Hi, I'm Maxim here. So today I'd like to welcome like at any of us about this from the comparative bioinformatics group at the Center for genomics regulation in Barcelona. And he's going to talk about like protein fall, which is like a pipeline I'm really looking like forward we are about more and before the first release which is from what I hear like coming soon. So before we actually start, I'd like to say that just send you back initiative for helping us out. And listeners, you will be able to admit yourself at the end of the talk for questions. Thank you, thank you, Maxie for the nice introduction. I'm very happy today that I will present the NF core protein fold pipeline, best practice for informatics pipeline for protein structure prediction. Let me introduce first myself. I'm Athanasius Balzis. I'm a PhD fellow in Cedric Notre Dame's lab at Center for genomic regulation in Barcelona. And my thesis is about applications of protein structure modeling on multiple sequence alignment and phylogenetic reconstruction. And this is where it connects to protein structure prediction methods because I am very interested in all these tools. And I use them in my daily routine. So with the brief introduction to protein structure prediction, as you may know, there are there is scarce data on experimental protein structures, mainly due to technical difficulties with the already existing techniques. It is a long standing question for the community. How can we start from one GM in acid sequence to go to a 3D structure and gain more insight into the function of proteins. And for this reason, the so called protein folding problem. And for this reason, many techniques have been developed during the last mid century, and can be categorized into two main categories the templates based methods. For example, the gene modeling or for recognition, which rely on already existing experimental structures that are used as template to fold the query, the default, the query sequence. And so on the other hand, we have also template free or ab initio methods. We have a lot of some categories. For example, molecular dynamic simulations, where we tried to use physics law to find the conformation with the lowest dips energy, Gibbs free energy fragment based approaches such as Rosetta. Lately, per why special restraint based approaches where you use techniques to predict the inter residue contacts or the distances between into the query protein, and then use them as restraints in simulates in simulations in order to get the final predicted model. However, in the last year, a alpha fold to achieve the major breakthrough in this field. And it's now able to predict the protein structures from sequence with an unprecedented accuracy. This is mainly based on the incorporation of deep learning frameworks in the field. For example, here in the figure below you can see a brief representation of the alpha fold workflow, where we start with an input sequence. And this genetic databases in order to form an MSA from homologous sequences and convert it into a tensor. And also on the other hand you search for structural templates in order to populate these bearing matrix which actually represent the inter residue interactions of the input sequence. Alpha fold is actually consists actually of two main neural network blocks, the former where it gets unsimpled the MSA representation and the pair representation. And actually the MSA representation tries to populate and optimize the pair representation matrix. Afterwards, we have the structure module where we convert these two types of tensors into a tensor that contains the translations and rotation of the model. And during the learning process, this is optimized.  And of course, for better performance and accuracy, this happens three times there is a reside three recycling steps. After the release of alpha fold, there are several other tools with similar or even better accuracy and performance than alpha fold. However, the problem with this software is that they have a lot of dependencies and mainly we refer to the genetic databases you need at this step here in order to build the input multiple sequence alignment. So, as many labs and researchers in the community, try to use alpha fold in a large scale. This is what we did in our MSA F2 NF pipeline. We were interested in to develop a pipeline that they can take care of all these dependencies, the databases, the alpha fold parameters, in order to be able to get fast and as reproducible as possible models predicted models. And after actually the release of our pipeline here, we, many, many researchers got in contact with us from the academic or the private sector that were interested for a scalable alpha fold pipeline that deals with this problem with the dependencies. And for this reason, we got in contact with NFCOR and Secura Labs, and we collaborated in order to develop such a scalable protein structure prediction pipeline. And here we have an overview of what we already have at the moment. As you can see, we have four modes, mainly based on two sub workflows, the Alpha Fold 2 one and the Colab Fold one. But let's go through this overview step by step. Let's start with the input sample sheet, which is quite similar with the input that is already used in the majority of NFCOR pipelines, but it's a bit different in the sense that here we have a comma separated file with two columns. The first column is the sequence header and the second column is the pass to the FASTA file. For monomer predictions, it is recommended to use multiple entries for each monomer sequence you want to predict. And here you have an example of a FASTA file. And for multiple predictions, it is recommended to use one entry with a corresponding to a multi FASTA file. For example, here you have this multimer and you have a look here at the multi FASTA file containing as many entries as the sub units that you want to predict for this multimer. Once the pipeline checks for the validity of the input, you have two options to sub workflows. The first one is the Alpha Fold 2 sub workflow, where we first, it first passes through the prepare Alpha Fold 2 sub workflow, which checks if you have provided the AF2DB parameter, which actually specifies the path where the pipeline can find all the databases and the parameters that Alpha Fold will use if you have downloaded them. Otherwise, it downloads themselves to the required databases and model parameters. I would like to point out at this point that this is quite computationally expensive, since it has to download around 2.2 terabytes. You can use Alpha Folds in two modes. The first one is the default one, where you just feed the input CSV to the Alpha Fold and it computes the multiple sequence, the input multiple sequence alignment and say it does the model inference in the same process. For the sake of computational cost, we also implemented another mode of Alpha Fold, we call it Alpha Fold Split, where it actually gets the input CSV and the FASTA and produces the input MSA in a separate process than the model inference. And if you think about it, this is quite convenient, for example, in cloud infrastructures, because this step, the AF2 prediction step, requires GPU. So if you run everything, it's these two steps in the default mode on GPUs, it's much more computationally expensive than it costs. It costs much more other than the AF2 split. You can specify these two modes using the standard AF2 parameter here. True for the default Alpha Folds and false for the Alpha Fold split. The second standard, say sub workflow, is about collab fold run, where we have more or less a similar strategy. We have the prepared collab fold sub workflow, where you can specify if you have already downloaded the databases and the required parameters of the model, you can specify the path using the collab fold DB parameter. Otherwise, it downloads automatically the required databases and model parameters. And here again, it requires a lot of storage around 1.8 terabytes. We have two modes in the collab fold as well. The default mode is the collab fold web server, where you actually depend on a web server that can run the database search and MSA creation. By default, this web server is the one provided by MM6 team. But using the parameter host URL, you can specify the URL to your custom web server if you have set it up. In order to specify this mode, the collab fold web server, you just have to use the modes parameter. The second mode is the collab fold local mode. And it's quite similar to the Alpha Fold split mode we have seen in the last slide. In the sense that you first have a process to compute the input multiple sequence alignment. We're using the MM6 and then you have a separate process for the model inference and the proteins structure prediction. Again, you can specify it using the mode parameter of the pipeline. Let's have a look at some more advanced parameters. For example, the huge GPUs parameter when available. As I explained before, it's a much more computationally expensive to use only CPUs, especially in the prediction steps. But you should also pay attention to the configuration profile you are using in combination with the GPU. So was to define the corresponding prediction process to the GPU to or machines you have in your infrastructure. For example, we have the in. We have in the github repository of the pipeline, a CRG institutional profile that we are using at the moment so we can have a look. Then with the outdoor parameter you can specify the output directory. This is this applies for all the NFCore pipelines. The specific alpha for two options, the full DB, the full DB's parameter where you can select if you want to use the full databases for the first part of the sequence search and the creation of the MSA, or you can use a reduced version of the databases, which means that the pipeline will run faster. But with the beat of trade off in the with the accuracy of the produced model. The model preset parameter where you have to specify what type of prediction you want to do and which model to use. For example, we have a three monomer models. The default is this one, the other two are actually provided by the alpha for two team for reproducibility purposes, this one was used in the cast competition cast 14 competition for example, or the monomer model for multiple predictions. Regarding the column fold specific options again, you can specify the model type. The alpha for PDM, which is the default for monomers and to a multiple models. The, the most improved version is the default version to you can also specify if you want to use PDB structure templates or not in the first step, where you have where you populate the pair representation matrix. And also some more specific and detailed description on the parameters available at the moment in the corresponding web page of the NFC or protein fold pipeline. Regarding the output here you can see the three structure let's say of the produced output. Alpha fold, you have an alpha fold a directory and one more sub directory with the sequence name you have provided in your CSV file that contains the computer MSA is the unrelaxed and relaxed structures the rank structures. And the role model output some meta data and of course timings. And the, the first ranked model that you probably is what you want to use in your research that contains as well. The PLDT scores, which is the confidence metric used by alpha fold per residue. And another directory that contains the symbolic links to the downloaded databases and parameter files. The same applies for call up fold where you have an out, an output directory, depending on them, the model you have selected call up fold web server or call up for the local that again contains all this information we have explained for alpha fold, and the symbolic links to the downloaded databases. Of course, as in all the NFC or pipelines, there is a directory with the pipeline info execution trace files, and so on. And the next steps. We are now at this point that we have to set up and run the AWS full tests in order to create the first release of the pipeline. The first release is we're planning to want to add more open source protein structure prediction tools, such as open fold, or even a newer generation of prediction tool tools such as ESM fold, or omega fold that use protein the language language models. And for this reason, they are more or less in order of magnitude faster than alpha fold call up fold and the but without losing accuracy. In fact, they, they have the same levels or even better levels of accuracy. We're also interested in incorporating more advanced software for product in product interaction, such as fold book, because there are plenty of researcher in the seeding predicting a advanced multimers. And moreover, to add to solve bug fixes and add more optimization upon request. We are very, very open to contributions and ideas in order to improve even more of the pipeline and adapted to the needs of the community. So please do not hesitate to contact us and propose or contribute to the already existing repository. At this point before finishing I would like to thanks my colleagues, first from the Notre Dame's lab, Jose Spinoza and Luisa Santos, that are contributing to this pipeline, as well as Secura labs and especially Harsil Patel for all the guidance and the help during the implementation process. And the collaborators from the Interline Therapeutics, especially Norma Gouda and Walid Osman for testing the pipeline in the clouds. So thank you for your attention. And I would be very happy to answer if you have any question. And that's it, thank you. Thank you very much for the amazing talk. So I will allow everyone to unmute themselves. If anyone has any questions, please let's go. Otherwise, I think I have like one question. So at the moment you just have like alpha fold two right and you're planning to add like more, more tools, but not in this first release but in the coming one right. Exactly. Yes. And yes, and I assume that's the main issue with like having more tools is that it's a lot of databases that you need as an input. Exactly, exactly. That's true, because each tool uses its own databases, let's say. So you need a lot of storage to be able to test everything or even to compare between tools. May I ask along this line. So you basically retrain the model every time you run the pipeline or at least like every time institution retrains their model from scratch or do you use pre-trained models? We use pre-trained models. We just download the already provided models by alpha folder, column folder, let's say. And it still takes these huge databases. Yeah, yeah, yeah, yeah, yeah, yeah, because this is separate from the training process. These databases are needed in order to create the input multiple sequence alignment. To actually have this homolog, all this bunch of homolog sequences in order for the model to be able to find all the correlations, the interest in the correlations and form the final model. I see. Thank you. I think we're good with the number of questions. So, thanks again, that was like an amazing talk. I'm super happy to have learned more about it. And I'm hoping to see this release like coming. So thank you all again, everyone.


Sister-in-arms, let us pause to reflect Lord Patriot, Submariner, and Superintendent Barb as a shipmate during my war patrols. All right, I'll meet you in that parking lot. That's when we decided I'm right. Lieutenant McFadden, now inherent. I now recognize our new brother and sister-in-arms. Let us pause to reflect King's Great Days. Yes, it's Marquette Lavec, and Marquette M-A-R-Q-U-E-T-T-E, Lavec L-E-V, as in Victor, E-Q-U-E. Definitely, it's such an honor to join such a long tradition of Submariners finally. I think finally when you get wearing the dolphins, it's such a long tradition of amazing men that have fought in the submarine world. So finally getting to join those ranks just feels incredible. Obviously it's a huge honor, but quite honestly I just go to work every day and do my job just like my male counterparts have. So it's a huge honor, but at the same time it feels good to finally be a Submariner. Definitely, so in order to qualify for our dolphins as officers on board, we go through about a year and a half of training before we show up at the boat, and then when we show up on our boat, we're required to qualify both in the engineering field as engineering officer watch as well as all the forward qualifications. So we're pretty much required to know all the systems on the entire boat in order for the captain to certify that we are qualified to stand officer of the deck. Right, I mean the whole process of getting the dolphins is incredibly rigorous. There's so much information that you have to learn and know, and just really being in it every single day and working and a couple hours every single day learning all the systems and the qualifying. So it's definitely a very rigorous process. Right, so one thing I love about the submarine force is that immediately when you get on board you're given a division. So I was given a division of about 12 guys, and I love the fact that I get on board and I can just start leading right away. So now that I'm qualified, I'm definitely qualified to stay on the watch, but I've been doing, you know, all the hard work since day one's getting on board, so. Mr. U.S. Spalman Gold, Chris Nash, H-R-I-S, N-A-S-H. Yeah, so Lieutenant Lavec worked for about a year, a little over a year to get that submarine qualification. As they progress through their qualifications, they'll begin with engineering officer of the watch, engineering duty officer, where they'll qualify in the propulsion plant as supervisors. Once that is done, then they'll work forward. They'll qualify a contact coordinator, surfaced off to the deck, submerged off to the deck, and then finally once they've demonstrated they can operate the ship forward and aft, and also understand the war-fighting aspects, then they'll get qualified in submarines, which is what we celebrate today. The significance of this event is really a culmination of her apprenticeship program onboard the submarine. The expectations are today much higher for Lieutenant Lavec than they were yesterday, because over that period of year, she has demonstrated not only technical knowledge, but also technical competence and leadership competence. So the expectations are no longer that she'd be an apprentice, but that she'd be a leader, and so I have great expectations that she'll do a great job in that role. The junior officer tour, initial tour is about a three-year tour, a little less than 32 months, and so what this means is for the remainder of the next two years roughly onboard, she'll take ever-increasing positions of responsibility. She'll take a greater and greater role in the planning and execution of the war-fighting of the ship, and we have great expectations for her in terms of taking that next step and have, based on her talents and technical competence, placing her in some fairly substantial positions over the next couple of years. Yeah, to me the significance of this event is, you know, every time we qualify an officer or an enlisted person in submarines, that's significant, because as I mentioned earlier, it is a testament both their individual hard work, but also the hard work of the crew to get them to that point. This is significant specifically for Lieutenant Lavec, because it represents an opening of the aperture for technical talent coming into the Navy. So for officers, we now have twice the number of people that we can select from to take those positions of leadership, having now females qualified. For me, it's also a great thing, because I think, and I look at my daughter, and I say, you know what, honey, you can look at Lieutenant Lavec, and one day you could be a submarine or two, just like Daddy. Well, to me this was a great day at a great location. The weather was perfect. We were able to do it here at World War II Memorial, which I think is appropriate, because any time that we do one of these ceremonies, we look back on our past, because it helps to find who we are today. So it's appropriate, because not only does it show that she has taken those steps and talks about what we did in the past, but it also shows what the future of the submarine force is going to be and how wide that aperture is for anyone. Fair and equal treatment for all is part of our Navy ethos, and I think that Lieutenant Lavec represents that well for the Navy and for the USS Wyoming. Is there anything you would like to add, sir? That's it.


Okay, you guys, so I wanted to show you one of my funniest videos how to get your man to give you money. I Have so many funny comments from guys on this that are so mad. Why are they mad? Because it works. Okay Every week or every couple of days I will get a man who finds this video in comments that I am disgusting and I'm a manipulator and I'm this and I'm that Why because obviously those tricks work. Okay, now if you haven't seen this video go watch it It's on my she risk of a channel how to get your man to give you money It's on this channel and I'm probably gonna link it but The way that I use to get man to give you money is very manipulative and it works Okay, the the title is not how to morally get your man to give you money It's just how to get your man to give you money point blank now a man asked me how do I get a woman to give me money? So I'm gonna answer that.  All you got to do is ask I mean most of the women that come to me have this this problem You know, they have it a man who doesn't work Who they support and usually the man looks way better than the woman Has many options, but she's just to lay on her couch and in exchange for that She gives him money so Yes, that is how you get money from a woman. Just look better than her and have many options. Okay, I Will make sure she have low self-esteem as well. Okay, so moving on. Let's read some of these comments This lady is effing disgusting. I would not give her one even if her pee was I Can't even look I Don't even that didn't even make sense. I'm sorry, but y'all can read it He does I don't know if English is his first language because he used like too many curse words in one sense That didn't make sense. Okay Someone's asking me if they can ask for advice But yes go watch this video and you will be laughing as well cuz y'all know I like, you know keep things humorous But someone says look women are leeches in general Emotionally financially mentally or physically. I'm glad men are wicked. Well. Yes women are leeches, you know, we're supposed to have to depend on Someone to do things for us Why because we can't do anything for ourselves, right? Yeah, well if you think about it men are the real leeches because They're actually in our wombs for nine months leeching off of our life force so if you know if We feel like we need protection and you know a man to provide for us if that's being a leech. Well Physically you are the leech first and got here from You know leeching off another life form.  Let me click on it People hate this one too. Y'all It's called profit off the power of the peace Okay, this will tell you how powerful you are. Oh look. I look like a vampire on this one for real Yes, my leech qualities in my vampire qualities are coming through Let me pause this Okay. Oh look they both be outside Yes, the profit off the power of the P. This is how most women who are smart You know build themselves up and Not wear themselves out. They use what they have to get what they want because most women Do it for free Without anything in exchange. No house. No cars. No relationship No ring.  Watch this video too. This is a good one Recommend it. Okay, man. Hate this one too because they know it's true Okay, y'all I get Okay, this one is how to Finding data rich man. I get comments on this every day actually this one I get every day Why because people are mad because women are not Trying to find the mediocre man anymore They want something spectacular So when they can brag on you know what I mean?  They're saying oh well You don't look that good. How can you get this man with money? Dada, I can look good if I want to you know, which I'll see on the video. It's not my best Yeah, I'm looking crazy right here But that doesn't mean that's how I look when I go out to shoot I had just got done swimming I threw on the quickest makeup job I could do just so I can get a video to y'all and then y'all Some people judge me on When I made this video, it's not about how it's about the content of the video You think if I was really going out to find someone with money, I would look like this Okay, no But Yes, this is funny And I have seen women that look worse than me marry men richer than me. So what the heck, you know It's not about looks. It's about Mentality, okay. It's about game. It's about supply and demand. That's what it's about. It's about preference It's about taste. It's about Chemistry and what you have in common. You know what I'm saying? Oh, this one used to get a lot of This one used to get a lot of slack back in the day How to tell a fake baller from a real financially abundant man Okay, y'all go watch this one if y'all are newly to dating if y'all are trying to date men of means Go watch this video to make sure you're not dating a fake baller because there are lots of them out there who pretend Okay They have bad credit nice clothes nice shoes nice car But you walk into their apartment and they have nothing but a car table and folding chairs and a mattress on the floor Okay, those are the fake ballers, okay So get it together Let's see I'm just making this video because I know I have a lot of new viewers and I'm trying to point out the videos That I like the one that gets the most attention from men The most anger so that you know, which ones are the truth, okay? Oh, this is a good one for women that are too nice, okay Okay, so this one is called nice women finish last, okay Nice women finish last if you're too nice men will walk over you.  They will do all the time types of things now This is not saying be a control freak Or too possessive this means when they mess up You don't treat them like they didn't do anything wrong You actually go out and do something even worse or you ignore it and or ignore them all together and replace them You don't you don't keep asking why why why do you do this to me?  Oh, no reason. I thought we were just you know Cool, you know if you can do that to me. I thought it was okay for me to do to you my bad So yeah, being too nice will backfire on you every time. It's not being a good woman Good wife good whatever material. It's being good Dormat material. Okay, you ever heard of the terms You ever heard of it? There's a book and there's a term or a meme or something like that that says Men love bitches and that's so true.  I know and they're men. They love my channel men more men Love my channel because I'm honest and they're like it's refreshing to see how honest you are I know women are Naturally materialistic they seek comfort and blah blah blah blah blah but they don't talk about it a lot because Society has made women shamed of their natural Feelings so I'm here to say yes women do seek comfort in lifestyle the man Who can provide the most comfortable environment usually has the best woman? Okay? As far as their standards are concerned The man who can provide the least lifestyle in the poorest environment will have the least desirable woman That's just it. Okay, and by desirable. I don't mean looks. I mean She'd you know, she's not Very Desirable of herself. She has low self-esteem. She settles for less.  What's what's worse, you know, it's better to be a gold digger upfront than to surprise them later on Okay Let's see another good one. Let me show you guys. Oh Oh, a lot of people want Oh, this is a good one Listen to men's insults They hold knowledge. Okay, so this one is about If a man is going to troll you or insult you in any sort of way Listen to what they choose to insult first. They they feel like they're gonna insult your weakness But really it's something that they admire about you and how strong you are and able to Be confident in yourself So for example, a lot of people a lot of the guys on here the MGTOW guys will attack Me saying that I'm fat Well, I don't look in the mirror every day and see bones sticking out So I'm obviously aware that I'm not skinny. Okay, but that has never stopped me from getting anything that I want in my life In fact, this actually helped me why because I don't use it as an excuse, you know, I use it as Okay, so and You know, it's just it's just part of you know, my body who I am, you know some men like it a lot of men like it actually So For example, like Yeah, see this is what this is one of my wedding pictures out. How was not skinny when I got married, okay? So Y'all act like being skinny is like the fountain of youth and the holy grail is not Believe me at most my friends Are not thin and they're all married to men with money. Okay, my sister is not a thin woman Her house is beautiful and gorgeous. Okay Um So I don't understand why people like why guys think that women have to be skinny to get a rich man That's just not true. I know about to I have men in my input. I could show y'all I got me in my inbox right now that have money trying to you know Trying to how I'm like, okay, I'm already married, but thank you You can donate to the channel and whatnot, but you know, I'm serious I Have crazy people asking me to take pictures of my foot and that they would send me a hundred dollars. Okay This is not a game. Okay ladies if you feel like You are Not able to get a man.   Okay, so why do you why do you think that is? Because they have high value on themselves and anything that has high value is sought after And supply and demand You know, okay, so here having excess or erotic interest in oneself and one's physical appearance. Okay The narcissist's actress egotistical Self-obsessed Conceded and self-centered now Loving and self-admiring. Okay, so anyone who is this Will never run into a man who's gonna mistreat them will never run into a broke man. Why? Because they can't Mentally cope or deal with one. Why because they love themselves too much. There is there's a article about Donald Trump And they say he's a narcissist and now he's the president, you know Narcissist leaders most leaders are narcissists.  Okay, there are lots of men who? find it a Turn on to actually worship women.  Okay, and how to bring it out of them is to worship yourself and men Will follow what you do. They will mirror you if you worship yourself ladies The man that's in your life will start to do the same They will put you on that pedestal if you're up there already. They have no choice but to look up, okay? So, yeah, this is my Psychological dysfunction. I love myself too much. I'm obsessed with myself too much. Obviously, I'm self-centered. I'm egotistical and Just by doing that and being who I am I Have someone that can mirror that for me now If you're a sociopathic narcissist, that's the difference This means you will do emotional harm To get people to worship you. Okay, I don't do this. I just I just admit this look I'm a narcissist. You got to take it or leave it a lot of people will be impressed that you can self-diagnose yourself and Use it to your advantage. Okay, ladies.   Okay? This scares the boys away. The men will be attracted to this. Okay And as far as like if you have a narcissistic partner and they're very self-absorbed The best way to deal with them is to be the exact same way to also be a narcissist. You know why?  Don't I to the other one and the other one won't even say they look good They'll say I look good too. Don't I look better. It's gonna be funny.  I'm just giving y'all some clues and tips because someone asked me to make a Video about a narcissist and what if you're in a relationship with a narcissist won't become one the best way to defeat your Enemy is to become them. Okay Become an a narcissist. Okay, and they will show you the way. Okay Now psychopathic narcissists narcissists and sociopathic narcissists are dangerous Okay, they take it to a new extreme of violence and crime. You just want a regular plain old narcissist. Okay Or you want to be one yourself Okay, the more it's just like building a business the more you promote your brand and have people talk about your brand and put your brands picture up on, you know Social media the more people are going to click on it and show interest in it and the higher they will pay for your product If they see you advertised all the time and if they hear good things about you and if you say good things about yourself or your company They're gonna buy from you versus someone that puts out one ad on Craigslist talking about Come by my shea butter, you know versus shea moisture The best ingredients ever, you know Who well who are you gonna purchase from? You know what I mean? So ladies you want a rich man?  All right. I will see y'all a little bye


Daily Tech News show is made possible by its listeners, thanks to all of you, including Erwin Sturr, Ken Hayes, and Phillip Shane. Coming up on DTNS, Dr. Nikki is here to explain that big advance for fusion energy you've probably been seeing headlines about, and to calm our fears about general AI replacing humans, even me. This is the Daily Tech News for Tuesday the 13th of December 2022 at Los Angeles I'm Tom Merritt. And from Studio Redwood, I'm Sarah Lane. Brand new from Alabama, I'm Dr. Nikki Ackermans. And on the show's producer, Roger Chen. Yes, we made Dr. Nikki move to Alabama so that we would have more states represented. So that my bird box could be better. And so that we could A-B test your bird box against New York City. No, real quickly, you've got a new job, is that why you're there? Yes, I am a newly assistant professor at the University of Alabama. Well, congratulations. Very cool. Thank you. Thank you. Yeah, congrats. All right, let's get into the quick hits. The US Securities and Exchange Commission has charged FTX co-founder Sam Bankfriend-Freed, we'll call him SBF going forward with fraud for allegedly moving customer funds from FTX to another of his company's Alameda Research. The US Commodity Futures Trading Commission filed a civil lawsuit against SBF for the same reasons, and the US Attorney's Office for the Southern District of New York brought eight criminal counts, including alleged wire fraud, securities fraud, and money laundering. SBF is alleged to have used customer deposits to fund risky investments in Alameda Research, causing both companies to go bankrupt. Authorities in the Bahamas arrested SBF Monday, and he will be extradited to the United States. I just love the way you said, with fraud. I mean, we all kind of knew. Yeah, right. Yeah. What are you going to do? Let's call it fraud. US Senator Marco Rubio of Florida introduced a bill meant to ban TikTok in the United States. The bill would block transactions with any social media influenced by the governments of Russia or China, and that would, of course, probably be applied to bite dance and TikTok. In 2020, the United States Government's Committee on Foreign Investment in the United States ordered China's bite dance to divest itself of TikTok in the US. That never went away. They never made them do it, but talks have been ongoing ever since to address national security concerns. Apple CEO Tim Cook has been visiting Sony manufacturing facilities in Japan posted on Twitter that Apple has partnered with Sony for more than a decade to make camera sensors for the iPhone. Apple had never before confirmed that Sony made that actual part, so they're confirming it now. Apple also announced Tuesday has invested more than $100 billion into its Japanese supply network over the next, over the last five years, highlighting another way that Apple is pivoting away from Chinese manufacturers. Yeah, Tim Cook doing a charm offensive in Japan, you know, talking about how much he loves it, posting on Twitter, all kinds of interesting things going on. The US and the EU used to have an agreement called Privacy Shield that allowed companies to move data back and forth between the two regions while maintaining the privacy protections of the citizens in those regions. But challenges to those agreements have meant that the companies have not had that protection for years now, instead having to each negotiate their own what are called standard contract clauses or SCCs or alternatively just stop transferring customer data between the regions, which increases the cost of your business quite a bit. However, the EU published a draft approval Monday to reestablish a framework for all companies. And this is because in October, the United States began implementing new safeguards on how its intelligence agencies can access such data. So under this new framework, EU citizens will be able to appeal data handling decisions to an arbitration panel. And the United States has agreed to limit intelligence agency data collection. However, lawyer Max Schrems, who is the person who successfully sued to strike down previous Privacy Shield agreements, has indicated that he finds this new agreement insufficient as well. Oh, boy. So it's not over. Probably not. Amazon announced new features for its digital voice assistant gestures on Echo Show devices. Those are the ones with screens and a camera. Let a user interact with the device using just their hands. The only gesture so far, pretty limited, is the ability to hold up your hand palm toward the camera to dismiss a timer. I actually tried to do this earlier and it did not work. Did you turn on the setting though? Uh, no, because gestures are only on by default. So I didn't. They're not on by default. They're, they're not on by default. Yes. You have to turn them on by default. Which I did not do. There's also a new text to speech option that can read out the title of menu options on the screen and consolidated captions turns on captioning for calls or videos or voice responses across all Echo Show devices on a single account. I can't wait to turn this on and stop the timer with my hand. All right, folks, you've been hearing a lot about fusion in the headlines. Dr. Nikki is here to tell us all about it. I'm so excited. I'm the person who gets to talk about this on DTNS today because this is a huge breakthrough. So Tuesday, scientists at the National Ignition Facility at the Lawrence Livermore National Laboratory in San Francisco announced that for the first time ever they achieved a net energy gain during a controlled fusion experiment. So if you've been following fusion science for a while, you know that this is a huge deal. And if not, I'm going to break it down for you a little bit more. So nuclear fusion is the type of energy that powers the sun. Understandably, this is something that we want to harness down here on earth because it would be a great source of green energy if we could make it work. It also doesn't emit carbon dioxide or long live radioactive byproducts like from the nuclear power that we currently use. So researchers have been working on decades to try and achieve stable fusion. And this happens when two hydrogen atoms are mushed together under really high pressure and they fuse and they form a helium atom.  It, however, takes a lot of energy to actually cause this reaction and even more for it to be sustained. So the hope is that in the first round of fusion in a controlled laboratory environment, enough energy is released in the form of heat to trigger a sort of chain reaction with another fusion and so on and so forth, which would be a cascade of fusion reactions that would run until your said fuel runs out. So so far, the amount of energy, sorry, the amount of energy necessary to achieve fusion has outweighed the amount of energy coming out. We've been putting a ton into it, but the results haven't been, you know, as good. Yeah. So you put 10 in, but you only get nine out. That's not. Yeah, or even way less. Right. And, you know, we always hope to get to the point where we would get more out. But some people weren't even that confident to reach this reaction phase, which is called ignition. However, in August last year, the NIF got really close to that threshold. They generated around 1.3 megajoules of electric of not electricity, but energy against the input laser power of 1.9 megajoules. So it's close. But on December 5th of this year, they actually achieved ignition. So yeah, clapping. Excited. Yeah, it's a huge. It's a big deal. It's a really huge deal. And all of this was achieved inside a teeny tiny capsule that's like this big, the size of a peppercorn filled with hydrogen isotopes and a type of fuel made of deuterium and tritium, which is a type of fusion fuel. And this type of fusion is called inertial confinement. So the fusion reaction was kickstarted at 2.05 megajoules of energy coming out of the largest lasers in the country, which is also pretty cool. And they were pointed at this tiny capsule and then they got 3.15 megajoules out, which is 50 more percent of what went in. Nice. This is the accumulation of over 60 years of research across the planet. And obviously it's a huge step towards proving that this type of reactor can actually generate energy. But this was only a proof of concept. So maybe, Tom, you can tell us more about what are the what's going on in the future. Or maybe your dog can tell us more. Ray would like to step in and tell you. But Ray, I'm going to take this one because I agreed to be the downer and explain about the hurdles. It's important to note that the experiment produced more energy than went into the experiment. That's not the same as producing more energy than was used in the experiment overall. These lasers are very inefficient. It took more than 300 megajoules of energy to create the 2.05 megajoules that the laser put out. That's not very efficient. It's still not a headline, though. Yeah, yeah, exactly. It's still scientifically important because what we showed, if it checked me if I'm wrong here, Nicky, is that if we had a perfectly efficient input that you could generate net positive energy. No, that's correct. Yeah, so the science has been demonstrated. It's a matter of now the practicality of making those lasers more efficient. And to do that, we'll have to see if we can repeat the process with more energy efficient lasers. They admitted at Lawrence Livermore, they were just trying to make this happen. They did not use the most efficient lasers that they could use. These are actually kind of old lasers. Or there might be an ability to do this similarly with a different method, like the magnetic devices used at JET or Eiter-Takamak reactor in France. Those are different ways than the what did you call it? The peppercorn. Yeah, different ways than the peppercorn. The constrained nature of doing the capsule, they do a different thing with the magnetics. But the point is, no, this is not going to lead us to diffusion any time soon. But yes, it's huge. And I think it even has a psychological effect to kick off people going, oh, this isn't theoretical anymore. You actually did it. You made more energy come out than you put in. Now we just need to work on how to make that better. Make it like you said, work in a chain reaction so you can have a sustained reaction. And that gets people excited enough to really apply their brains to this sort of thing. Yeah, and to the point that scientists are still warning like, listen, this is really exciting, but don't get too excited. We're still a few decades away from, you know, powering our houses and and businesses with this kind of reactor.  Like we need to work on other stuff. We're not going to sit here and wait for fusion to work. So they realize that this is such a big step that people are getting hopeful. But, you know, we still need to focus on other things. But like you said, this is a huge first step. And like we can make lasers more efficient. Like we're better at that than making fusion happen. So right. That was the harder problem. Yeah. So it's super exciting, no matter what. Yeah, I agree. Sarah, what do you think? Well, for anybody, I don't know. I mean, as the layman in this conversation, Nikki, I mean, for anybody who's saying, OK, well, as a consumer, maybe nothing changes tomorrow. But what does change in the next five to 10 years?  And in 25 years, they're able to run a fusion reaction in a chain and cleanly and safely produce energy. This could replace most of the things that we're using now. It's better than nuclear fission, which creates radioactive waste. It doesn't release carbon like all the coal burning and gas burning that we do. So we could actually have clean energy and a different clean energy than what we have today with windmills and things like that. So but that's a lot of ifs to get there. And I don't know if that's going to impact like prices. I don't know. The lasers are really, really energy consuming to start up. So what does that do price wise? I don't know. But yeah, that's the other part of this. It's the practicality of of making the laser, the input more efficient, whether you're using lasers or whether you make a tukamak work or something else, you're going to have to bring that down. And then there's cost. That's a whole separate issue. You might be able to do a really efficient input that makes fusion work and generate more electricity than is even used in the entire system, which would be great. And it costs a billion dollars a month to run. And that that's not going to be very practical economically. So there's that side of it, too, right? Yeah. And like you said, with the magnet one, for example, that's another possibility. I know that those require even more energy than the laser one. So it's kind of a race to see who's going to go first. But I'm I'm here and we're going to keep watching it and covering it on the show. I'm sure. Yeah.  And there was it was a big long step before you could actually put people on a rocket. But eventually we got there and then we sped through putting people onto the moon and then having reusable rockets and shuttles and all kinds of things. So it's, you know, we're we're early on in the process, but we're in the process where we went from we think this could work someday to, oh, we made it work. Now we just have to make it practical. It's from maybe not in our lifetime to maybe in our lifetime. So yeah. Yeah. Yeah. And then what are we going to do with all that helium? Yeah, we don't need it. Well, what we do need is for all y'all to give us feedback about the show. One way to do that is in our subreddit, submit stories and vote on them at dailytechnewshow.reddit.com. News about AI. People use it as a catch all for everything these days. But you see it in the headlines. The news comes fast and furious these days. TechCrunch just now reporting SensorTower data showing that that Lenza AI app, the one that is responsible for all the weird looking avatars people use on social media, so many of them. I have a few myself. It not only is continuing to top app store charts, but it's followed by several imitators, including three other AI art generators in the top 10. Do not pay demonstrated a chat bot that can interact with customer support chat at companies often handled by actual humans to negotiate better rates on like your cable bill or your phone bill or even cancel service. Those are stories that just came out today. You could be forgiven for thinking that general AI, the kind that people worry about taking over the world is just about here. So, Nikki, can you explain, first of all, what we mean when we say general AI and why I shouldn't worry that Skynet is going to arrive soon and kill us all? Yeah, Tom, I can I can explain. And I've got to say the reason this this topic came up is like it's a debate in my family all the time. They're like, no, I'm sure the robots are coming. Like every time something new comes out, they're like, they're going to get us. And I'm like, well, first of all, general AI is generalized. AI is is like really real intelligence. And and the fact that a computer could have its own thought that's not caused by humans inputting parameters or a data set. And we are not there yet. So the main problem with AI right now is that it's unreliable in unusual circumstances, because we are training them to be good at repeating things that have already been done. It can't really come up with new things very well. So it has a prompt. It follows the prompt based on the mathematical likeness of whether X or Y answer is more correct according to its data set. An example would be with Dolly, the artist in quotes, AI. And Dolly isn't able to tell the difference between the phrases an astronaut riding a horse and a horse riding an astronaut because it makes the mathematical assumption that one of them is more likely to be used than the other. And that's something that a human mind obviously could very easily tease apart. Yeah, we generally don't see horses riding astronauts. We just kind of got know that, right? Yeah. Yeah. Don't need to be trained. You could draw that if you wanted to. Yeah. So and then to follow up things like chat GBT that we've been talking about on the show a lot, they seem impressive, but it's kind of almost only a surface level. And that's because as with most of the machine learning processes, it's based on a data set. And this data set could be super large, but it's not infallible. And the calculations used to produce content can only run based on the parameters it's given by the developers. So original ideas couldn't be original if they followed a pattern. And that's why these things, these AIs can't come up with original ideas. And all the prompts created by chat GPT for like film scripts sound like an infinite iteration of Hallmark movies. They're kind of basic. Yeah. Chat GPT, you can almost always see where it got its ideas, even if it's not copying them, although sometimes I've noticed that it just copies them, but you could you're like, oh, it's taking this piece, this piece, this piece. If you were to say to chat, chat GPT, come up with an idea for a film script that has never been done before. It's going to fail. It's going to come up with something, but it's not going to come up with a film entitled never been done before. Right. Or something like that. Movie. Right. And it'll be a film. It will be a different text than has ever been done before. Possibly. But barely. But it won't be a new idea. That's the difference, right? If I'm getting this right. Yeah. And the problem, and I think what a lot of people forget is that the data and the parameters that are restricting this data are human generated. You have to tell the machine what to do. So the AI is automatically going to be biased towards the same human biases that the human programmers are. And we've covered biases extensively on a previous episode. But, you know, you can imagine that if you have a huge data set of human faces and you want it to recreate a certain face, it's going to under-represent whoever wasn't able to submit their face into the data set because they don't have an internet connection or something. Just things like this, just kind of snowball with AI. Yeah. So AI, machine learning, all these things are advancing at a huge breakneck speed and at a much faster rate than regulations are able to keep in check. We already know that our laws are slow, but compared to something like how AI is advancing, they're like snails. And so it's really important that we keep them in check and we keep kind of on our minds the benefits versus the drawbacks that we can predict for AI user cases. We don't do we need an AI that tries to clone dinosaurs? Probably not. Like we can focus them on things that are useful. And do you, Nikki, you know, as somebody in the science community feel like you're getting pushed back on things like this? Um, no, I mean, I, I feel like my peers kind of know that like right now it's not a big deal. There has been an AI that writes research papers and it's very funny to read. It's, it's like, I think one of the questions they submit to it was do vaccines cause autism? And the AI said yes and no. And so it's like, because it's probably found data on the internet that said yes, right? It said both. Yeah. Yeah. So I'm not threatened yet. That's what I'm saying. And the cool thing about these AIs, and I say AI generally, but yeah, it's machine learning and algorithms. There's a lot of inflammatory headlines that say that, you know, it's the end of the world, et cetera. But there is a silver lining. These products are in specific instances really, really useful for stuff that like we're too lazy to do. We don't want to do really complicated max math problems over and over and over. And my favorite example is actually one from Scott Johnson and another colleague on the show who says something like Dolly can help create character inspiration for artists instead of trying to find the perfect Google image. Just type it into the prompt and then you can draw from there. Yeah. And no one thought that was going to be what it was going to be used for. I think that's what we're experiencing with a lot of these different machine generated tools is that at first they're so impressive and maybe a little beyond what we expected that we get freaked out or easily impressed or both. Then, like we have with all technology, it's pretty soon we get used to it and start picking it apart. Like, oh, it's bad at this, doesn't do this well.  You know, that's what we do. And so I love that humans do that. I think as we become more familiar with these tools and they come out in the public more often, we'll become less freaked out about this. What I'm curious is what are the signs that something is a generalized AI? Like and granted, as you've I think well demonstrated, that's probably farther down the road than fusion reactors. Like, what would it look like, though? What would it do? I think if it gets to the point where the majority of the population can be tricked by it or even people who just aren't aware that it's an AI can't tell if it can pass its own by a more specialized Turing test, like not the Turing test, but like a more specialized Turing test. If it passes it, then we need to start thinking about that. But we're not there yet. For now, it's it's not like all the AIs that we have would need to be all grouped together in one like mega AI and then be better and then we could get closer. But they all do very specific tasks. Right, because the chat GPT won't play Stratigo very well. But the Stratigo AI is not going to be able to create art and the lens AI can't create a film strip. But even if you combine them all together, you're still going to find gaps of like, well, this can't do that. There's there's the other thing I want to do. And in general, I may not do everything well, because humans are general intelligence. We don't each of us doesn't do everything well, but we would be able to, you know, conceive of it and come up with a, you know, an approximation of an answer for anything. Yeah. And we can also go super philosophical and be like, well, what is intelligence? Like maybe this is AI's own form of intelligence. Like chickens are intelligent, but they can't they don't have thumbs. So they can't use tools. So like, what are we, where do we want to go with this? Well, and that would be really interesting is if an algorithm at some point demonstrates something that looks like intelligence that doesn't look like anything humans, dogs, dolphins or anything. I'd be excited for that. Like maybe that could be super helpful. Yeah, yeah. Unanticipated consequences could be positive. They don't look at the silver lining. Yeah, yeah. Well, speaking of maybe something that nobody asked for, Jeff Bezos's space tourism company, Blue Origin, will release a kid-friendly animated space adventure series called Blue Origins Space Rangers produced by companies, Genius Brands and SMAC Productions. But our release is DVD. They've just said they're new in it. The series will feature the voices of Bezos himself. And good morning, America co-host Michael Strahan, who was a passenger in December of 2021 on Blue Origin NS 19 on a 10 minute space flight. Bezos took his first ride to space in July of 2021. According to the press release, Blue Origin Space Rangers, the show will also include voice talent and other guest stars like junior astronauts, celebrities and adult leaders. So what I'm unclear on from what they've released about this is if Bezos is playing himself or not. Also, what's an adult leader? Oh, I mean, I don't know that to me, that just means like. Camp counselor. Yeah, they're going to they're going to pick politicians and heads of the G.O. Sure, like that. I sure that that's just that's a catch all term for important people who aren't celebrities or like a big weird ego trip. I'm sorry. I would like to say I'm an adult leader. OK, well, sure. Yeah, I mean, that was just in the Yahoo story. I don't know if that's the the actual text that Amazon is using to make it in the press release. Yeah, yeah, I can't hire like actual astronauts to do this. Why does that? Well, they are. They're going to have they're going to have actual astronauts in here. And in fact, Michael Strahan qualifies as an astronaut because he's done it. He's done it. I just like the fact that cartoons used to promote selling toys. And now they promote aerospace industrial projects. Do your parents want to go to space for three hundred thousand dollars? They can't instead of bugging mommy and daddy to buy that toy in the toy aisle, bug mommy and daddy to lobby their Congressperson on behalf of Blue Origin. Get y'all to Mars. All right, let's check out the bail bag. All right. So Terry wrote in about our discussion about terms of service with IA's act are yesterday, Terry said, it occurred to me that a simple solution could be put forth by either a big three group or the Fed having standardized abbreviated terms of services that could then be referenced. IE the general EULA could be a general international EULA. The use terms could be generalized and referenced as such. The liability waiver could also be generalized. Then to invoke each package, there could be a checkbox or a bulletless saying, quote, to use our service, you agree to the federal regulation EULA terms of use or federal regulation liability waiver. The following terms are specific to our product. That is what Terry had suggested. He also says below listed would be this specific terms of service for anything not covered under the regulated terms of service scripts. We will add that to our show notes. Yeah, we'll have the email in the show notes. He's saying below the checkboxes thing, they then every company could have anything else that wasn't covered by the by the the object oriented thing. I think this is an interesting idea, sort of, you know, object oriented. If your objection to this idea is like, well, I don't want the government involved. And OK, let's make it an industry standards organization, right? Similar to USB or something where we have blocks of of EULA content that everybody knows what it means. Everybody agrees what it means. The industry agrees that it protects them. I think the the fact that you would have specific terms of service for every company does lend a little backdoor to them putting in whatever they want, but at least it's less to cover and audit. I don't know, folks, what do you think of this? Let us know feedback at Daily Tech News Show dot com. In deed. Also, thanks to you, Dr. Nicky Ackermans for being with us today. Let folks know where they can keep up with your work. Yeah, they can go to my website is Nicole Ackermans dot com or I'm still on Twitter for now at Ackermans Nicole until it dies. Some I'm going down with the ship. Well, it's not dead yet. But yes, yes, many places to find various folks. So that's where you can find Dr. Nicky very happy to have you with us, though. Also very happy to have a brand new boss and that boss's name is Luke. Luke just started backing us on Patreon. Thank you, Luke. We're so glad to have you. Indeed. Thank you, Luke. Luke is now immediately signed on for the new Patreon merch. If Luke sticks around for three months, unless they're at the production assistant level, all the other levels will get a piece of merch with Len Peralta's exclusive nine year anniversary art on it. So at the various levels, there's stickers, there's t-shirts, there's hoodies, there's tote bags, there's coffee mugs. If you're not a patron already, now's a good time to get started. Patreon dot com slash DTNS speaking up, Patron, stick around for our extended show, Good Day Internet, where we talk about all the things. You can also catch this show. DTNS is live Monday through Friday at 4 p.m. Eastern 2100 UTC. Find out more at dailytechnewshow.com slash live. We are back tomorrow with Scott Johnson and Will Smith, and we're talking PC upgrades. See you then. This show is part of the Frog Pants Network. Get more at frogpants.com. Diamond Club hopes you have enjoyed this program.


Welcome back, folks. We have the Dow Industrialist right now trading down 190 points. You get the NASDAQ off 37, SAP's off 10 and a half, 16 and a half rather. Let's go over to our mammoth to Basel Chapman as we do each and every Tuesday at 20 past the hour. Don't forget, folks, Basel has an outstanding show here. Every trading day, 12 to 1 Eastern Standard Time, also has a great newsletter. The way you get the newsletter, you can go over to our website at TFNN. You're going to see it right under featured content. The opening call with our mammoth to Basel Chapman. You hit the opening call. You hit subscribe. You can get that for one month for $128. You can get it for six months at five, which is $173 savings. You can get it for a year for $995, which is a $541 savings. They all come with a 30-day money-back guarantee. Basel Chapman, what's going on, brother? Hi, Tom. How are you doing there? I'm doing great, man, yourself? Very good. Thank you. Interesting market, as always. Oh, yeah. No doubt, man. No doubt. Come in at the spot. We're into spring, right? We'll see what this market is going to do. Right. So I'm showing the Dow chart, yes, I always do. And actually, just before I do that, I'll just show you the Chapman Wave methodology what we're always looking for. We're looking for four higher peaks. We alphabetize them, uppercase A, B, C, D. The fourth highest peak is D. It can go higher than that, but at D, other things can happen. I always look for patterns. I think the market's made up either of a straight line move up or down, or an arch, or a cup formation. But that cup formation and arch formation can also have a variation, and that's where you get the lowercase H. It looks like a lowercase H, where you come down, make an arch, retest the left side low, whether you break it or you hold it. That's very important. But on the upside, you make it inverted Y. And if you test the left side and break above it, you've got to analyze it. And where are we right now? I'm going to show the Dow again on the left side. The Dow chart, there it is. So there's that inverted Y formation. Made the high on the 25th of February at 26,241. And we pull back to 25,252,000 points, rallies back up again, and it just sneaks above the left side high of 26,241, and it goes to 26,487. So for subscribers over the weekend, I did as much of analysis as I could on the various indices. The Dow looked to me like with that doji candle, that's a little, looks like a little plus sign, in a leg E in the Chapman wave just above that left side high that we were talking about from the 25th. And the MACD was good, but really way, way under where it was back when it hit that high on the 25th of February. Mr. Kasich had run up, but we're showing a little bit of weakness. So on Monday at the open, we shorted the Dow. And the very big issue now for me is this all very well getting into the short position. But I'm looking at the at the market and I'm saying, okay, this is going to be very interesting. Why? The weekly chart is still very strong. We had spoken about this the other day, the XLK, which is the S&P select tax spider fund went within two cents. I mean, how does a market ever remember where it was? Two cents of the of the all-time high back in October, 7627 in a V-shaped pattern goes down 24%. I mean, that's a big deal. But what does it do? It rallies back up again to 7626, a penny below that high, it had to go two cents higher. It would have made a new all-time high. So this V-shaped pattern is really important. And you coincided with looking at the QQQ, look at this weekly chart, there's another V-shaped pattern. That's not as good as V as you can get, too. I mean, not only is it, you rarely see this in the indexes. You can see it in stocks. I've got many charts. I used to call it a Groucher Marks Eyebrows Pattern years ago when I was doing an all-day webinar. Well, I saw one of these patterns and said, oh my, if I put little eyebrows on the left side and I put a big eyebrow on the right, it looks like Groucher Marks and we called it just for fun. But nobody remembers Groucher Marks anymore. Anyway, I said to think of it as a V-shaped pattern and 187.53 on the QQs on the 5th of October plunges 20% to 143.46 late December. And where does it go to? It goes to 185.28. I mean, this is just over two points away from the all-time high. So we've got to always look at it this way. You always talk about looking at ice, gapping over something that is when you store at a previous level. So I'm looking at this and I'm saying, you know, I think there could be a little bit of a pullback here. There could be some kind of digestion kind of buildup steam to be able to go higher. So not all the indexes are the same. Not all the stocks are the same. Look at this, Akamai is a fantastic stock. It's, look how well it's done. There's another one that was sent to me just a few moments ago and I thought, oh, I haven't looked at that for a while now because I can't find it. But it was at all-time highs. So it's not everything. But if you look at the XRT, which is the retail sector, this is not looking too good. So this is a, I like to call it a bifurcated market when you're looking at some sectors that are really doing well and others are doing poorly. That's what we're looking at here. So the Dow, Boeing, of course, is really pushing the Dow down. But the Dow itself was looking a little bit weak. So I feel confident that at least there should be some kind of a pullback here in the Dow, but it's how the others work, whether they, you know, IWM has been lagging. The semiconductor index goes within two points of its all-time high. Another V-shaped pattern. Look at this. Same thing, 1,455 was the March high of last year. And all of a sudden, it gets to 112.90 yesterday. So that kind of power, because it didn't go right through, because V-shaped patterns can be really powerful if they break the left side high, the former high, if you go right through it, unlike a cup, which really is a more determined move, a V-shaped pattern is like a rocket. So stalling here is going to be very important. And I think I mentioned last week that I thought it was a pretty important week for a lot of areas that included the dollar, which had made that pattern. Also a cup formation kind of stalled. But I think it has a chance to get to the 98s at some point fairly soon, a couple of weeks, holding very well if you look at the weekly and the monthly charts. So this is very select. We are still along the IYT, which is the Transportation Index. Another cup formation. The difference here is that when it took out the left side high for 192.27 of February, this time it's gone to 192.78. And the pattern is a little bit more stable than the down. Look at the stochastic, how it's holding steady at 91%. So I think this is very selective. I think that if you long, we are long still a number of positions. I like those positions. I would like to add to them. I just mentioned the grains to you for a couple of weeks now. The grains have been holding pretty well at this point. And we have a fund, an ETF that actually is part of the grain complex. So I like that very selective and very on the downside, I don't want to be saying everything's going down. It's very selective on the downside at this moment as well. It'll gather a lot of strength if the VIX index finally is able to rally and then hold its gains. It's up, it's at 14.32. If the VIX gets to 15, one has to be very careful. But at this point, this is the beginning of a move. And folks, the way you get Basil's newsletter, come over to our website at TFNN. You go into featured content. You can see the opening call right there. Check it out. Subscribe right here right now. Basil, you have a great night, safe night. We look forward to the show tomorrow. Same to you, Tom. Thank you very much. Thank you. Stay right there, folks. Come right back.


Daniel Medvedev vs Alejandro Davidovic Fakina is the first quarterfinal for the men's Indian Wells for 2023. And both players have had to fight to get to the quarterfinal stage. Fakina started the tournament off as the number 23 seed and we're going to buy in the first round. In the second round he take on the Chinese player Wu who got a wild card into this event. This pose no problem for Fakina getting through in straights 6-4-6-1. In the third round he take on the 13th seed Hashinov who had made the semi-finals of the Australian Open only a couple of months ago. But after dropping the second set Fakina regrouped to winning 6-3-1-6-6-4. In the fourth round he take on the qualifier Garin who had just beaten Kasparud in the previous round. But again this pose no problem for Fakina getting through in straights sets 6-3-6-4 to advance to another Masters 1000 quarterfinal. Medvedev started the tournament off as the number five seed and we're going to buy in the first round. In the second round he take on Nakashima who had just beaten John Isner in the previous round. But this was no problem for Medvedev getting through in straights 6-4-6-3. In the third round he take on Avashka who had just beaten Van Zanshub in the 28th seed in the previous round. And this was a tough match for Medvedev after complaining about the court several times he got through in three sets 6-2-3-6-6-1. In the fourth round he take on the 12th seed Zverev who was coming back nicely from his injury. And this was the all-time match of the tournament so far going over three hours with Medvedev eventually winning 6-7-7-6-7-5 to advance to the quarterfinals. These two have only played the two times before with Medvedev getting both wins against Fakina including a win in three sets only a few weeks ago in Rotterdam on the hard courts. If Fakina is going to win this one he needs to rally, rally, rally. He needs to get everything back against Medvedev and really just be a brick wall. He's got great defensive skills just for Fakina and he's really going to have to frustrate Medvedev who we all know is not actually liking being out here in any world. If Medvedev is going to win this one he needs to be patient and he needs to keep his cool. And he also needs to make sure that his ankle is okay because he did roll his ankle in this Zverev match. He's going to have to do a lot of running against Fakina so keeping his head and hopefully keeping his body intact will get him to win. This is going to be a very interesting match and with Medvedev having problems with the court and also problems with his injury with this ankle problem it could be very tough for Medvedev but I'm going to go Medvedev in three sets but very tentative three sets for me. I wouldn't be surprised if Fakina wins this one because of where Medvedev has been mentally lately in this tournament but I'm going to go with Medvedev in three but let me know down in the comments below. Who do you think is going to win this one?