What is up everybody, welcome back to the channel. This right here, you guys, the most important email that you could ever send, okay? That's the title of the damn video. The most important email that you can ever send is the first email. And it's what we call your welcome email. So it doesn't matter what kind of business you're building. If you're driving traffic to a landing page to capture a lead, right? To get people over here to, let's just say, I have a few of them, but let's just say FB ads for beginners. Again, it doesn't matter what your business is.  Stop paying them to your overwhelming crowded website. You gotta start bringing people to one single page, right? This is your landing page. So whatever you're in, people come over here. They click on the button. They enter their name and email. When I click on that blue button, that first impression, that very, well, I should tell you the first impression is the landing page, right? That's the first part. But the first intro, the first introduction, your first freaking, what word can I use?  That is the most important email, man, because the chances are if that first email doesn't appeal to that client or to that prospect, right? If that first, that's why I'm saying when you give out something for free, whether it's a free training, whether it's a free download, whatever, when you give out something for free, it trains that prospect to go look at the email. That's why some people, they'll bring traffic to a web page, to a landing page. They'll have that person opt in. Let's say they're given out a free download. And then right when that person enters their information, they click on the blue button, they go to the thank you page, and on the thank you page is a link to download whatever the hell they opted in for. Some people do it that way, but I do it through email because I wanna already train them. I wanna program them. I wanna get them already used to checking for my email. So whatever you're given, a free video, a free download, a free PDF, some type of free buyer guide, whatever, you wanna send it to the email because that again trains them to start looking out for your email. But what I'm saying is that when you do it that way, it gets the prospect to go check the email right away. And that's what I'm saying is the most important. The very first email is the most important. So all I'm doing with this friend of mine here, shout out to my buddy Kelly, but all I'm gonna show you is how you set it up in Cartra. Like how that first welcome email goes out. It can go out through the form or it can go out through your my communications, which is your sequence, right? So when someone opts into the page, they're automatically put on a specific list. And with that list, there should be an email sequence tied to it, right? An email autoresponder, that's what's gonna automatically go out. Those handful of emails that are gonna go out later, right? Now I can either set up that first email to go out through the form or I can set up that first email to go out through the my communications through the sequence. Now in this case, I'm gonna show you how to send it out through the form. So when they come, this form right here, in fact, let me just open it up. It's this one right here. So this form is what's on any landing page. So when you look at a page, when you look at a page, you have the page and then when they click on the button, that's the form. So right here, I can ask for name. I can ask for last name. I can ask for email. I can ask for phone number. I can ask for freaking date of birth.  But when you're getting people to opt in and it's cold traffic, they don't necessarily know you. Asking for the name and email is all you wanna do. Don't try to ask for phone number. I know so many business owners that wanna capture leads, right? That's why they're running Facebook ads and they just wanna ask for people's phone number right away. Especially real estate agents. They want people's phone number right away. It's like, dude, when do you ever click on anything online and give up your phone number right away?  I'll give up my name and email. If it's interesting, I'll give up name and email just to kind of dig deeper, but that's about it. So right here, you have to put your consumer hat on too, man. Your prospects, people you're promoting your business to, we're just like them, they're just like us. We're just normal consumers, man.  Name and email is all you want, but my point is that this is the form and that form is right here. It's all in my form. So in here, you can create new forms and then you can come over here and hit change and this is where I can add new forms. So look, I can go new filled and I can go middle name, last name. I can ask for company. I can ask for phone number. I can ask for address, right? I can ask for website. I can ask for social media handles, right? So I can ask for whatever I want really, but or I can create a custom filled and I can ask for whatever I want. But in this case, I'm telling you, name and email is all you need. That's all you need to need to know right now. And then once I get that person on my email list, then again, if you have an email sequence, which you should have set up, that's your email auto responder, you should have a handful of emails that are going out automatically, right? That's where you build the trust. That's where you build the like. That's where you build the rapport. That's how you nurture your list. You're emailing those people, value, not just pitch, pitch, pitch.  No dude, it's not just pitching, right? It's freaking value. Start educating your list. Start teaching them stuff that they may not know. And then occasionally some type of call to action. And so if you're in the client services business, which is a real estate agent, an agency, a freelancer, you're actually looking for clients. As you get this list going, it's in the email auto responder where you wanna sprinkle a few emails with call to actions to get them to click on your calendar to schedule an appointment. Because you're either, if you're watching my video right now, man, you're either servicing people through client services, right? You need someone on the phone. You need to grab someone's credit card. You need an appointment. You need a scheduled phone call, right? Or some of you guys might be affiliate marketers or just, or maybe e-commerce. You guys are selling stuff online, right? So if you're, no matter what you're doing, in the emails is when you'll put a link to buy your product. Or in the emails, that's where you'll put a link for your calendar to get people to schedule an appointment. So it's the sequence, man, where you're gonna build the trust. That's where you can get people to schedule an appointment. That's where you can get people to give up their phone number. You know what I mean? Like it's in the email sequence, but you gotta be delivering value.   It's the only reason, man.  So video, when you're building a business nowadays, you gotta be putting yourself out there like this. And I know some of you wanna hide behind the camera, I get it. You know what I mean? Some of you guys may not even necessarily need to be on video, but if you're dealing with people, and I'm talking about clients, not customers. And the difference is that a customer is someone that's maybe buying a physical product, maybe off of your website, that's a customer.  A doctor has clients, patients, but clients, a dentist, clients, a chiropractor, clients, right? So if you're doing any of that kind of sort, man, you're driving traffic to the webpage and what I would be doing, and it's what I do, so if you're on any of my other list, mainly when it pertains to my social media agency, the emails that I send out to those people, I'm on video. I'm on video. And I teach you guys how I do that in my previous videos here, man, how I send out an email, but how I tie it to a page, right, so when they open up the email and they click the link, it takes them to a cartridge page where it's me on video. And I simply put my YouTube video in there, for example. But I'm only doing that to build the like and trust factor. People start to see your video, they start to feel like they know you, they start to feel like they like you, they start to feel like they trust you because it's video. And so the email sequence, man, is where you're gonna nurture the list and then get people to do what you need them to do. Don't ask for too much upfront when they're just coming to your page and they don't even know you yet, right? So that's what we're gonna do, just ask for a first name and an email and then we could choose to be a single opt-in or a double opt-in. On a couple of my list are double opt-ins. And when you go double opt-in, your opt-in rate will actually decrease because sometimes people are fake, man, and sometimes people opt in a stuff and they're really not sure. So you don't wanna build that kind of list. You wanna build a list of people that actually confirm. They opted in, the system emailed them a confirmation, which is a double opt-in. And then the system cartra, for example, or any email auto responder is like this, it's all in the settings. Under double opt-in, it would be set up in a way where that person is emailed, the moment they opt in, they're emailed, but they're emailed a confirmation first. And they're required to click, to confirm, to double, to just make sure that you really wanna be on that person's list. And so when you get a double opt-in, when you do double opt-in, that list is freaking, is solid. Like that's a solid ass list because that person double confirmed and that person really wants to be hearing from you versus single opt-in, in my opinion, a lot of people go with single opt-in. And again, I have a ton of pages where I got single opt-in as well, but at the same time, you end up getting a ton of more people, for sure, but also you get a handful of people that are just not gonna engage. And yeah, they're just not gonna engage. And again, another reason why double opt-in works so well is because if it's set up that way, they go to the page, they opt-in, the system sends them a confirmation, hey, just wanted to double check that you really wanna be on this list. That right there automatically, automatically programs them and trains them to check for my emails. So that's why double opt-in works really well as well because you build a list of solid people. And I'd rather have a list of 200 solid ass leads than a list of 2,000 that are wishy-washy, right? But at the same time, you could do what you want. In this case, I'm gonna do single opt-in. And then the success page, once they come to the page and they enter their name and email and they click on the blue button, that's when they go to a success page. That's what Karcher calls a success page, which is your thank you page. So we're taking them to this thank you page right here. You can hit the dropdown, Karcher Pages, or you could take them to an external URL, but the site, the Karcher page will save that, save a next. And this is what I'm talking about, you guys. This right here is the most important email. If it's not the double opt-in and if they're not getting that type of confirmation email, then it's this one, this welcome message. This is the very first, your first impression. This is their first impression of you, right? This welcome email. So all I'm setting up right here is when someone opts into his page, I wanna make sure that this email is going out. So it's coming from this email right here, and make sure that this is an actual email, not a Gmail or a Yahoo. It needs to be your actual email. So whatever.com you have set up, set up the email as well, hit save. And then here is the message, and this is what I wanted to show you. So I have what the client wants, and all I'm gonna do is I'm going to simply copy and bring it over. This is the first email. So in this case with my buddy, Kelly, he's driving traffic to a landing page and giving out a free download, okay? So what I wanna do is I wanna simply copy this, and all I'm gonna do is bring it over. And I'm gonna actually add the first name as well. So I'm gonna put hello first name, and that will be pre-populated with their actual name that they use to opt in, okay? So hello, John. Thank you for requesting our free guide. Inside you'll learn, let's just hold this real quick. Inside you'll learn seven steps to manage your rental property that keep, this doesn't even make sense, seven steps to manage your rental property that keeps your home rented and money flowing. Okay, so let's space this. Okay, so seven steps, and let's put the number one there. Actually, seven steps to manage your rental property that keeps your home rented and money flowing. How to protect your home from illegal tenants while your property is vacant. Know your rights and protect yourself. Three things you need to know about state laws to keep your home out of litigation and courts. Hot topic, use this to make sure your biggest investment is protected. Click the button below to grab a copy of the guide now. Okay, so here's what I'm gonna do now. So he has a button right here. If I click that button, it takes me out to the actual download, okay? And this download is hosted on Cartra. So here is the download right here. So all I'm going to do is I'm gonna grab the link. This one here at the very, very top, okay? Yeah, this is it. This is the download, you guys, I'm telling you, man. This is a download and there's a link. This right here, you can easily, easily, easily have someone on Fiverr do this right here for a few bucks. So there's a link in the description. There's a discount code there. Click on the link. It'll give you some kind of discount, discount as you check out on Fiverr. But this is a download, man. This right here is freaking amazing. This is what it takes to capture leads. Give something of value for free. And it's this right here. So all I'm gonna do is I'm gonna copy this entire link at the top, okay? And then I'm going to, let's go back to the email. And then I'll put the PS, but let me put that button. Click here to download. So here's how I'm gonna do it inside of Cartra. So let's add, let's add like an asterisk or something to this. Okay, so let's hold that. Okay, so here's what I'm gonna do now. So now I'm gonna go to advanced. I'm gonna go to design templates. Actually, you know what? Let's go back to, I'm gonna have to take it out of. So here's the deal. It was in text, right? I had it in basic. So right here, I have it in basic. That's when you can write the, hi, it's Kelly Archer. Okay, so let's do, okay, so hi, it's Kelly Archer. Okay, so let's add that part too real quick. Here's what I'm gonna do. I'm gonna put their first name right in between. So I'm gonna go, hi, Jack. Hi, Jack. It's Kelly Archer. Okay, thank you for requesting our free guide. Okay, so that's that. Okay, so now, because I have it in text, or because I have it in basic, that's for basic text. But what I wanna do is I wanna add a button. So that's when I have to go advanced. So I'm gonna have to take it out of this basic. And what I'm gonna do is I'm gonna cut. I'm gonna come down to, I'm gonna click on advanced. And then I'm gonna come down to design templates. And I'm going to simply, I can go my templates. And if he had anything saved, yeah, what I can do is I can just add this one. Perfect, so it's right here.  Let me just drop it right here.  Perfect, so I didn't even have to freaking write it out. Perfect, actually, yeah, he had bullet points. Perfect, okay, so that's that right there. Let's go with, let's add, so then from here, now that I'm in this advanced part, I can also go back to dynamic variables and I can grab the first name. I always use their first name in the emails. So I'll go hi, Jack. Hi, Jack, it's Kelly Archer. Okay, perfect, so I'll go hi, Jack, it's Kelly Archer. Thank you for requesting our free guide. Inside, you'll learn, perfect. Okay, so there's that.  So let's send test, send a test. So the test was sent, let's go to that email and let's see that email. So it's right here at the top. You have a new credit report. Okay, perfect, so then right here, thank you for requesting our free guide. Inside, you'll learn, so that's the email. Your cash flow guide is ready. They come over here, they click on the orange button and boom, there goes their free 30-page download.  When Kelly first showed me this, he had like freaking two pages. I'm like, dude, no one wants to opt in, no one's going to give you their name and email, no one's going to care about your two damn pages.   That right there is value, you guys, that's value. So now he's going to be able to take his entire online marketing campaign and build everything around the free download. This is what we use as our bait. This is what we use at the top of the funnel to get people into the funnel, right? You got to give, give, give, give, give, give. And again, in a ton of my videos, I talk about make it valuable. Make it valuable, make it some type of digital copy where people can easily opt in and download it right away. So that's that. So that email was sent, that came out successfully. So now all I'm going to do is I'm going to come back over here. I'm going to hit save, and that is our welcome message. And then I hit save and next, and then this person is going to be automatically subscribed to, in fact, you know what? I am going to change this list and I'm going to add them to a different list. Actually, you know what? That's the list, that list is fine. How do, yeah, that list is fine. So I'm going to hit save and then subscribe to a sequence. I can do it right here as well, but I'll do it from, yeah, let me do it here. So I'm going to subscribe them to an auto responder. So I'm going to subscribe them to the sequence, which is this one, I think it's actually, it's this one. Okay, so step and we're going to go with step one. So now they're going to start on step one of the email sequence. When they opt in, they're going to get the welcome message, right? That's the welcome email, that's this one right here. That is the very first email. That right there is the most important email right there. It's the first one, all right? So once they get that one, we're going to set it up where the sequence goes out a day later. So over the next few days, they're going to be a part of the sequence. And that's what I'm trying to tell you, is that over the next few days, that person needs to keep hearing from you. You have to nurture that list, okay? And I'm just saying, as I said earlier, as these emails go out, put some of your videos out on it. Those emails need to be tied to a video, okay? And that's what you see. If you're on any of my lists, like, well actually to tell you the truth, a lot of you guys are on certain list of mine. There's, I don't think there's many videos on there, but when it comes to my social media clients, those emails that go out, those are all video-based emails. So again, it just allows me to stand out even more, which is powerful. So that right there is the sequence. And then advanced automations, you don't need to worry about that just yet, but for those of you that are advanced, advanced automations is that, let's say for example, someone clicks, that you send them an email and an email goes out, they click on something, I can set it up through automations where if they click, subscribe into a different list. Or I can set it up in automations. If they don't click, don't send out the next email. Or I can set it up in automations if they don't even open the damn email, don't send them the next one. Like so in advanced automations, you can do a lot, but I'll just keep it simple, save an X and that's it. So that is the welcome email. That right there is the most important email you can ever send out. It's the first one. So make it good, make it good. Put some good information in it.   And if you're not gonna do that in video, do it in text. Hey, John, I'm super fired up. Thank you so much for opting in and getting more information regarding what we got going on. Hey, let me just kind of tell you a little bit about what we got happening and see if we could possibly make a match in the future. Like some type of, you guys know your business, right? You know how you could tailor it to your customer.    Don't throw up on them, right?  Just put yourself in the consumer's shoes. What would be some good information for you to receive right away? You know, think as if you were the consumer or the potential client or the prospect in this case, right? And just put your, what I always tell my clients, I'm like, dude, because a lot of times people have a hard time with these emails, I'm like, dude, you got to keep it simple. Like don't put out some email where people are going to open it and think that you're some corporate conglomerate.   Like that's what you want to do. Relate to the consumer, relate to the audience. And that's why you see me on these damn videos where it's like, dude, just freaking scrubbing it.  I want to be authentic as possible. That's why none of these damn videos are even edited. I'm no professional. So it's like, if I can attract people that are just like me, that's what you guys want to do. And that's why I tell real estate agents all the time.  Stop trying to look like you're worth a million bucks. Like I see real estate agents all the time trying to look their best.  Like your audience, they're not, unless they are, unless you're dealing with high-end multi-million dollar investors or buyers, then you got to obviously look the part. But that's my point. You're looking like them. That's why you're driving that Benz. That's why you're carrying that Louis Vuitton bag. That's why you got on them red bottoms because you're relating to the client because that client is a millionaire, right? But if you're not dealing with clients that are at that status, why are you trying to look all that? Like why?  When you're dealing with clients that are buying a $300,000 house, right? Like it makes no sense. And that's just what I see all over social media. People are trying to look their best when it's like, dude, in reality at the end of the day, that turns people off because they feel like they can't relate to you.  Because you guys can relate to me, right? Cause I'm dumbing it down.   And again, if you're on that higher end of intellect and you're just that higher, at that higher status as far as knowledge and education and that's really how you talk with big words. Okay, cool, that's how you talk. My point is keep it real, keep it real. These emails that are going out, keep it real. Hey, what up, John? David just following up, wanted to say what up.  You're reading these emails that I wrote that sound just like the way I talk, right? And again, man, when you can do that, you don't need millions of people.  Like the ROI on this would absolutely be ridiculous. You spend 500 a month on Facebook ads, you close one deal and you make five grand. How does that not make mathematical sense, right? So at the end of the day, you don't even need a lot of leads, you don't even need a lot of people. You don't even need everyone liking you at all. When you can be yourself, you attract people like attracts like.  So the emails are the same, man, make it sound like you.  No dude, like crazy as far as, you know, out of the box, someone that's not you. Make it sound like you, make it you.   So have fun with it, man. That first email is the most important email. That's how I set it up in the form through the welcome email. And then after that, that person's just gonna get dripped on through the email sequence and that's where more value will be given in the future. All right, guys, so hope for that help, man. If you haven't subscribed, please subscribe, man. Smash the bell for notifications.  I'm in the trenches building a business. This is what I do. And all I'm doing is just simply documenting little random things that I got going on. So subscribe, comment below, let me know where you guys are coming from and let me know how I can bring value to what you got going on online. All right, guys, I'll see you in the next video over and out.


Ukraine announces major operation in Crimea amid Black Sea actions. The chief of the main directorate of intelligence of Ukraine, Kirill Obudanov, has announced the preparation of a significant operation in Crimea amid the actions carried out by the reconnaissance units of combat missions referring to the main directorate of intelligence's documentary film, War for the Sea, from the Nipro to Crimea, commenting on the significance of the combat missions carried out by the reconnaissance units, particularly in the Black Sea and in the temporarily occupied territory of Crimea, Obudanov said that these are all preparatory measures for a serious operation in Crimea. This is a test of the accuracy of our claims about the routes of approach and withdrawal from there. Also, it is a morale boost for the population that's been living under occupation for already 10 years. Many of them believe they have been forgotten. Kirill Obudanov said the main directorate of intelligence disclosed operations successfully executed by the department's special forces in the summer of 2023. The Shaman's special forces unit crossed to the left bank of the Nipro River in the Kursan region. After destroying and capturing invaders, the first steps were taken towards establishing a left bank bridgehead in that area. Also in July of last year, the Timor special forces unit regained control of the state-owned oil and gas extraction platform Ukraine, and later participated in the Crimea operation Awakening of Strength. Regarding the destruction of enemy fleet ships, the main directorate of intelligence said that this is the responsibility area of the Group 13 special forces unit. On the night of February 1st, Ukrainian forces destroyed the missile boat Ivanovets of the Russian Black Sea fleet. As reported by the main directorate of intelligence, the Ivanovets boat was to some extent the pride of the Russian fleet. There were only three units of such ships. It could accommodate up to 40 Russian sailors. Furthermore, near the shores of the temporarily occupied Crimea on February the 14th, the main directorate of intelligence and the armed forces of Ukraine destroyed the Russian large landing ship Cezar Kunikov near Alupka off the coast of Crimea. On the night of March 5th, 2024, the special forces unit, Group 13 of the main directorate of intelligence of Ukraine, attacked the patrol ship Sergei Kotov of the Russian Black Sea fleet. More countries warn about terrorism in Russia after Moscow plot thwarted. More countries have joined the United States in warning their citizens in Russia to avoid crowds over an imminent terrorist threat shortly after Russian officials said they thwarted an attempted attack on a Moscow synagogue, according to Newsweek. A security alert issued warned that the U.S. Embassy in Moscow was monitoring reports that extremists have imminent plans to target large gatherings in Moscow to include concerts and U.S. citizens should be advised to avoid large gatherings. The British Embassy in Moscow quickly shared the warnings while advising U.K. nationals to avoid all travel to Russia. At least six of the nations had joined in warning their citizens about the alleged threat, including Canada, Germany, Sweden, South Korea, Latvia, and the Czech Republic. While the nature of the threat was nonspecific, Russia's Federal Security Service said in a statement that it had prevented an attempt by an Islamic State terror cell to commit a terrorist act against one of the Jewish religious institutions in Moscow, according to Russian state media agency TAS. Active search measures established that they were militants of an international terrorist organization preparing an attack on the congregation of a synagogue with the use of firearms, the FSB statement reads. During an operation to detain them, the terrorists put armed resistance to Russian FSB staff and as a result were neutralized by return fire. It continues. It was unclear whether or not the purported synagogue attack plan was tied to the threat that the U.S. Embassy claimed soon after. The U.S. State Department spokesperson told Newsweek that there was no higher priority than the safety and security of U.S. citizens overseas but declined comment on any potential connection pointing out the department's policy of avoiding the disclosure of information on intelligence matters. Weapons that help Ukrainian armed forces destroy Russian fighter jets identified. Over two years of full-scale war, Russia could have lost at least three times as many planes as the average. The Ukrainian military managed to shoot down Russian jets with the help of a certain technique. They took some of the components from the U.S. Patriot missile battery and combined them into mobile groups. This was reported by the telegraph. A pair of four-cornered Patriot launchers connected to long-range radars via radio communications could ambush Russian aircraft 144 km away. The publication believes this is what allowed the Ukrainians to shoot down several Russian fighter jets late last year. On February the 23rd, the destruction of a Russian A-50 aircraft over the Sea of Azov occurred 193 km from the front line but that's a long distance from Patriot. The only missile system with this range that Ukraine has, however, is the Soviet era S-200. The Ukrainian Air Force decommissioned its S-200s in 2013 but returned some of them last year. However, it is not clear what radars orient these missiles as the original Soviet ones emit a lot of energy so they give away the missile launch and give pilots time to evade. The Ukrainians may have found a more devious way to use these missiles. In February 2023, the Biden administration announced a military aid package that included equipment to integrate Western launchers, missiles and radars with Ukraine's air defense systems. Analysts see this as a curtsy to the US Army's integrated battle command system. Specifically, it is a set of processes, radio channels and algorithms that allow almost any air defense radar and missile to work together. The development of integrated battle command system is not yet fully complete but it is close. The Americans have not yet deployed any major components of integrated battle command system but the Poles have done so, the reporters said.


Okay, so now we'll start these presentations of the different research projects and the first one in fact is the biggest because it's the one that has the most collaborations among faculty members. Basically most of the biomedical type of faculty members got together and they proposed and they are working on this kind of unified initiative. So that's a great way to start these presentations and Barth will be the one to present it. Thank you very much. Better now? Yeah. Okay. Good. Thank you very much for the introduction Chavier. So what I try to do is present the Maria de Mistu project of what we call now BCN MedTech. So what we started to do here in the department, as Chavier already mentioned, is that quite a lot of groups around bio and especially medical image analysis or especially also medical image modeling or application physiological modeling have been kind of developing in turbulent historical growth path, I would say. And what we decided to do now is that while there are different groups working on their own projects in some way, I think it's good to have an umbrella to do things together. And for that we created the BCN MedTech kind of unit within the department of several groups that work in this area. And also from there, we said like, okay, let's try to approach this Maria de Mistu so that it's very complementary to what we try to do here. And so that we can leverage all the initiatives that we are taking. And just to quickly mention it, the groups that are involved is the group of Oscar and myself. We're mainly doing cardiovascular applications. The group of Michelangelo and Gemma, who's doing at the one hand fundamental research on machine learning registration and so on. And for the rest also, image analysis and simulations, especially for therapy guidance at this moment. The group of Tony Ivorra, who is doing really kind of electronic, bio-electronic devices, whether it's again for therapy or for example, for rehabilitation. The health is our signal processing specialists and who is doing fundamental work on signal analysis of large data sets of signals. And then last but not least, very recently also Jerome joined the department here on mainly like biomechanics and mechanical biology. And with all of these it's like, although this is a very wide diverse field with a lot of very kind of different approaches in some ways also. Of course the over the kind of the umbrella that goes over this is of course the biomedical applications. And so we try to leverage this and try to reuse as much of the initiatives and tools that we have. So coming back to some of the goals of the million MS to partially translate it into what would be important for us is that one of the things is that when you start to look at, for example, our joint publication list, it's clear that we have excellence, we have impact within the field. But for some reason also partially the combination of the department, the way we work and things like that is like our visibility might not be the way that it could be. And so what we want to do is of course use these initiatives to strengthen especially this impact of the work that we are doing. And that's partially by leveraging on the kind of the knowledge that we already have translating that to other fields, but also especially promoting this. What you see nowadays is when you're in biology, when you even go to clinical conferences, everybody says like what you need to do now is machine learning. Machine learning is the buzzword. Also in medical journals you will see that now in every issue you will see something about machine learning. And obviously not that we have to follow kind of this trend, but I think it's important to see how we can use this, how we can integrate this. And this is a thing that has been working or has been kind of boiling within the different groups. And of course within the department there's a lot of kind of collaborations that we could do there. And especially one of the things that I think also is visibility comes from of course research output, but visibility also comes from showing what you're doing and especially teaching people. Teaching people first of all your knowledge, but additionally also your tools and partially related to that is of course your data sharing. And so that's why we decided to do like in a way a bio image analysis, biosignal analysis project that we try to both internally within the department, but especially internationally and within the Barcelona context try to work around. And one of the things that is partially lacking within at least the engineering part of our university and many universities is for example the analysis of bio images. We're very good in the analysis of medical images, which are actually images from patients. But what you see nowadays that in the biology community, there's so many imaging modalities being created based on microscopy and so on, which have some things in common with the medical imaging community. But on the other hand are also very, very different. And what you see is if you start talking to biologists, the analysis of their data is the biggest challenge. Both for the combination that there is no tools in order to do it. There's enormous data sets, I'll come back to that later on. But on the other hand, there's also very few experts from the engineering part, from the analysis part that can help them. And this is one of the things where we can try to transfer our knowledge, link our knowledge from the medical imaging community towards the biology community. And especially when you look in this context, for example, within Barcelona, what you see is that there's extremely good groups in Barcelona on the biology side. And you see the PRBB, but there's many others not only linked to the UPF, but to other universities. And on the other hand, we have our department here with a lot of both the medical images, medical image experience. But on the other hand, there's many others in the multimedia, for example, the machine learning community signal analysis and things like that. And what we need to do is bridge this gap. And my MMS2 project is a way to try to create both visibility and to create units where we can try to translate this. And as Xavier already mentioned, all of this is in a way data driven, because especially the research that we do, it's all based on data. Whether we do medical imaging research or especially when you do biology research, a lot of discovery is to data gathering data analysis. And even now, every day almost, there's a new imaging technique coming on the market. Or there's an improvement in an imaging technique so that we get different data at higher resolution. Higher data sets, for example, the data set that we are soon going to acquire within the group is, for example, one image is 15 terabytes. So these are totally different ways of working with this data. But everything is data driven and we need to end, gather the data, work on the data, acquisition, data manipulation. Making this data available for other people, of course, because these are very unique data sets sometimes. But then on the other hand, of course, try to analyze this. What I try to do is show you a flavor of some of the things that we're working on and that we will show also, or that we will try to make available to the community within this kind of projects. And I'll show you some of the examples. And here is one example of a heart at very high resolution that we are trying to work on. Because for example, the knowledge of the detailed structure of the heart is something that we have some ideas, especially from the past, especially from literature. But a lot of this is not enough to do the contemporary research. For example, to do computational modeling in very high detailed. So we have been looking for ways to find a kind of access to this detailed information. And these are some of the data sets that we recently acquired. In this case, using syncretron x-ray imaging. So you have to go to these large facilities, large particle accelerators in order to be able to make these images. And these are the images that made up this big amount of data. And of course, from this data, what you need to do is try to extract the relevant information. And these are some of the projects that we do, that we need to do, where we try to transfer technology from the medical community to this more like biological fundamental thing. Where we need to try to do like fiber tracking of the fibers within our heart. Where we need to extract, for example, the vasculature from these data sets. But the combination of both, that these are challengingly large data sets. And that's very difficult to structure and to analyze these data sets. Means that we need to get new technologies or new ways of doing this. And actually, this extraction of vessels is based on the machine learning approach where you locally learn the data, the image data that is available there. And this way try to distinguish different types of structures. And so these are the approaches, the classical, more classical approaches don't work here anymore. Both based on the challenging content of the data, together with the large data sets. Here's another example of what we very, very recently obtained. Again, you see very detailed structures within the heart. But you can go for almost a whole heart to almost cellular level where you can extract cell information from this. And this is giving unique data sets for both research, developing research, but especially for this data driven what we would call like physiology anatomy research. Because in the medical community nowadays, what you can do is either analyze data that you have and try to do statistics. And then you can prove that one thing is better than the other. But in a lot of cases, you can only get good therapies when you really understand what's happening. Understand, for example, how the heart works. I mean, there's hard cardiology research for many, many, well, almost millennia. But what you see is we still don't understand how this works. And we need knowledge in order to develop new approaches. Similarly, there's also structures that sometimes we have been ignoring in a way because we couldn't measure that we couldn't analyze them. And here's an example then of the complexity of the inner side of the heart. And especially when you see that hearts are actually really complex inside. But in the clinic, we don't really see this data because we don't have enough resolution. But what you start to see is that several diseases might actually be linked to this. So we need to understand this. And what we do there, and that's a collaboration with the University of Minnesota, actually, in the United States, where they have hearts from people which kind of were diseased and then were actually donated their hearts for science. And what we do is we get high resolution data sets. And we try to find ways where we make software in order to kind of match the data sets from different patients so that you really can compare different individuals and can see what's different, why is it different, how is this linked to disease. And this is, for example, the software in order to do this is actually based on some generic software that we now trying to use in different applications and that we will very soon make available also for the community. Another application that we are working on is in this case, for example, cochlear research. So the cochlea is your inner ear. So what you see is that there are therapies in order to when you have deaf people and you need to do like implants with electronic stimulation. But what you see is, for example, in clinical practice, this is the data sets that you get. So this is the resolution that you can obtain in a patient. Of course, it's very difficult to do anything, to learn anything, to plan things from this kind of data set. So what you do again is go back to we need more knowledge, we need more information. You go back to very high resolution data sets. Again, here, of people that died and we get the information out. And ex vivo, we can get very, very detailed information. We're then through image analysis. So we have segmentation tools. And based on this, we can make like atlas, statistical atlas of what can you see in the population, what can you expect. And then you can try this to match this to the data and thus obtain much more information. And also these tools is like some are generic and we can use in several things and some of the approaches are generic. But on the other hand, also these data sets are very unique because we were forced to create these data sets because they're not there. And again, these data sets we try or we hope to be able to put available for the community very soon. Similarly here also is like what you see is you get clinical information, you get scanner information and people say like, ah, this is this, this is this. But again, there is very little validation of some of these things. And so again, data might be needed for validation. And here again, this is actually an x-planted heart from a patient that went for a heart transplant because they had a very, very damaged heart. And this has a lot of information and we have preoperative information from the regular clinical images from these patients but we really need more detail. So in collaboration with some people here in Spain what we do also is try to get really detailed information and what we then try to do is analyze this and match this. And again, these data sets are not available. Some people might have tried that in the past but they're definitely not available for the research community, partially because people didn't find the need or found the ways in order to do it. And for us, this is an opportunity in order to be able to and create these data sets but especially curate these data sets and make them available for the community. But you can already imagine that these are huge data sets. It's not so much that is big data in the sense of many, many data sets but it's big data in the sense that one data set can be enormous and one data set is so unique that it's really important but we need the facilities, we need the hardware and so on in order to do that. Other projects that are partially related to this is really based on clinical information. So here you see clinical information and this is actually from a European project. What we started to do there is try to use machine learning techniques again in order to extract this data. But as you might know people that are working in this field or heard about it it's like working with clinical data is very challenging in the sense that you have these problems with patient confidentiality. Also with that a lot of medical doctors when they have the data sets because they know these are unique data sets they're not very eager to share that. Even after publications they're not eager to share that for different ways of saying like okay this is my data this is one thing which we still need to do is like work on the perception of data sharing of course in this community. But on the other hand also it's like especially medical and clinical data is actually kind of I would say crap data almost. It's like people are sometimes afraid to show it because you will never have a good data set. Having good data in clinical practice is almost impossible because there's always either something missing or the patient is moving or there's something going wrong or things like that. So data is not ideal and I think we also have to work within this project to work on like we don't have ideal data set that we can give as an example to the community but what we should do is try to share whatever there is and kind of acknowledge that this is reality that everybody works with and then people will start to work on the same framework. And then you can see that we developed some tools to do kind of machine learning in this data. But what you see is also kind of when you look at the current what I was saying these machine learning papers that come out in the medical community or in the biological community is in a lot of cases for decision making. So you do like a deep learning black box to say this patient has this disease or that disease and things like that. But this, although this is in a way data-driven research to make decisions but what you get in the end is a black box to make a decision. But you don't know whether this decision or how this decision is being made and it doesn't help you to try to understand and to know okay which is the next data that we need in the future which are the new approaches. That's where we very, very much like the idea of using machine learning to start learning instead of making decisions. And here is an example of where we do unsupervised machine learning where we try to understand the group of patients and what you then see again is that reality is not clear cut. And that you see that patients which have been cataloged binary by the clinician as having this disease or not having this disease. But there's a lot of people that are in the gray zone and when you start looking at the data then you see like, okay, non-diseased patients actually have a lot of disease properties while the other way around some diseased ones might be almost similar to normal but something is going on. And this understanding will be much more important and I think some of the new technologies that have been developed in artificial intelligence and machine learning can be translated in order to do like really physiological learning and trying to understand what's in this data. And this is a thing that we want to really strongly work on. Some other examples is here is like some spine data that we're working on. So we have collected a lot of data from patients where we then extract the information of the spine, make models for biomechanical simulations. Again, these are unique data sets that should be made available to the community and that we're working on. Then talking about the images, some of Ralph's work is where intracranial ECGs are collected. You can already imagine that to do that in humans to get intracranial data, which you have to do of course intraoperative. This is also, this is very unique data. And again, it's very, very difficult to find groups and clinicians that are able to share this data and are able to pool this data for the community, for the analysis, to try out new algorithms. And Ralph has managed partially also now with an extension in this Maria de Mesto projects to be able to acquire this data sets or going to acquire some of these new data sets and then really make this available to the community. And some of the things, some of these are already available but one of the things that Ralph also said to me is like, when you see if you take your paper and together with the paper, you link the data, even if it's a lot of work to convince the clinicians and you almost have to threaten that you don't want to work with them anymore, once you do it, you will see that the impact of your paper is much, much higher. He's saying that one of his highest cited publications is one like, okay, the technology and the science behind it is of course good and interesting, but the fact of adding this data set and making it available makes the impact so much larger. And I think this is important, but here, especially in the medical community, we have to fight for this data because we cannot acquire this ourselves. We always have the need of medical doctors, biologists and we have to convince them that this is the way to do it. And then of course, these data sets go towards trying to have the tools available. We need to extract information of it. We always, the way you see this, like what I said is we need this data because at some point there was a need in order to get this data because we needed the information. Now once you have the data sets, then of course the challenge of getting the information out that you want to use is again, quite large. And so that's where within this project we also work on this. And then what I've been saying is like, as I said, like our experience is mainly in the medical community, but as you see, like now people start to work on biological applications with microscopy and some of the challenges, for example, is here you have like growing tissue where you want to understand how is growth taking place, which of the cells are growing, are they getting bigger, are they getting more, which are dividing. So what you need to do is over a long time tracking of huge amount of data, for example, and these are things which are big challenges in order to do that. Or this is an example of a data set from a brain, a high resolution electron microscopy data set of a brain of a mouse, which is available.  And in the past people would do that by hand, it's like microscopy was analyzed by hand, still nowadays a lot is analyzed by hand, but if you have these huge data sets with millions of neurons in there, there's no way you can do that by hand. But that also brings you to the challenges like how do you validate the algorithms that you have. So you need in some way for both for machine learning but also for any other analysis techniques you need to have ground truth in some way. So how do you generate ground truth when it's almost impossible to really create this ground truth? So a lot of the research that we do also has to go towards this is like how can you reliably try to label data sets without having to do that manually. And there's like two things that we need to do is once, it's like one individual cannot do that anymore. So what we do is we create initiatives where we might try to make, for example, we organize taggathons, we will do that quite soon again here also, where we say like, okay, you gather 30, 50 people that are two days or three days just drawing on these images in order to get ground truth or partially do crowd sourcing here also, right? There's other initiatives for that also. But on the other hand, you also need algorithms not for the analysis, but for the training of your analysis. So that's another challenging part that we work on. And one of the things that we also realized is that when you have these applications and especially when you give an application to a biologist, when you give an application to a clinician, it's like mostly just giving an executable or something like that doesn't work. What you need to do is you need to create an environment and especially in the community of both medical and biological image analysis, it's like a lot of tools might be available, whether it's open source or not. But at the moment that you try to compile them, you see that, ah, it only works in this version of Ubuntu with this library and with this kind of thing.  It just doesn't work. So we also need ways of trying to do this. And when you look at the current recent developments also in informatics and so on, is what you see is that we have to start working much more in a container-based way, like working with applications. And so one of the things that we started to see is we actually started to develop that out of need for our clinical applications, but we now see that this is actually translatable, for example also to microscopy, is that we're creating a platform which is much more like a mediator in some ways. And a mediator between data sets and large data sets which might be hosted here, but sometimes there's a data set hosted somewhere else. And if it's a data set of 10 terabytes, this is not something that you just download on your computer. When you want to do processing there, what you actually have to do is likely put your algorithm on a computer there which has direct access to it. And so instead of moving around data, we have to move around applications. And so your data sets are one thing, but your applications should be made in some ways that we can run them in other places, that you can run them on your local computer, that you can run them on these kind of high-performance computer facilities. So for example, also we collaborate a lot with the Barcelona Supercomputing Center for some of these applications. And then on the other hand it's like then, okay, you need the applications, but you need also the data interpretation. And putting all these things together is not of this time anymore because it doesn't work. It doesn't work with the applications. It doesn't work with the developments that are going on. So we need some ways in order to develop that. And we, within the Maria and Esther, we started to think about this kind of architectures and we're working on this. And here is an example of some of the like really dedicated applications in this case for the analysis of some medical images. But one of the most important things is that we need to have some kind of reusability of both data sets, of both tools and all these kind of things. But we need to try to approach that in a similar way. So what we need to do is, and that's what we developed within this platform, is try to define ways to isolate data sets, to isolate specific applications and make sure that they stay compatible, that they are compatible, but especially stay compatible in the future. And one of the things that we are really trying to do now is then also make sure that this approach is useful for the different things. So we have expertise in medical image analysis. We start to translate that in bio in image analysis and actually there's a lot of things which are similar. There are other things which are different, and within this project what we try to do is also, we try to identify the similarities and try to structure our data sets, our algorithms, the way to share all of these in a similar way that we can use the same tools for them. What I also mentioned is that I think that exploitation and outreach is very, very important, especially for us, it's like we are a small university that is not too much known at this moment in the world, and I think we can do a lot better. And one of the things is, of course, the active collaborations, especially internationally, which are important. The thing is we are involved in a lot of European projects, so as individuals we actually quite known, and that's something which is also typical for the whole department, is individuals are known within their field, but the whole institute is not known that much, and I think there we can work on. And one of the things that I really think that helps in trying to promote the place locally, the unity of the department of the university, is trying to get people here in order for conferences, training courses, and things like that. And so what we started to do is try to organize, especially these specialists, specialized, and I think also research-oriented training, because there is a clear need, especially as I said in the community of biology, there's a big need, the community of the medical imaging, there's a big need, so I think we really have to try to use that, and especially if you see, for example, this auditorium and things like that, we have great facilities in order to do that, and I think it's underused, so this is a thing that we work on. And Xavier was also already saying that entrepreneurship is something which is really important, and the way we currently try to do that, and which I think is a very good way, especially towards young researchers, not only researchers, also young engineers, is trying to get them into partnerships, get them internships, or get collaborations with the industry. So we try to very much promote industrial doctorates, for example, we have a couple of Marie Curie industrial doctorates, or a kind of networks and things like that. I think this is a way to get closer to the industry and learn our students, learn our PhDs in order to do this, how to work with the industry, and the entrepreneurship, which not only, as Xavier said, leads you to the industry, but will help you in any part of your career. And some of the initiatives that we have recently done is we organized very recently here, for example, a virtual physiological human summer school, so that is a modeling of parts of the human body. So that's what we did, which was actually very successful. The students especially liked this. And as I said, especially in the image analysis, the bio image analysis, we start to do more and more training initiatives where some of the courses have started already internationally. The postdoc, Chung, who is working in this project, especially in this area, was already involved in courses abroad, in Germany and things like that. And now we are translating this in Barcelona. And in September, for example, we will organize some of these courses. And we will do courses both for the local community, but especially internationally. And I think this is very, very important to get the context for the future because these projects are intrinsically interdisciplinary. So we need both the engineers and the biologists and trying to get the biologists to know our institution by training them, even if it's very, very kind of basic training sometimes, it helps you to think on what are they need. It helps them to discover what engineering can do. And this way we can try to collaborate together and get for future and progress in science and collaboration that can help us funding, for example, these science. So I think that's what I wanted to do, is like give a presentation of the first start, actually the onset of this project. And I hope in the next years that we can start to give you some more concrete results where we got in this way. Thank you very much. Question, for me, just one comment that it was not clear, the issue of the software, the data is, I think it's quite clear. Concretely, do you have some clear strategy of how to reuse software? For me, it's always the issue, a new student comes and has to continue some existing PhD work for someone else. And basically they then have to start from the scratch. This is indeed a challenge, I agree with that. But partially, in a way we will partially approach that I think for now is trying to make sure that a lot of these projects that the specificity of the project is partially isolated. That means that access to the data, how you present it to a clinician or to a biologist, we try to make that in a way in a common way and then make sure, so that's like what we start to see is like that we want to work with these kind of containers more, like let's say like, okay, when you do an algorithm, you cannot force a student to use a certain language or use Matlab or C++ or whatever, because you have to do what they're comfortable with. That's the most efficient. But you have to make sure that you can try to contain kind of what's, I mean, how you can incorporate that in a more bigger environment, I think. And this is a thing that we try to work on, which we saw the need of. But I don't believe in a full common platform because these become mastodons that you cannot use anymore, but I do believe that there is a common framework that might be useful and can be more so that that's the work of developers.  So that's why we start to think about this kind of approach where we try to isolate it. And even like when you have an algorithm, you know it works to ensure that it works. The only way is create a virtual machine, for example, which can be there forever, whatever version of whichever Unix this was, you don't care, but you can keep on using it. So that's what we try to do. We'll see if it works. In three years, we can tell you. Any other comment? So my question refers to the green slide where you had your roadmap of how you would like to see cyber infrastructure developed that supports the research. So my question is there are a lot of new tasks in there that are not in the traditional role of a researcher. So for example, even sort of platform development, you're talking about the containerization and all of the software development takes more and more time than it ever has traditionally as well as sort of working with the data and so on. So my question is how do you imagine or how are the people who are doing this were getting rewarded for that? When typically the reward is really for the publication and the sort of traditional research output. Yeah, so for this you need indeed developers, software developers, but I think in a way when you look at which are the possibilities for a software developer, well, one of the things is you go to a very big company, you get quite a good salary, but you mostly do very boring work. When you go to the university, the challenge from them is like, okay, you try to use the newest technology, you try to be embedded in this. So the thing would be really kind of in a way an intellectual stimulus to make it that they can learn more, that they can develop more, they definitely get less money, we all know that. But in a way, it's the same thing as in academia.  Not I have this paper, but I solved this problem. And in a way, I think it's very, very similar. And there, the kind of, whenever you have a software that is being used by somebody, I think that's a really, really big reward. Because a lot of the software developers also in big companies, maybe they do a small part of something, they have no clue who uses it or are not interested. Well, and this kind of things is you're very close to either the researcher or the biologist and so on. And you see them using the tool and you see them telling like, oh, this is great. We should have had this years before. I think this is a great reward. So this is what we have to do with. We count off our money, so.


I'm in an empty apartment. Yes, we had to move from where we live, unfortunately. But for good reason. So yes, over the past week we've had to move our things in bits out of this apartment because when we were paying our rent last year, which is in dollars, it was fairly affordable for how much we were paying. Decent two bedroom, it's quite spacious, has a lot of things already fixed. It's semi furnished. It's beautiful. We loved it here. But when we converted due to the city's depreciation, we figured, no, this might not be in for us in a long term. We're having a baby soon and we can't afford to be paying this much in this apartment and still have only two bedrooms. So we decided to move to a bigger place. So in this video, we're going to show you how the packing and the moving went So what we did since we're moving to a place nearby in the same neighborhood was to move in bits starting with our non-essentials. We essentially did a number of mini trips to reduce our weight before the main day. Next came my studio and office since I wasn't really having any voiceover work backlog and I had to go. So this is what I got after clearing out my studio. Stands, old gimbals, my screen for editing, boxes on boxes on, oh my God. And it's not even finished. There's a desk, there's this, there's all of these things to clear out of here. So the acoustic panels were installed on the Thursday before our main moving day on Saturday. In this new house, which is a three-bedroom, I get to have a dedicated space to work from. And now a quick interlude as Nala enjoys the first time on her new compound. It's the actual main moving day. I just got back from the new place. I just installed the washing machine, which I forgot to film, but I'm forgetting to film in your piece. So as you can see, everything is in shambles around here. That's, my kids is taking care of the kitchen. And yeah, so that's what we're going to be doing today. I cleared out all my studio panels two days ago. So it's just left on my desk and my chair and a few things. So we're getting the work done here as well, stuff on the beds. We're getting there. Yeah, almost there. So we're getting help coming in. Almost empty. Yeah. And then this one is empty. It's left with the manuals for the ACs, which they need to keep. And then I'm taking out the clothes. Yeah, it's a lot. And Nala is also lifting stuff, helping out, you know. So that's what it's going to be for today. We're getting help coming in in an hour to help us lift the heavy stuff. But we're doing all the packing, hopefully, so that when they come, it's just lift, lift, lift, drop, drop, drop and bounce. So we'll catch you in a new place. It's going to be a long day. The third part of moving. How come? What are you doing? Wow. Yeah, it's an idea of it. If you dismantle this at least twice in your life, at the time it comes way easier. Whilst we're dismantling. We're working with work when our friends came to help that we forgot to film anything with the actual moving. However, this is how it all looked with the house empty. And over at a new place where it's still settling in slowly. And this is how my office looks now. Thank you for watching and supporting and catching you in a new one. Wishing you a merry, merry Christmas. Enjoy.


Interest rates and inflation dominate in a short term, when you talk about a 2-3 year time resin, but innovation is the one that dominates in the long term. It could take over a year before we'll see the end of this bear market, according to the CEO of Electric Capital, Avishal Garg. Despite the rough times, venture capitalist money is still flowing into the industry. Earlier this year, Electric Capital raised $1 billion to fund the companies with the best chances to emerge stronger from this crypto winter. Why would you spend $1,000 a year on shoes? In this video, we sat down with Garg to understand the current state of venture capital in crypto. We also discussed the industry segment with the best chances to explode in the next bull cycle. Before we start, don't forget to like the video and subscribe to our channel. I'm Giovanni Rost and this is a Coin Telegraph interview. So Avishal, you founded Electric Capital in the midst of the 2018 bear market. So now that we are in another bear market, how can you compare those two situations, the one we are now and the bear market back then? Yeah, good question. This one feels not as bad, to me at least. Maybe I'm just dead inside now. I flipped through two bear markets, one personally and one with Electric and now I just don't feel it anymore. If we think about where we are as an industry, it's all of the things that we would have hoped for in the bull market in 2017 have actually happened. The institutions are actually here, ETH is not a security, proof of stake is happening, merge is here, L2 is actually work, DeFi is actually a thing, NFTs are actually a thing, there's IPOs happening from crypto companies, the rate of progress and evolution over the last four or five years is just amazing. So I kind of look at it like this bear market is better than the 2017 bull market and when you put it into that kind of context, if you've been through one of those, it doesn't feel as bad. Of course, if it's your first one, it feels terrible, but that's sort of the nature of crypto. Yeah, that's very interesting if you look at just the price, you don't get the feeling of how the ecosystem has improved, how much the ecosystem has grown. As you said in terms of sophistication, in terms of use cases, you said that this bear market could actually last long for a long time. You mentioned two years of bear market with the next year as the recession year. So can you guide us through this prediction? Actually since the modern beginning of the space, let's say 2008 with Bitcoin, we really only had macro tailwind, we really had central banks reducing interest rates, you've had stock markets going up. And so we don't really know what happens in an inflationary environment, we don't really know what happens when central banks are raising rates. And that's not to say that it has to be the opposite, that doesn't necessarily mean that Bitcoin or Ethereum or these things don't work, it just means we don't know. And so from an intellectually honest perspective, I think you have to look at that risk and say, okay, well, it could be that these things decouple, it could be that capital keeps coming in because this is a highly innovative area and we haven't gone up the S curve in its early stage technology and those things work despite what is happening in the macro, like 2009, 2010 was a great time to be investing in early stage technology. Or it could be that it behaves dramatically differently. It could be that everything couples doesn't move until the stock market comes back. And if that takes three or four years this time, then the bear market may be much, much longer. Now, I'm not saying it is going to be longer, I'm just saying we don't know. Yeah, also talking to, you know, other analysts, experts, it really seems that the prices in crypto are fundamentally tied to this macro economic picture and that the Fed and the way central banks all over the world are managing these monetary policies are basically deciding where the crypto prices are going and this macro economic picture doesn't change. It seems unlikely that we are going to see the next bull run. Would you agree with this kind of statement? Well, yes and no. So I think when we think about the math for a back drop, I think you have to think about three things.  So one obviously is the inflation rate and it has a real impact on people's lives. Food costs go up, energy costs go up and depending on where you are in the world right now, if you're in the western democracies, let's say, you're probably dealing with somewhere between 7% and 12% inflation, which is sizable. That's real. I expect that will come down over the next year or two, but inflation is one consideration. The second is interest rates, as you're saying, and as interest rates go up, that changes flow of capital and risk reward, but the third one is innovation. That's the third eye. And I actually think the innovation eye is the dominant one in the long term. So interest rates and inflation dominate in a short term when you're talking about a two to three year time horizon, but innovation is the one that dominates in the long term. And so part of this is what sort of time horizon are you investing on? And so we're VCs and so we think about what happens seven to ten years from now. And if you rewind seven years, if you're in didn't exist. If you rewind ten years, Facebook had just IPO'd. There was no Uber IPO. There was no Pinterest IPO. There was no Snap. There was no TikTok. You know, the iPhone was only five years old. Ten years is a very, very long time. And so if you look at it through that lens and you say, well, how is this stuff going to compound on a fundamental basis? How many people might be using this? Can we go from 10 million people to a billion people using D5 products? Can we go from 30 million people to 2 billion people using NFTs? And if that happens, then the innovation eye is going to be the dominant eye. And frankly, it won't matter what the inflation rates and the interest rates are. So now I would like just to move on talking about the specific sectors in the industry you feel excited about. So 2.4 billion dollars were invested into metaverse and gaming projects in 2022, which is a much bigger figure than what we saw in 2021. Do you also feel bullish about metaverse and gaming? And why do you think they still see so much capital flowing in despite the market? Yeah. So the short answer is yes, very optimistic about gaming and NFTs and sort of that whole part of the ecosystem. And the way that we think about sort of the most promising areas inside that umbrella term, whatever metaverse means to you, gaming, certainly, we continue to see just a flurry of activity. We don't really invest in games, which is just not our era of expertise, but I probably still get cold inbound one game pitch a day, which is remarkable. And so if I'm getting one a day, then investors are getting five or ten a day. I have to imagine there's so much activity, which means 18, 24 months from now, I think we're just going to see this onslaught of games, which will be great. And these are, they range from casual lightweight games to people coming out of Tencent and Epic and AAA game studios that are going to spend tens of millions of dollars to build really robust video games on top of the infrastructure. So that's the gaming side. We don't invest a ton there. The two other parts that are adjacent to this, though, we do invest in. One is infrastructure, which is how you're going to run all these games.  And for example, do you need like layer twos as a service, right? Do you necessarily want to run it on optimism or every trimmer?  And so like Magic Eden works on some of this kind of stuff. And then and then the third category is NFTs themselves, because I think the interoperability that you get with NFTs for video games is going to be really, really great. I just wanted to jump into this because my following question was actually about NFTs. You said that NFTs, according to you, are the crypto category that is going to bring crypto into the mainstream. So maybe you can expand on why you think that specifically NFTs are going to have that role that is going to bring crypto into the mainstream. Yeah, it's because NFTs are being used by creators, you know, creators like musicians, athletes, artists, writers. These people realize that this is an opportunity to have a relationship, a direct relationship with their fans. So those thousand people that absolutely love your art or your music can find you now and they can pay you. And that's actually stuff that normal people can understand.  And so all of these creative people, I think, are going to make this stuff go really mainstream, whereas when you start talking about DeFi, I think from a TAN perspective, like a total addressable market perspective, it's going to be huge. But how many people really understand options, right? How many people really understand the Greeks? How many people really understand these DeFi protocols and the tail risks that you're taking with smart contracts? Like a lot of people don't. And so maybe that ends up being a couple hundred million people and many, many trillions of dollars. But I could imagine that NFTs are actually many billions of people because because it's ultimately culture. And that's something that everybody can participate in and everybody can understand. And so I've increasingly become, in essence, an NFT maximalist. We were talking about this time horizon that you as a VC investor look at. You would think that in 10 years, NFTs will be all over the place in our lives. Yeah, that's right. I think they'll be everywhere. I think it's a data standard. It's an interoperability standard. And if you think about, I mean, as we were talking about before, we already spend most of our time in the metaverse. And it reminds me actually of with the internet, there were these graphics that used to get passed around where you could look at the amount of time spent on the internet versus the advertising dollars. And then you'd look at newspapers or TV and a time with shrinking, but the ad dollars were still really, really large. And so you could look at that really simple graph and you could say, this has to fix itself. And so if you just look at where people spend their time today, especially if you're under 40 and especially if you're under 30, it's all in certain places online. And where do you spend your money? Like you're spending it in actually other places. You're spending it mostly in the physical world. And so what are the use cases and the needs that you have that are being spent on the physical side of things that actually you could be spending digitally? And I would posit that it's a very, very substantial portion of the average consumer's discretionary spend. So it's something like 25 to 50% of the stuff that you spend money on, clothes, shoes, purses, watches, even your car, a lot of these things actually, once you start living in primarily digital existence, well, if you're not wearing shoes at home, like why would you spend $1,000 a year on shoes? That makes no sense, right? You should take that extra discretionary money and move it into the metaverse. And then NFTs are the vehicle through which that happens this time around. Instead of ad dollars, it's gonna be through NFTs. That was a great insight in the way you make your decision in terms of investing crypto. Thanks a lot, Avishal. That was a very cool conversation. I hope to see you soon on our channel. Thank you so much. Thanks for having me.


Lola had a problem where her kidney was blocked. It's really one of the largest kidneys I'd ever seen.