. And Louise, thank you so much, Anna. Thanks for coming on this segment. Thanks for coming on theCUBE and being part of the 80 of startup showcase. I'm John Furrier, your host. Thanks for watching.


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What's up everybody? It's Jason here for Jasper's Case Brace.com. Another case of Flawless sold out guys. 2022 Flawless Baseball Two Box Case Break Picketing number two. And here we go guys. Again thank you guys for selling all of these out. We got five sold out before we even started today guys. Here's number two. Greg got the Rockies last bomb soldier and this one double last bomb soldier looks like. So here we go guys. So we got some solid stuff in the first one. Great patches out of threes out of fives. We got a one-on-one patch autograph. The Kangaroo Juniors are three in case. Just probably the biggest card. No rookie in case in the first one. So we have to find a rookie one. Hopefully here number two. 11 out of 20 Tristan Casas to start us off and that's Boston Red Sox Zachary H. And then we have a dual patch Bobby Whitt Jr. Hunter Green. One out of 15. So that'll be probably a randomizer between Joe with the Blue Jays and then Hunter Green or sorry not Blue Jays. Royals is Stacey and then Reds is John. FSU cards nice. Awesome man. Will Bednar 13 out of 15. That is for the San Francisco Giants. Giants is Greg Dash. Look at that patch. Suzuki to 20. 10 of 20. A little Cubs patch right there. That is Chicago Cubs going to David Chan. We have a 6 of 10 Josiah Gray for Washington. Washington Nationals is Zachary. And then we got Marcel Osuna to 15 for Atlanta going to Jason K. Got a CJ Abrams. 12 out of 15. San Diego Gunn or for the first time I think actually. Joe. And the last one here is a Freddie Freeman. Atlanta Braves Edition Freddie Freeman. Prime signature materials. 14 of 15. So that is going to Jason K. There's the first one. So here we go first. We got a Manny Machado one out of five. Double Emerald USA Baseball. Again these are all going to USA Baseball. So of course teams are currently on that's a Padre. So Padres is Joe. And then this one here. Juan Soto momentous autograph to 15 and that is still Washington Nationals Juan Soto. So that's going to go to Zachary and the Nationals. I know people sorry man. Not the craziest hits but I know any hits. It's gonna be good in this right. I mean maybe you're gonna get the better stuff man. Don't worry. Here we go guys. Next bot is a showcase relic and that looks like it's a little chipped here on the top right. And that's kind of hard for you guys to see that but like see it right there. Royce Lewis. That's going to Minnesota. Twins is Eric. We have a out of seven Frankie Frisch. St. Louis and Cardinals is Kaisan. We got a Dutchman RPA there to 25. Cubs is David Chan. We got a Shane Baz for Tampa Bay 23 out of 25. James with that one. Quad relic. That is Matt Manny. Or not Quad relic. I guess that's dual relic. Ten of ten. Detroit Tigers is Kev. We have a Jaren Duran 11 out of 20. And look at those patches. Josh Jung. Look at that. That is three out of five. Texas Rangers going ahead. And we got a Diamond autograph of Chris Bryant. Four out of ten. Now again that's USA Baseball so that'll go to the team that's currently on it. I think he's on the Rockies right? Did he sign that long term deal with the Rockies? Rockies is going to Greg Dash. And second briefcase guys. Here we go. This one might have the Diamond this time. I might have peeped a little bit and seen an auto over there. And yes another USA Baseball Diamond here. That's Bryce Harper. Two out of twenty again. USA Baseball will be going to Phillies. Phillies that is going to Zachary. I didn't know who the autograph was but I did see like ink on this side so that's why I figured. Alright here we go. And it's Ichiro. Very nice. Nine out of ten. Breaks 84 year old hits record. And that is Seattle Mariners edition of Ichiro. And Mariners. Chris Butler. Again with the Mariners. Very nice man. So you got a Ken Griffey Jr. in the first case. Got a nice Ichiro to go with it in the second case. So nice. Again still no veteran. Or some still no RPA patch in case Cartier guys.  There you go guys another solid case of flawless there. Bobby Wood Jr. Hunter Green Dual Relic. Let's randomize that. And then number three will be coming up next. So let's see. Here we go guys. Roll it. 11 times 5 into 6. 1 2 3 4 5 6 7 8 9 10 11 11 Reds win that battle there. And John G with that one. So there you go guys. Number three is coming up next guys. Jaspiescasebreaks.com guys. Get some more spots in the other bricks as well. We have a lot.