Hello everyone, my name is Nahla Salem and I'm here today to tell you how to build a machine learning product team. First, a little bit about myself. My specialty is machine learning product management. In the past, I've helped set up machine learning and product teams, mostly in Toronto startups and mostly in enterprise product management. My current position is a senior product manager in Yelp, where I'm helping build out a marketing analytics practice. My past customers include Fortune 50, Manufacturing Conglomerates United Technologies Corporation, as well as AS Watson, which is the largest health and beauty retail group in the world. In the past, I've also done software development and consulting. I have a master's in business analytics as well as an EMBA. However, I would like you to not worry too much because you do not really have to have a master's in analytics or data science to benefit from this presentation. So this presentation will be geared towards product leaders and tech leaders who would like to answer the question of should we start a machine learning team and how do we go about it? Because of my background, it will mostly be relevant to startups or medium sized companies who have some sort of core product that could or not necessarily include machine learning. So you're either looking to add machine learning from scratch or to scale a tiny team of maybe one or more data science people. I'm going to tell you first of all what machine learning is. Where should you start? Because this could be a daunting question to many. A mini crash course of machine learning for product managers. And then I'll look at the question of building a machine learning team from three axes, who will build, how to build and what to build. We'll talk about tips for starting your first machine learning features as well as pitfalls to avoid. Let's get started. What's machine learning? Well, I can tell you that it is not this. It is not artificial intelligence that's going to take over the world. There is a lot of hype going around machine learning that I would like to dispel. And surprisingly to me, the question I get most often is what's the difference between machine learning and data science and AI. Well, let me tell you that you do not really need to worry about this. For the most part, these are very technical or academic differences. For the sake of what you want to do with this presentation of starting a machine learning team, just choose a name and be consistent and use it internally and externally to avoid confusion. And I'm going to be using machine learning today. So what machine learning is to oversimplify it, it is using data, processing it in order to understand the world and then make a decision to predict a future that is specific to a certain business problem. So that would involve capturing large amounts of data and the processing would include finding trends in this data, hence the need for large quantities so that you can get the right trend and not overfit, for example. And then use that decision for a specific business problem and defining the problem and what you want to do with the result is the most important part. So machine learning is ubiquitous in our life right now. You have probably used it even if you hadn't realized. So examples for machine learning systems that are out there you probably used are spam alerts. So that's a decision that is made for an incoming email based on previous historical emails, including spam and non spam to issue an alert for a specific email. Netflix uses your watch history as well as that of millions of others to give you recommendations for things to watch usually works very well. And you could have something like a sales call be transcripted into text, which would involve a discipline of natural language processing subset of machine learning. Andrew Nguyen is a thought leader, really in the space. He is also the co founder of Google Brain, Cloudera and an academic and he puts it very well in terms of describing how the future is machine learning. He says AI is the new electricity, just as electricity transformed almost everything 100 years ago. Today I actually have a hard time thinking of an industry that I do not think AI will transform in the next several years. Those of us who've been in the industry long enough will remember the days in which CTOs and CIOs were asking themselves whether they should move to the cloud. Now this is a no brainer. And in the last couple of years, this is the same thing that's been happening with machine learning. So where should you start? I think you should start with the following question of should we get a machine learning team in our company? And as good product managers are, I would like to ask people to approach that question with a bit of skepticism. And that's because more often than not, I see people rushing into the hype and I think more thoughtfulness is needed to answer that question. And I'm going to give you some helpful questions to ask yourself in order to decide. So you have to be very clear on what your value proposition is and whether and then ask yourself whether it can be enhanced by machine learning. Ask yourself about your use cases and whether they could make use of predictive power. And then ask yourself if you have the data, even if the answer is no, this doesn't necessarily shoot it down. But that would help you understand the following question, which is what are the costs and what's the ROI. And you need really to educate yourself. You need to know just enough about it in order to start. And one good resource is actually Andrew Nguyen, who I think is great in terms of explaining very technical concepts very well to people who will not have a strong technical background. He has a Coursera course called AI for everyone, which could be a good start. Point is get help and do some homework in order to educate yourself and your team. So machine learning for PMs, this could be a presentation in and of its own. So I'll hold on on some things that from my experience are useful. Where the first question is, are machine learning products different? Are they different from other software products? Well, it depends on how you look at it. Something I see happening often is machine learning or a machine learning team getting special treatment, which is not necessarily conducive of the productivity that we want to see in our teams. I think we should look at machine learning. That's my personal philosophy. We should look at machine learning as just another technology and be thoughtful about the product value that we want to give to our customers. However, there are some differences that I will highlight now in terms of the development lifecycle that are indeed different from your quote unquote regular software development lifecycle. First and foremost, as I alluded, you have to start with formulating a problem. And if you don't get that right, then the rest of the steps are not really going to work out well for you. And then you ask yourself based on the problem, what's the data that can help me answer this problem or solve it rather. And you will need with your team to understand the data and do pre processing, etc. With the aim of finding out doing feature engineering, which is essentially finding out what are the elements that affect the decision that we're trying to reach. And then you will build a model, trade the model on the data that you have provided earlier, test it and then tune the model. So if you've been paying attention, you will notice that this development lifecycle unlike maybe others you've seen in your past is cyclical. And that's exactly the point you with machine learning rarely get it right from the first time. And you need an iterative process in order to sometimes even answer the question of, can we solve this problem using data and machine learning. But more often than not, the iterations are going to include things like, oh, we need more data, or we need to go and review the features that we have defined, or we need to keep tuning the model until we reach a performance that we're happy with. And in terms of model performance, this is really what I think the most partnership between product and data science needs to happen. And where, if you're as a PM going to spend time on education, this is where you need to spend time, not so much on the inner workings of a model, but spend time understanding the things that I have here, which we won't have time to get into in details, but essentially the ways in which you can assess a model's performance. If you're a technical product manager or a technically oriented product manager, then you will have conversations with your data science team, regarding the iterations that we just talked about when to stop and guide them with how to conduct the iterations in a way that optimizes for the business goals. So go and educate yourself on things like confusion matrix and why saying a model accuracy is simply not enough to describe a model's performance entirely. You will perhaps guess that a model's accuracy of say 85% is better than 70%, but then an accuracy metric doesn't give you the full picture of a model's performance and spend some time understanding why that's the case and how you can better make decisions around the choice of models. Alright, so let's now approach the access of who is going to build and if you're just starting this is going to involve hiring people to work on machine learning. Typically that's going to involve data scientists as well as machine learning engineers. Data scientist is the person who is going to build out the model and machine learning engineer generally speaking is the person that's going to operationalize the models so that will involve making the data available for the model and then making the results available inside your product. What to look for in my opinion is a very good focus and understanding of the business aspects and the business goals that you can use machine learning to serve more importantly for the data scientists. You will probably find a lot of people who are good with the theory, but the application is what you would want to focus on in the hiring process. Look also at people's backgrounds, someone coming fresh out of academia is going to be different from someone coming from a well established company and spend years there. Someone from a startup for example will tend to be scrappier as those who work in startups would know. As for the interview process, I think a very good way to go about it is to present the candidates with a business problem and then see how they're going to approach it and then work together in a collaborative manner because that's going to simulate how things play out in real life. Unlike the picture of the interview we have here should be an interactive collaborative process. As for the machine learning engineer, their interview process is going to be engineering oriented, but you still need to measure whether or not they have a good understanding of machine learning concepts as well. One pitfall that I've seen many companies fall into is this dilemma of if you're starting a machine learning team and you do not have in-house experience, how do you then assess candidates? And for that I highly suggest getting outside help, you can fall on your network and see if someone can help you or get some consultancy done for you in order to guide the interview process. But don't try and do it on your own because this is a complex discipline and not knowing enough to judge people might not give you the best ability to judge candidates. And finally, since we're for the most part working remotely, don't forget the social aspect of building out a new team in a remote setting and try to compensate for the lack of face-to-face interaction. Alright, so team structure, this is something I'm very passionate about, I could go on and on about so I'll try to be brief and make an important point that I also see very important. So technical people tend to gravitate based on their skills and when it comes to machine learning this is not any different. So a setup that I've seen in many companies is they start out a machine learning team as a separate machine learning team, which is separate from maybe the multiple product development teams that they have. And this does not give you the best performance. It results in a siloed team that is not connected to the business, is not connected to the priorities, is not connected to the goals. And very often I've seen companies do that and then they reach a point where they realize that they do need product help and then they start adding a product manager to the mix, but then that still doesn't give them the optimal setting. And the optimal setting I think is what we've seen in the Spotify model and really what is the model in many other companies at least if you boil it down to the basics, which is having an autonomous product development team that includes all the required skills to push a feature to production, including data science. So a friend I have who worked in Netflix mentioned that they have data scientists embedded in product teams, so those would be working on product problems as per the team they're in. However, they do have a separate machine learning team, but that team services general requests that can come from even outside the product teams in the company like finance. And that's a good model that straddles the benefits of both setups. However, if you just rely on a separate machine learning team, then inevitably that team is going to operate more like a service desk. They will have a hard time prioritizing it might end up being a first come first serve basis, which is again suboptimal because you always want prioritization to be connected to business goals you want the people who are working on code to be connected to how that code is going to be used and the only way of doing that is to be embedded in the product team that's working on that specific problem. Now the obvious elephant in the room is if you're starting you will not have the capacity to embed a data scientist in the couple of or more product development teams that you have and that's fine. That's fine. It is okay to start out with a separate machine learning team, but then you have to compensate for the things that I mentioned by perhaps creating dotted line teams Tiger teams. However, you name it, have a data scientist be embedded with a product team for the duration of anywhere between a sprint and a quarter depending on your needs, but do your best to compensate for not having an embedded team. Okay, so how how then should you go about building machine learning features. So we've we've seen the process and how it is different in and then in order to work with that difference what is very important is for you to educate and educate and educate your stakeholders and internally in your company. I've, I've done in companies that I've worked in presentations like this set expectations have people be aligned at least on a conceptual level on the different development cycle the fact that it needs more time and includes uncertainty sometimes to the point of failing to build a model that actually improves the user experience, and you only know that after spending iterations and spending time. So, so find a good balance between setting expectations and not being too pessimistic, but but I found it very important, because sometimes people have an expectation that machine learning is a magic one that's going to solve all the problems that we have been moving from quarter to quarter into the future and unable to to solve and generate artifacts, spread the word use your company's wiki and whatever tools you have at hand for internal education. Alright, so on the question of what to build. I think an ideal setup is to have some sort of research roadmap that precedes your regular roadmap or your feature roadmap. So a research roadmap would include working on models and doing proofs of concept of concept, and then a feature roadmap that makes use of machine learning will include the actual building of features that use those models, the productizing and sometimes also the scaling because when you initially build a model you build it on a subset of the data, then with the help of machine learning engineering you will scale that to production size data. After the model is found to be successful so in the past I've done both. We have had research roadmaps in addition to feature roadmaps maintained separately. When you're starting out you'll probably not do that, but at least do it in a in the lines manner in which you have one roadmap but you plan by having research ahead of the actual development of a machine learning feature. And this gives you the best productivity because it gives you a practical tool to deal with the uncertainty we talked about in terms of building machine learning features. And at the same time it gives efficiency in terms of using resources because the data science work is going to be very heavy in the modeling stage, but then in the productizing stage the machine learning engineering work will be heavy and the data science work will start to wind down. So if you plan it well ahead of time and at that point have another research problem for the data science to work on, data science team or data scientist, then they will work on it and then make it ready for the next chunk of time for the future development to take place and so on and so forth. Okay, so especially if you're going to use machine learning to automate a decision that's been previously done by a human, you have to keep in mind building trust with the users as you're doing this replacement. So use your good old change management practices to make that successful, prioritize the trust aspect in machine learning feature, don't let it be an afterthought towards the end, build fallback scenarios because sometimes the model is not going to perform well and you have to keep that in mind and have a plan B. So for example, if the model results are not as expected, then you revert back to an old process that was there before the machine learning feature, depending on your product and the feature that you're building. This is a diagram from an article I've written, you'll find it on my website and the article is dedicated to building trustable machine learning products and things that we have done in the past such as have the model be tuned through business rules and then allow a user to set those, have somewhere in the product and give access at least to an admin for some sanity validation reports for model outputs or a sample review of a model's outputs. And something we've done in the past is especially if this is a very machine learning heavy operation that's replacing a human decision, give the users the ability to override model outputs if they're not happy with them, and give the users the ability to affect model parameters and that's a way of both combining the human experience and the business experience that the stakeholders would have with the power of machine learning. So you'll see more about this in the article, but the two takeaways here is to keep trust first and foremost in your mind. And then secondly, the takeaway is we have seen that with time also because models get better, users trust the models more and features that we have had such as overriding model results actually tend to get used less as time goes on. Okay, so now you feel you know what to do and you're jumping into building your first machine learning feature, definitely a moment to celebrate, but I also want you to be thoughtful as you do that and we'll give you some tips for building your first machine learning feature. Start small. Think with a quick win mentality. If you even start with without building a model on your own and use a model from an existing library, that's a success. It will allow you to test your process and structure, build out the infrastructure as you go and understand what you need. And finally, make sure that you choose the best product problem and not the best machine learning problem because more often than not, these are two different things. Some pitfalls to avoid that I've seen companies fall into in the past. Machine learning is a powerful tool and as the saying goes, if you have a hammer, all problems turn into nails. So be very conscious to not become technology centric and to be user centric. If you are in Toronto like me, then you've probably seen that there are startups that do AI for insert between brackets, whatever business problem there is out there. And we have seen that a lot of these startups fail and the reason why, in my opinion, is because they've become sort of too fond of the technology to the point of losing user focus. Resist the temptation to talk about the technology in your product messaging. And sometimes this comes from business teams that are excited about machine learning. However, as good product managers know the technology should be as inconspicuous as possible for the user, so shouldn't be spelled out in, say, the UI of the product, for example. And good engineers would want to build things right and do things well. And this in our situation might translate into wanting to build out a whole data infrastructure before we even start working on machine learning features. I would advise against that because you are not going to really know what you need until you start building your machine learning features. So I would advise that you find some middle ground between building some infrastructure while you're building your first features and only invest after you get a picture of what is it that you're going to do with machine learning. And if it's even going to be an investment worth making in your special case. So the last words I want to leave you with machine learning is magical, but it's not a magic one. I want you to be both excited about the potential that it could unblock for you. But at the same time, be aware of the challenges and to go about it thoughtfully. Be business and user focused and not get too fond of the technology. Sometimes you realize that at the start, but as time goes by sort of the technology takes over. So as good product managers do, you should always see it as part of your job to keep your team focused on the strategy and the priorities. And pay attention to team structure and dynamics. These could make it or break it in terms of the success of your machine learning initiatives or at least the efficiency of the teams operations. Thank you so much for listening. If you want to connect in the future, you can go to my website and hope you found this useful and see you perhaps in the next talk. Cheers.


Tomorrow, on Thursday the 12th of December, the people of Britain will be asked to make a momentous decision in a general election, which frankly has been the dirtiest, the most vicious election campaign I think in the history of modern Britain. I believe that in order to find anything remotely resembling this, you'd have to go back to the year 1924 to the notorious Zinoviev letter scandal, which brought down the Labour government at that time. But even if you were to compare that avalanche of lies, I think that it pales in insignificance, in comparison to the avalanche, the torrent, the tsunami of lies and distortions and misrepresentation and sheer slander that has been heaped on the Labour Party in recent weeks by our wonderful free press so-called, I don't know what they're free from, free from telling the truth, free from the influence of big business hardly, but free from any semblance of shame in the way that they have viciously denigrated and attacked the Labour leader Jeremy Corbyn. I think you'd be hard pressed to find any example in recent decades, in recent history, for a smear campaign, an organised, orchestrated, deliberate campaign of smear directed against one individual, as has been directed against Jeremy Corbyn in the press, in the media, in the newspapers every day, every evening, every broadcast, every news coverage, specifically designed only for one thing, to discredit and undermine one man, the leader of the Labour Party Jeremy Corbyn, in order to conduct this campaign, they've even called in the aid of a rather important individual, the Almighty, yes, oh yes, yes. The Chief Rabbi's services have been called into action to add his voice of denunciation to this man. Incidentally, I just discovered that the Wiesenthal Centre recently published a report in which they said, get a load of this, just sit back in case you fall down, that in the entire world, out of about 2,000 anti-Jewish anti-Semitic incidents or whatever, the most dangerous, the most appalling was that of, guess what, Jeremy Corbyn's Labour Party in Britain. Now you will forgive me of being, perhaps I've got a nasty suspicious mind, but you see this so-called anti-Semitic smear, because that's what it is, a smear without any content, no real evidence or content behind it whatsoever, is orchestrated in a very strange way, in a very orchestrated, deliberate, very effective way, I might add, which frankly makes me suspicious that maybe there's some kind of invisible hand behind this campaign, and I think that this invisible hand could quite well be traced back to Israeli Secret Service, the Mossad, quite effective people, in an attempt to prevent Jeremy Corbyn with his record, of course, of support for the Palestinians ever coming to power in Britain, but there we are, that's just a suspicion, which I will not insist upon. And of course, immediately they all piled in, all the rest of the God Squad immediately piled in, the Archbishop of Canterbury, yes, the Jewish there, of course I've mentioned, and even the Hindus have been involved in a moody, that ultra-reactionary right-wing element in India, as organized forces in order to discredit the Labour Party and prevent a Labour victory in Britain. All that was missing was the Jehovah Witnesses and the Flat Earth Society, and then you really would have been in business. Yes, but in spite of all this vile abuse and slanders and smears, despite all this colossal campaign, this deafening campaign, it's completely one-sided by the way, the role of the media is particularly scandalous in that respect. You can contrast the hard-hitting attacks on Corbyn when he is attempted to speak on television to the kid-glove treatment of Boris Johnson when he has been in interviews. In fact, he's so reluctant to have interviews at all that I read in this morning papers, quite astonishingly, that he took revisions of the refrigerator in order to avoid being interviewed by Piers Morgan, I think it was on the BBC, on the radio.  Yes, a massive unprecedented coming, and yet, oh, by the way, I've forgotten the most important element in this campaign. Yesterday, the news was full, the TV's were full of a statement, a scandalous interview by John Ashworth, a member of Labour's Sado cabinet, I don't know how it held somebody like that, a Blair like that, that could be included in one of Corbyn's cabinet. We have to take this up very seriously after the election. A vicious tirade against the Labour Party and against Jeremy Corbyn, a defeatist, by one of the main spokesmen, Labour's Sado health spokesman, attacking Corbyn, attacking the Labour Party and so on. So, you see, what I can say is, in a sense, we should thank Mr Ashworth, please thank John Ashworth, because he has revealed, he has let slip, the mask has slipped, and what he is saying, what all the other right wing fifth columnists and proto-tories in the parliamentary Labour Party are actually thinking. All these ladies in general, every single one of them, is actually wanting and wishing and working for a Labour defeat. They want Labour to be defeated, because Corbyn victory, of course, would undermine the position, whereas what they really want to do is not to defeat the Tories, or Boris Johnson, but to defeat the main enemy, which of course is Jeremy Corbyn. Now, with this avalanche of gangsters ganging up on the Labour leader, one would imagine that Labour wouldn't stand a cat's chance in hell. It's no wonder, by the way, that the Poles gave a big, or gave rather, gave because they've narrowed, gave a big majority to the Tories. It's no wonder that even Boris Johnson was extremely unpopular for obvious reasons, one would have thought. These more popular, that Jeremy Corbyn, rather, is even more unpopular than Boris Johnson. So they say, and so John Ashworth, of course, obligingly repeats. One would have thought that Labour wouldn't stand a cat's chance in hell. And yet, and yet, and yet for reasons which the pundits find utterly incomprehensible, in the recent weeks and days indeed, as the election approaches, as election day comes, the moment of decision arrives, the gap has been systematically decreased. The gap between Labour and Tories have been decreased to the extent where now they're talking about the possibility of a hung parliament, which would contain, by the way, the danger from their point of view, the standpoint of the big business and the capitalists and bankers who really run this affair, and run the nation, of course, irrespective of who's in number 10 Downing Street, it fills them with fear and panic. Oh yes, I can detect an element, a note of panic, in the conduct of Johnson and his advisers, including the incident of the refrigerator. It's because they're in a state of blue funk because despite all their attacks that they've failed, they have failed. You see, Labour is recovering on the basis of a left-wing program, which indicates something about the real mood that exists among people in Britain, who have fed up after 10 years of constant attacks, reduction in living standards, viscous conservative austerity, attacks on the national health. The picture of this poor young kid that was on the floor of Leeds General Hospital was shocked the nation. The same day, by the way that that photograph appeared, a good friend of mine who was due for a cancer, a serious cancer operation. His operation was cancelled when he was on the trolley waiting to go into the operating theatre, having done all the preliminary reasons. Cancelled why? Lack of beds. So the national health is in crisis, is in danger.  The American big pharmaceutical companies, they're licking their lips, they're licking their chops, they're rubbing their hands at the prospect of a conservative government which would open up the national health, of course, to private decision. You better believe it, but leave that to one side. Despite all these things, there has been a reaction in favour of Labour in recent weeks and days indeed, particularly among the youth. All the opinion polls agree that the great majority of young people in Britain fervently desire a change. Who can blame them? And indeed, a swinging behind the Labour Party in droves, in masses. Corbyn is the only politically little bit that's been able to adapt to all big political mass meetings in different parts of the country. There's been a surge, particularly of re-energising, if you like, of Labour's activist base, particularly the young people, not only the young people. A surge of activist participation in the election campaign, particularly in London, but not only in London. It's throughout the country is the case. Liverpool football fans in your last night, in a football match, I think it was against an Austrian team, yes, the Salzburg, I think, were actually captured on TV in the middle, this is abroad, this is not in Britain even, chanting, oh Jeremy Corbyn, it was picked up on the TV screens. So even there you see the mood of the masses which is beginning to react seriously against the Tories and the class of exploiters and parasites, which they stand for. So many people have volunteered to help Labour in the election campaign tomorrow, that in London, I think in Putney the other day, 700 people turned up to canvas. So many people have volunteered for election day, that they've had to be redirected to the Midlands in order to go to other seats. And therefore let nobody tell you that there is not a radicalised mood in Britain among the working class and among the youth. There is of course, despite all the attempts to sidetrack the nonsense of the Conservatives or get Brexit done, that was the slogan and so on. And on that note of Brexit which has indeed confused and polarised Britain in a very unfortunate sense, let me just make a final appeal to those supporters of Brexit, there are some, quite a few, which are contemplating maybe voting Conservatives only on that issue, not because they agree with the Conservatives, they don't all agree with Boris Johnson, they certainly do not. Let me just quote you a little example from history. You may have heard of the Battle of the Boine, that was the famous battle in the 17th century where the Protestant forces of William of Orange defeated the Catholic forces who supported King James in Ireland. And after the Battle of the Boine, King William of Orange was crossing the river Boine in a ferry, this was supposed to be the case, I don't know if it's two or not but it's the story. And the ferryman who was a poor Irish peasant didn't know anything about politics or battles or anything like that, he asked, he didn't know it was the King, he didn't know it was King William, he said, asked him, who won the battle? The reply of William of Orange was as follows, my man, it matters not to you who won the battle because afterwards you'll be a poor ferryman just the same.  Britain will be sold at bargain prices starting with the national health of the American capitalists and so on and so forth, that's so much for the sovereignty of Britain where Johnson is a bootlicker of American imperialism in general and Mr. Trump in particular. No, no, no, there's no way forward for the British working class on the basis of capitalism this vicious unjust cruel system which the Tory party represents power excellence.  There's no future for you in a capitalist Britain whether it's in or out of the European Union, it's entirely a false position, don't fall for this my friend, stand for a class position which must be above all the defeat of this reactionary anti-working class right-wing conservative government and the election of a Labour government pledged a genuine socialist policies, the policy undoubtedly represents a big step forward. This is the only way to clear the decks for the real transformation of Britain in the interest of the majority of people, people like yourselves like myself, the working class, the class to which we belong. That is the only way forward for Britain. Let's make a start tomorrow morning by voting massively against Johnson and the Conservatives for a Labour government and then fight for the implementation of genuine socialist policies.


Everybody forced here we're up in 2022 contenders optic football hobby 10 box half case number 14 random team style find these results Any time late sports cards calm. Let's get started. All right, I'll get knocking a recap gonna be Still behind trying to make up a little bit of time here if we can Harris Isaiah Pacheco Got a silver rookie ticket out of Malik Willis on the RPS Titans nice card and an aqua rookie ticket auto to 99 Jelani Woods Colts Green Pulsar rookie Browns David Bell 30 of 30 and winning ticket pink Mahomes chiefs the 75 Swift and James Cook Got a Cameron Thomas aqua rookie ticket auto to 99 for the Cardinals George Pickens variation rookie ticket RPS aqua 24 out of 30. We'll get this one magged for you Steelers Pickens live and on card into a man. We got red rookie ticket Kyle Hamilton Ravens to 175 And a Josh Allen X's a nose blue Jersey match one of 99 or a color matches a pretty base Broncos rookie auto silver mantra Washington rookie ticket auto and Aidan Hutchinson rookie ticket auto our rookie ticket RPS red Going to Detroit. This one is number 87 of 149 Lamar Jackson red for the Ravens Scott paid It's 175 and Nick Chubb X's and O's pink 17 of 75 browns Harris rookie ticket Kyle Hamilton a David O'Jabbo rookie ticket auto for the Ravens Rookie of the Year contenders auto Christian Watson on card for the Packers My 75 out of 75 that's a dimple on the left. I'll send a box wrapper to the Packers, but There's no case it but there are some there are some short prints Got red Trayvon Walker for the Jags number 2175 And Jets Garry Wilson blue X's and O's to 99 But short prints will be like Ligitness now contending. I think those are the only two short prints But you can check out the checklist right there Lincoln Shack Hasn't really stopped like non-stop just about McLauren and picket rookie base Jeremy Ruckett rookie ticket auto for the Jets Oh gold variation rookie ticket RPS Sam Howell a one out of ten commanders what a hit A disgusting color match red two Is that a gold behind the two red to 175? I believe 175 nausea Harris And yep, the gold is Sam Howell 10 of 10 X's and O's Two gold Sam Howells in the box Congrats commanders you kidding me dude Todd with the commanders golds Guys if the power goes out, I'll have to stop right here. Uh, there's like really strong winds right now And the lights just flickered if you didn't see it so our might go out Pepe Williams rookie ticket autos silver for the Ravens It's a 99 Packers rookie ticket auto aqua christian watson nice hit Russia this break you right red chimera saints to 175 And picket X's and O's pink for the Steelers number to 75 It's really coming down I might just stop for a second right And take this out and just stop for a second. That's supposed to be this bad But the wind on the I think Dude is bad outside right now christian means that let's quarterback There you go Dude is bad here right now randy. All right. I'm gonna go for it It's jaymo. We've got a reggie wane Two of ten gold all-time contenders auto for the cults. Wow That's sick and on card reggie wane Kenny picket redemption rookie ticket rps auto teal for the Steelers So I guess I was saying aqua. That's teal actually so this should be to 99 We'll get that magnet of course red miles garret to 175 The browns and pink michael parson's cowboys to 75 Dude a popka does get it's like literally the worst storms here like every year Doesn't float a lot though, so I don't know randy. We live in winter guard. My wife said it's crazy there too right now Saw a scarter now. It's not windy But probably windy somewhere else now Cameron thomas ricciato cardinals Just started pouring there. Yeah, you're probably getting what we just got That was like really when it had to be gusts of like 50 or so I haven't heard like that and I've only heard like that once or twice before Variation rookie ticket on card out of breeze hall. There we go rps for the Jets There's a silver eklard chargers And a silver mpp contenders joe burrow Training out the carnival. Oh the carnival ends at 10 anyway, so they'd be all right Lions in the qb all the lion's hands.  We're supposed to take it 14 I thought Cool Not upset so A green pulsar fill sims out over the giant silver to 25 So it's not windy here anymore, but that doesn't mean the power is so can't go out. You know what I mean? I guys and variation Red rookie ticket rps garret wilson for the jets This one is 43 out of 50 It's been a very good half case Red white and blue mojo isaac spiller rookie six of 13 chargers First one of those you've seen and green pulsar x's and o's of trevor lorenz. What a box that was 10 of 30 trevor lorenz jags Christian dollars, let's go I'll take it I'm getting worried now. See I told you guys Nice flickering He's back. I'm trying to finish trying to finish the break Greg dulcich broncos rookie ticket auto Malik willis variation rookie ticket rps. Nice hit for the titans And take the qb. Yeah, so red. I'll jeer to 175 rookie ticket for the falcons And a rainy moss silver x's and o's for the vikings that'll do it. Thanks again everybody On to the next half case


The Danish story is that, amazingly enough, there seems to be a lot of backing for this green conversion or revolution in the Danish society. When we last year in these days decided upon a very ambitious 2020 target, 95 percent of the seats in parliament voted for this new rule law. And also Danish industries, the cultural sector, everybody supports it. It's a very amazing thing, but very helpful, by the way. And the story is that right now we, in the Danish society as such, we use around 850 petadules of energy. And when we approach 2050, well, everything should be turned green, renewables. And at the same time we expect to grow the economy, more or less to double the GDP over the next 40 years. So without, I mean, how can we make this equation, I mean, run only by efficient energy efficiency, very, very harsh measures on the energy efficiency. And also, as Jan told us about the electrification, because electricity is such an efficient tool to combat energy consumption. So that's part of the equation. The other part of the equation is that in the renewable side, well, when we did a study two, three years ago, we had a climate commission doing a thorough study saying, okay, how can we decarbonize the Danish society up to 2050? Is it doable? Yes, it is doable. We can do it. We do have the technology at hand, more or less. Secondly, is it feasible from a financial point of view and from a competitive point of view? Yes, it is feasible. It will not destroy the Danish competitiveness going down this road, as also Jan confirmed in his study. We did a similar study in EnergyNet because we have, in fact, also the responsibility for security of supply. And we do also have the responsibility of an efficient integration of renewable in the Danish society. So it is affordable for industry, it's affordable for consumers. And it's so exactly the same. It is doable. And we can do it without destroying the competitiveness of the economy if we do it right. If we do it right, because the cost can't be too high. We see that in Germany right now, because it did not build out the transmission capacity in due time. And that's the reason why now the cost of this conversion goes through the roof. And we have some social unrest because of that. But it can be done right. And I'll give you some ingredients as we see it, how to do it right. So basically when we approach 2050, our study show, yes, we have plenty of wind. We have, I mean, more three, four, five times as much wind resources as we need as we approach 2050. But also we have some biomass that we already today use. As you can see, it's a big chunk. But also waste is going through this combined heat and power processes. We have taken out three weeks in November from last year. You can see the pattern here, the gray, that's the load. You can see the weekends, you can see the spikes when we have cooking at evening. And that's approximately, it was 32% of classic demand we had as in wind. The wind accounted for 32% last year of total consumption. And you can see the green part here, that's the wind production. 2020 is seven years ahead and the plan has been laid down. We will build a number of offshore wind parks. We are now constructing the offshore platforms and taking the power ashore. And you can see here now, we see that many more hours we have much more wind generation than we can consume. Moving to 2035, because also the politicians said in 2035 we are not allowed any longer to use coal, oil or gas for heat and power production. We will have to turn these sectors green. The only thing left then in 3035 will be transportation, but the other take something longer. And that means that about 80% of classic demand will then be covered by wind, rest by biomass, basically. And then we go to 2050 and that's a rump scale, if we take the rice scale, that will be the picture. And then you can see really the challenge we have in front of us in terms of how to balance how to make sure we have security of supply also in 2050. We have some peaks, some valleys. We have more production here. How do we solve it? Well, first of all, I want to highlight that you cannot have 32% wind in your system, not even 20% wind in your system if you do not have a well function of markets. The market has to deliver most of these balancing services. So that allow the power to flow where the willingness to pay is highest. We are not allowed to shut down wind turbines in Denmark. They will always produce. That means that it's critically important for us that the market will then absorb and give the right price signals to the other production facilities we have. So if you do not have a market, I do know you have a market here in Ireland. But also to see the market in a bigger perspective, now in our part we have had the Nordic market for many years and now we are developing the Western European market and that is helping us, I mean joint expectation. Second thing is interconnectors are compared to that. It is that to be able to balance the system across a nation and to share resources, to share reserves is critically important. We come back to that one. And third thing is new flexible consumption. We need to be able to really absorb the amount of power when it's there, meaning that for instance the heating sector should be able to take the power. Also the transportation will be able to take some of the excess power we have. And in the valleys again interconnectors, hopefully there will be some power north or south and east and west of Denmark that we can import in these days, hours and domestic flexible power production. Well we will need power production also 10, 20, 30, 40 years from now. We have in Denmark, as you may be aware, a lot of district heating, two-thirds of all households is on district heating and they are provided with heat by combined heat and power. And they are very flexible systems we have, gas fired typically, and they are able to produce power when prices go up and it's a good business. So basically that's it. Here we have the map of interconnectors in and out of Denmark. We have already three HVDC to Norway, we're building a fourth now. We have three to Sweden here, it's also HVDC. And strong AC connection here and AC connection to Germany. And HVDC here to Germany and building a new one crickets flag, HVDC, to combine grid solution as mentioned before. And then we have a study of building a new connection to the UK and to the Netherlands and yeah.  Our peak load is 7 gigawatt, our interconnectors and I think that's comparable to yoga. And our capacity in and out of the country is 7 gigawatt. Meaning that I could manage security of supply without having any single power plant running in Denmark. And really when I add all my capacity up together, right now I have 7 gigawatt of thermal capacity, I have 7 gigawatt of interconnectors and I have 4 and soon 6 gigawatt of renewable. So basically I can supply and a good day I can supply three times the power as we use. And that calls for interconnectors basically, that calls for interconnectors. And all these interconnectors, we have a huge inflow of money because they are really money makers, these interconnectors. Both in terms to my pockets, but also in terms of society. Because it helps, I mean directs the power to where the willingness to pay is highest. The value for society is highest and it helps us to eliminate the non-productive power plants. Because only the productive power plant will be allowed to run, the unproductive will be kicked out of the market. Even if we have 7 gigawatt of interconnectors, we are now building two more gigawatt because they have good business cases. And it tells me that, for instance, Germany only have a very few interconnectors in and out of Germany that there is room for improvement. Especially in the UK, I think they have only 4 gigawatt in and out of the UK and they have a peak load of 70 gigawatt or something like that. So there is a tremendous, well, good opportunities there. And then finally, this is, I know it's a very busy slide, but it's basically very simple. It says we have a well integrated power infrastructure, that's a blue one. We have a well integrated international gas structure that works. And then we have a heating system which is meshed between the power system and the gas system. Moving forward to make this integration cost effective, really we have to get the maximum out of this flexibility which is built into the heating system. Because our heating system can store heat for 2, 3 days, a week, some of the plants for a month because they have big tanks where they can accumulate heat. So that means that when power prices is low or we have too much wind, they should just produce heat via heat pumps or boilers. Use electricity, very efficient. And when there is no wind, I mean use the storage facility. That's one part of the equation. So another part of the equation is that when we have too much power, we can convert the power to gas. The problem here is efficiency, we lose 20, 30, 35% in this conversion. But because of this conversion either requires heat or creates heat, if we can combine that with our heating system and use this excess heat, I mean we can bring down the losses of the conversion. And also the biomass can be by thermal gasification or by biogas put into our gas storage facility. We have a lot of gas storage, underground gas storage facility where we can store energy, green energy also to cover the values when we do not have wind. So market interconnected to rethink the power, gas, heating and transport system and make them integrate. I think that's three critical elements to bring down the cost of this green revolution. That was my presentation.


Meritocracy is a political philosophy which holds that certain things, such as economic goods or power, should be vested in individuals on the basis of talent, effort, and achievement, rather than factors such as sexuality, race, gender, or wealth. Advancement in such a system is based on performance, as measured through examination award demonstrated achievement. Although the concept of meritocracy has existed for centuries, the term itself was first created in 1958 by the sociologist Michael Young. The Oxford English Dictionary shows the first use in 1956 and gives this citation. 1956 A Fox and Socialist Com. May 13 over won the meritocracy, the society in which the gifted, the smart, the energetic, the ambitious and the ruthless are carefully sifted out and helped towards their destined positions of dominance. Young's entry is listed as 1958-Meter Young Rise of Meritocracy IV. 71 Before the meritocracy was fully established, age stratification as a substitute for the hereditary order may have been necessary for the sake of social stability.


All right, let's do some main New York City factory footage and wouldn't be in New York City factory footage I'm gonna have to start Recording the audio because it is so loud the Disney headquarters. It's being built across the street It is as big as it is loud now, but it is what it is and Your Disney plus membership at work stream Hawkeye


Hey what's going on guys, welcome to a new video. So today then we're gonna be looking at three different dropship installs, each that have done seven figures in sales in their own right, and we're just gonna be looking at the kind of things that we can learn from them. So we're gonna be looking at their store, their best selling products, their Facebook ads. But before we get into store number one then, I just wanna mention quickly, as always, I am gonna wait a free one-to-one call with me on this video. If you want to enter the draw then, all you have to do is simply like the video and leave a comment down below. And if you commented on my previous video then, just make sure you stay tuned to the end of this one where the winner will be announced. And that being said then guys, let's get straight into store number one. So this is store number one then, they're called Keen and Social. Now in case you're wondering where I find these stores, it's simply the exchange marketplace.com. It's where people come to sell their Shopify stores and you can filter by, if I just show them, business type and you can go by dropshipping. So you know exactly what the stores do and you know exactly what they turn over to because the numbers can't be fudged. If we scroll down, just to quickly show you, it's all verified by Shopify so it's legit sales. So it's really good place to come to find information on competing stores. So anyway, the first one I want to show you then Keen and Social, if we just head over to their website, I'll take you to the homepage just so you can see the kind of like design and layout of the page. The reason I wanted to show you this one so much is because in my opinion, actually looks quite amateurish but if we look at their sales numbers, they've did over 3 million I think so 3.1 million and it's just kind of approved that you don't have to put a lot of time into your store to get effective results and I just want to show you too is that the theme they're using is Brooklyn which is actually a free theme so you don't actually have to invest in a paid theme either to produce some pretty decent numbers. So as we can see then the store looks pretty basic. The next thing I want to show you though is actually their best selling product and the actual product page which is these leggings here. So it's $40, these leggings are really expensive actually in fact we'll try and find them on AliExpress after this. I mean if we just have a look at the product page like it actually looks really basic but as you can see you don't actually have to have like a really professional looking page. Now obviously it won't hurt to have a really professional page but to do over 3 million and the majority of the sales come from this product and just look at the product page. It isn't great but it just shows like as long as you've got a really good product and a really good Facebook campaign like they're the two kind of main ingredients that you need. I just did a quick search on AliExpress for pleated leggings and it tilt can be like literally 10 seconds to find these. And as you can see the profit margins are just crazy. It's the same product all in these different colors but the sale price is 10 pounds. Let's see what shipping is to the UK. So four pounds. You're looking at about 15 pound delivered to the customer. They're selling them for $40. So it's even got really good profit margins too. So it just shows that a decent product with a decent campaign and a half decent store really at best can produce just amazing results. So if we just head over to their Facebook page just to see if they are running any ads to see what the kind of ads look like that produce those sorts of numbers. But I'm always intrigued as well to look at the kind of posts they put on their page, the kind of content. As you can see they've got 50,000 followers which is decent and just a kind of structure because you'll find that a lot of pages will stick to the same kind of structure and template of their posts.  Same kind of setup here. And just kind of if we just let this load up same kind of setup. And then something just kind of out of the blue just some kind of like car seat cover. And then if we go into the info and add to see if they're running any ads. So they're not running any ads at the moment but it's always worth having a look just to the way they structure their posts because by the looks of things they just use images and then kind of like this same layout. So some kind of question or statement followed by a short URL. So again it shows you don't really have to put that much time and effort into what you're doing. You just like the key ingredient that can make the difference between failure and success is who you're targeting on Facebook and the actual strategy you're using. So anyway that was store number one. Hopefully you guys found it useful. Let's get straight into store number two. So this is store number two then average monthly revenue 40K they're asking 20K for purchase price but their total revenue then is 1.3 million. So as we can see still a seven figure store. So let's just head over to their website then and see what we've got. So to me I think this is probably a shop to my website if we have a look turbo which is like a shop to my theme and it looks a lot better than what we've just seen but it's still not great to be honest and as you can see that these are 100% AliExpress drop shipping products. Let's just go straight to their best sellers then. So the best selling products is this tattoo arm sleeve and you've got like this makeup cover, some concealer, the pet glove. And I was talking to somebody recently about this glove and so these are the best selling products then. Let's just have a look at the product page. It's always worth having a look at the product pages because essentially that's gonna be like the first introduction to your customer. That's what the customer is gonna see first. So this is essentially the page which is most important in my opinion on your store. And to be honest it looks pretty good as I would expect with it being a shop to my theme the shop to my product pages are just unbeatable in my opinion. So they've got like a video which works which is pretty cool, all these different product images. And if we scroll down they've got reviews too which is always a key ingredient again to a successful product page. So if we scroll to the bottom then let's try and find their Facebook page and just have a look at the kind of content and hopefully that adds to the currently running too. Over 100,000 likes then which is good. And one thing I wanna point out as well is that these guys are clearly a general store. General stores get a lot of bad press but and I don't know why to be honest as long as you've got a good product, good campaign and decent product pace that's all that matters. People aren't gonna stop buying from you because you sell other types of products. So yeah, I don't really understand why they get the bad press. But anyway, let's have a look at what kind of ads they're running. So these guys aren't running any ads either actually which is surprising. So let's just have a quick look at the content the kind of posts they put out. So let's just have a quick look at the kind of content they're posting. These guys got over 100,000 likes as well. So just a simple post should bring in like some pretty decent numbers for them. So change in profile, nothing really that interesting. We've got some sort of video, full body pillow. Looks like they've made that themselves too. Now that is a really important thing as well that people underrate is that making your own video ad something like this that actually shows the product being used by a real human being makes all the difference. We've got like some LED strip lights and then some spinner cell phone thing. Then let's just have a quick look at a couple more. Dog toy car, no way that is so cool. So yeah, that's a pretty cool product. In fact, I just wanna talk about this car for a second because in my opinion, it just kind of summarizes what makes a really good product for Facebook and social media. And essentially that is what I call a social product and social products are things like this that people look at and that people look at and they say, oh, that's pretty cool. And because it's cool, they wanna share it with other people and that is the key. Try and think, when you're thinking of products to sell rather than think, would someone buy this? Think, would somebody share this? So would this post or would this video, does it have potential to go viral? Would somebody want to show somebody this because that should be kind of like your main focus because if you can like drum up a lot of attention by people sharing and commenting then you're going to make the sales and they're kind of like the key ingredients to a successful product. And with that being said, then let's move on to the third store and this is an interesting store and these guys are running ads and it's quite an interesting strategy that they're using as well. So I think you find it quite interesting. But anyway, let's have a quick look at their store then. It looks a bit better than the first one we looked at but as you can see, it still looks pretty basic. And let's have a look at what theme they're using. It looks like a paid one, but I'm not 100% sure. Commerce inspectors aren't working at the moment. I'm just going to refresh it quickly. Let's have a look. So it's turbo with bold currency. Ah, right, that's interesting. So let's have a look at their best selling products then. The LED dead color, believe it or not. Some sort of like poor things you can put in your van. Not about $15. They've got like a dog seatbelt. All kind of like the stereotypical drop shipping products you would expect actually. I've seen all of these before and actually sold a couple of them myself. So anyway, what I want to show you about these guys is, number one is the amount of traffic they're getting. So you can see that they're legit. This is similar web, by the way. You can do this pretty much any website as long as they get a significant amount of traffic. It's going to give you all this interesting data you can use. And if we just have a quick look, so October since 120,085, 130, 140, 110. So these guys consistently averaging about 100,000 visits a month, which is really good. You can have a look at the country as well. So you can see exactly where these people are coming from. So majority is obviously America. And if we scroll down, then there's loads of other information too that they give us. So we can see like where the traffic's coming from and this is desktop, you can see referrals. You can see all sorts of different information, definitely worthwhile tool. Something I wanted to mention in the video, because when you are doing product research and looking at different stores, definitely worth checking out. So if we just have a look at their Facebook page and just to see the kind of ads they're running, we'll have a look at the content first. So over 300,000 likes again, which is really good. Simply these guys just putting out a post with 300,000 likes it's gotta bring in sales. And that is the importance of building a brand and building a following. Because when you have like this amount of people, then you don't essentially have to spend money to make money. Simply putting out a post on your page for free is just, it's gonna bring in sales. So definitely something to consider. So these guys are advertising as giveaways. Let's just open up in fact and let's see what it says. So they're doing a free plus shipping offer. There's not a lot of people still using that strategy. So it must still be working for these guys. Otherwise they wouldn't be doing it. And if we scroll down, it seems to be kind of like the common technique they use. Almost on every products in fact, free giveaway. Some kind of like funny post, free giveaway. So that seems to be like the kind of thing they're going for. And if we just have a look at their info and ads too. So this is ads just in the UK by the way. And again, free giveaway. But if we actually click on it, then I'm assuming it'll be a free plus shipping offer, which it is. So they've got, they're saying that it should be 30 pounds crossed out as free. And they're doing a free plus shipping offer. So these guys are absolutely smashing it with free plus shipping. Just to double check these guys are drop shipping these too. I literally head over to AliExpress and it took me like five seconds to find these. So they're a pound then as you can see delivered. These guys were selling them at, so they're selling them free. And in fact, let's see what they're charging for shipping. Let's go to check out and see how far we have to go before they'll tell us calculate the next step. So in fact, I'm just gonna go ahead and fill these out and move on to the next step. So here we are then on the next step, as we can see they're only charging $8 for shipping. So I think given that, I think the kind of strategy they're going for is using free plus shipping methods. They're probably using really low budgets and probably traffic campaigns too, just to try and basically build up their following on Facebook. If we just go back to the homepage because they've got so many likes, I think that's the strategy they're going for just to bring in as much following as likes as possible. So when they do put out these organic posts, they can make money on the backend that way. And that's quite an important note in fact to consider is that even if you don't make money necessarily on the front end, so when you put out an ad, if it doesn't necessarily bring in your profit, it doesn't really matter. If you can find other ways to kind of make money on the backend, then that's definitely one of the keys to running a successful and profitable business for a sustainable amount of time. Facebook ads can be really difficult sometimes to bring in money initially, but as long as you keep track of your average order values and how much your average customer is worth, it's called customer lifetime value. I could do a video on it if you want to. Then the whole game becomes a lot easier because if you're acquiring customers for say 15 pounds, but you're making a loss on the front end, but then you know every customer is worth 20 pounds in the long term, then you know that you're still gonna make a profit in the long run if that makes sense. But anyway, that's completely another video. And with that being said then guys, I'm gonna wrap this one up. If you're still watching the video, hopefully you found it useful. If you did, then awesome. All I ask is that you please do leave the video a like. And if you wanna be entered into that draw then for a chance to win one-to-one call, just make sure you leave a comment down below as well. And with that being said then, let's get into announcing the winner of the previous video. So here we are then guys on my previous video. It was a short-term drop shipping strategy. If you guys wanna see kind of like the flip side of that like a long-term one, then make sure you let me know in the comment section down below. So I'm just gonna take the URL then on top left, copy it, head over to our random comment picker, get YouTube comments, 70 unique comments, which is awesome. In fact, I think that's a record. So thank you very much guys. We have just passed 5,000 subs as well, which is absolutely awesome. So thank you. And the winner then of the previous video is Kyle Bartlett, awesome video jack. So thank you very much. Reach out to me on Instagram then Kyle and we can get that call arranged. And guys, if you just wanna get straight down to business instead of constantly trying your luck and just book a call straight away, then you can actually do so. There is a link in the video description down below. So make sure you go and check that one out. And that means something guys. Thank you once again for tuning in for another video. And I'll see you in the next one.


This is how you would do that. You take your mouse and click over on Settings. Go down to Setting Line. Click on Settings there. Then you go over to Calendars. You click on Calendars. Find the calendar you want to work with. You're going to click on Dustin Garza's calendar to embed it. We follow these instructions which is to cut and paste this embed code on your website and your calendar will appear.


My name is Hadi Yasin Bakalua Seshwanyi, based here in Uganda. I'm a guy who worked as a security guard in the UK for 19 years, and then I was like, hey, I've had enough. I need to go back where I belong. This is Africa, this is Uganda, where my love is, my motherland. I came in back here, realized that there was a problem in construction. There were many Ugandans working in diaspora, and they wanted to construct. They wanted to do something back home, but they were afraid. They couldn't do it. Many people were eating their money. And I thought to myself, this must be an opportunity. That was a problem. When I say to myself, listen, if you want to make it in life, look for a problem. And when you see the problem, just come up with a solution. For the last two years since I opened up my company, I've constructed over 200, 200 structures for people I've never met. They only see me on social media. This is my story, and I've made it. You can make it. Africa is wonderful. I can't believe that I'm meeting you.  You need to meet Hajima Kalwa, he's the guy with the story. I'm glad. Nice to meet you. Nice meeting you to my brother.  How long have you been back? I've been here for two years. Two years? Yeah, two years. And you built a mall in two years? Yeah, I've built a mall. I've built ten other apartments as well. That's for myself. And before that, I had 35 apartments. And that was before I left London. No. You did all this in two years? In two years. Were you born in Uganda? Yes, I was. I was born here. I left Uganda when I was 19. And I stayed in the UK for another 19. So my life has been 50-50. But my heart remained in Uganda completely. I never connected with the UK. Never. Before I continued. If you've done all this in two years, when I came to this country, I realized that so many people are telling me that anyone who makes it big in Uganda is a friend of Musa Veni. I don't laugh. Are you a friend of his excellence? If everyone who makes it here is a friend to the president, he must be having many friends. Because we have many billionaires here who are quiet. You never even get to hear of them. Come on, people are making it. And when I sold out my story actually, the first thing I started doing is trying to change people's minds. I tried talking to them. Now what you're hearing, that is a poor mindset. You know, in your mind, if you cannot make it, if you don't see it for yourself, you cannot see it for others. So when others make it, you're like, nah, it's impossible. It is impossibility all filled up in your mind. Come on, you must be having the same story actually. Well, on your side, who's Wade? What is he trying to do? He's nothing. Because I can't believe it. All the stories that I've done, incredible stories, but at the time I post the stories, people are like, ah, this person is in bed with the government. And I don't see it that way because I see people who started from scratch and have made it big in the country. Why you left London in the first place? I started... You know, when I was up there, I was a security guard. I had a lot of time to read. And I used to read a lot of inspirational stories. I saw people who came from scratch to being some, from rags to riches. And I thought to myself, I think I can do it. So from the word go, I was enriching my mind with good ideas. I knew it was possible. And I wasn't letting anyone tell me that it wasn't. So from the word go, all the money I had, the little I had, I was already planning it. You know, what is making people not make it in life is just living a life without a goal. Life without a purpose. Life without direction. From the word go, I had my direction. Actually, it doesn't matter how much money you're earning. What you need is a goal. Once you believe in your goal and you're going to go for it, no one is going to stop you. Trust me, there's nothing waiting for you apart from success. That's what I had for 19 years. And along the way, as you try to make it, the difference between someone who's going to make it and someone who's not going to make it is consistent. And for 19 years, I was like, listen, I'm going to make it in real estate. The reason why people are failing to make it in real estate is lack of consistency because it takes a lot of time. Someone who genuinely makes it in real estate will have to be able to withstand 10 years of pain, of displaying, and I had to do it for 19 years. Come on, what was left apart from success? 19 years. 19 years. And actually, I never saw myself in the UK. I was there, but my heart was in Africa. I knew there was no way out. Come on, I read stories. The difference between us and people who fail to make it, they don't analyse, they don't understand stories. I had seen people there from university. They go, they get good jobs. They go on finance. They end up having lots of loans. Then what happens? That's their life. They are captured. They become prisoners for years. I've seen suicide stories. People committing suicide. Why? They don't have way out. It's like working going back home, working going back home. I did all the jobs, cleaning, working in factories, all of that. I did it. But what I realised is the money is not even enough to save.  Even if you are working double shifts, because the tax is very high and the cost of living is very high. So on average, everyone ends up borrowing money to survive or to want the nicer things. And I said to myself, listen, actually there's one quote I read. I wasn't born to go work, pay bills and die. I was like, listen, I think this is what I'm doing. I have to change it.  Here I am in Africa. You called me last night. You said you wanted to meet me. Here I am. I'm living my life my own terms. I can be where I want with who I want anytime. This is the freedom. It's until you make it, until you test that freedom. That's when you know that listen, it was worth it. I can wake up anytime I want. That's what I want. What did your friends tell you when you wanted to leave the UK? I saw it earlier. Someone who doesn't see it for themselves, they can't see it for you. And let me tell you what it is. The people who believe in you last will be the people who know you. Pre-conceived denotion of someone.  Are you a family? Are you friends? Why? They saw you as a little boy. They saw you begging for one dollar or two dollars. They saw you going to the well. And they're like, who's this guy? Whatever you're trying to do, they're just like, that guy, he's got too much here. Who does he think he is? To them, what they believed in me or what they spoke about me, it didn't matter to me. This happens to everyone. So to them, it was theirs. And what I did, I think I heard Steve Harvey say it. The quickest way to destroy a dream is to tell a big dream to a small brain. So you are busy going around telling people I need to do this, this and that, this and that. Come on, they don't see it. They're not seeing the larger picture. So when you force it into their brains, what's left, apart from destroying it? So I was doing it and the job I was doing was, wasn't one of the biggest jobs. Come on, I was a security guard. For 19 years? For 19 years. I never changed a job. I was doing the same job. But listen, I had laid out my plan. I knew exactly what I was making. I knew my transportation. I knew what I was paying. All my bills. And I knew my balance. So I was working on the balance. I was working on the balance. And every four months I had made it that I was coming back to Africa. So I was always in the know of what was happening here. That's why even when I came back, it was so easy for me to integrate. How easy it was? It was easy. Listen, the first thing you need to do in life is to believe in yourself. This is something you're not going to get in class. It is something your parents are not going to instill in you. It's going to be you. The day you wake up and know that life is 100% my responsibility. There are people out there who are saying, oh, we are waiting for this government to come. Oh, I wish this government could do this and that. There was an American president during his inauguration. He said that this is not time for you to ask what the country can do for you. It's time for you to ask what you can do for the country. So it's not what others can do for you. It's you.  Normally, I summon myself in a meeting, personal meeting. I summon myself. Listen, yes, we need to sit down and I speak to myself. What's my relationship with my future, with my money? It's me. And I go through that conversation myself because the best conversation you can have is the conversation you have with yourself. So I make sure that I always review what I'm thinking, what I'm planning, and that is by myself. I make my decisions. I'm ready for the repercussions and what happens in the end. Once you know what's exactly happening in your life, you see, you don't allow things to happen to you. You make things happen. That's the road I took. I sit down on a daily and I review my life and I do it. That's why people say, oh, you cannot make it. Come on, he must be connected. Listen, people tend to mix what they want to be with the how. That's where they fail. You must know what you are or what you want to be without mixing how. Once you know that I want to be this, I want to be known as the most famous YouTuber in Africa, you make that decision. It's done, it's done, it's done. Then the how will come.  Listen, I'm a small boy from Ghana. Come on, there are many people out there who are doing it. It's that conversation you have with yourself. No one stops you. So just imagine if you met two people in a day and you told them what you're going to do and they told you that you can't do it. Listen, ten years, who knew that you're going to be here? Who knew that you're going to fly? Now, tell me, who believed in you in the beginning? They thought you were crazy. Listen, if they were like, this guy, I don't know why he went to China. I don't know why he had all this education. He should be putting on a suit, going to the city, looking for a job. What is he trying to do? Going in front of the camera? Trying to talk to everyone in the world? Is he crazy? His parents must be sick. Oh, they must be crying. They made a loss. Exactly. But hey, even your parents didn't know what you're going to be. So it's a matter of believing in yourself. Before coming here, which means you've saved a lot of money to come and invest in your first project in here. Actually, I saved some money at the beginning of 2003. I came back, you know, in real estate, you know how property tends to increase in terms of value. I bought this place like one and a half years ago. But if you go back and evaluate it just 18 months, it's not of the same value. Exactly. Okay? Now, I bought my first property after spending like two years in the UK. I was being paid the minimum wage. But the first thing is knowing all the information you need in the sector you're going to be investing in. So I knew exactly what I wanted. That's why even the little I had, I was valuing it. Okay? I came in here, I bought my first property. I think in today's equivalency it's around 1200. But after like 10 years, I sold it at 160 million, which is like 17 grand. Wow. That is Great Britain Pound. Exactly, appreciating. Yeah, it appreciated. In real estate, it's about being patient, being strategical and being patient. People think that they are going to wake up one day, they buy a prime land and then they start constructing. No, that is not an advice. I'm going to give someone who's trying to make it. Okay? In Africa, if point A is developing, just know that point B is going to be developing very time soon. And then point C will follow. Now what you need to do is to go in point B and apply a piece of land. If you cannot afford point B, go to C. That means you just have to wait a bit longer, but it's going to happen. Listen, land does not produce. But human beings produce. So the population, the demand is going to be there. It's a matter of thinking about it. And that piece of land, which I bought so cheaply, actually I kept on coming back here and I saw the guys I left who were still here struggling. They had made it and I was like, how did they make it? Listen, I go to England, Queensland, and I come back, these guys are driving Range Rovers. What's going on? What's going on? Now, in Africa, when someone makes it, instead of you coming close to them, asking them like, listen man, how do you make it? It's everywhere. It's in to us. It's inbuilt in us. What I did, I used to go to those people who have made it. Try to be friend of them. Most of them are not going to be welcoming. You know that.  You see. And what you are exposed to, you manifest. All right? I used to speak to them. I go around. I even used to check out beautiful buildings. I used to go to places like Cololo Munoño. Like, you see beautiful things. And I was like, all right, I think I can do this. Now, I had started with building like the small ones, which I could with my little I had. Then I realized that the place had become prime.  The land was going to give me some good money. And I tried it. And bang! It paid off. Someone bought it. And I went somewhere. I had already bought another piece of land for years. This had increased in value. So what I did, I put there something good. Beautiful. Actually, I went to China. Really? Yeah, I went to China my first time. Even my friends were discouraging me. Why are you going to China? Who's going to ship this and that? All those guys, they will steal your money. And I was like, listen, I was going. And guess who was telling me? My good friends. So it is always those close to you. If they don't have the courage, they don't have the interest, they don't have the information. So they don't have the same vibration as you've got. I went to China, brought my 20 feet container here, had beautiful tiles, beautiful lights. And I built very good rentals. All the small scale, but very beautiful. Within no time, someone reached care. I was like, all right. I think I like this. I think I like this. I was like, you like it? How much are you giving me? And the amount he said, I'll keep it to myself. But I was like, no, this is not happening. This is not happening. I got the money. Now, mathematics don't lie. When you're in a field, you start doing the calculation. But still, that mindset of going back to England and work was still, you know, disturbing me. I got the money, put it on the account. Went back, put on my uniform. Went security guard on the door. Good morning, sir. Good morning, sir. Good morning, sir. But I kept on thinking. I kept on writing. I was scared of coming back to Africa. I've seen many failed stories. People have come back here. They have come with the money, but they have failed. I was like, should I go? Should I not go? And if I go, I'm just going for like two, three weeks during my holiday. I'm just going to spend everything just, you know, recklessly. And you don't know how long it took me to write that resignation later. Actually, I wrote it, put it in my email, and I had to listen to a lot of motivation. I was listening to that, listening to that, just to get the courage to press send. And I remember. It's like yesterday it was around three in the morning. At night, I pressed it. I gave them the date. I said I was going. And this was like two weeks before. When the day came, and I saw someone walking into the door to relieve me. He's a good friend of mine, Stanley Robertson. He's a Ghanaian. Oh, wow. Very good friend of mine. I didn't want to say anything, but I had told another friend. He was a Cameroonian that I was going. I didn't want to tell anyone. I didn't tell my wife. I didn't tell my kids. Because I was so scared. I didn't want to tell anyone who can tell me that I wasn't going to make it. And when I walked out of that building, took one corner, second one, I started crying. I started crying. I didn't know what I had done, but I was flying into something. I was scared, but at the same time, after 19 years, it was the first day I was walking home knowing that I'm not coming back to work. So I was scared, and at the same time, I could feel the freedom. I was like, all right, now from today, I think life is going to be my responsibility.  It must be a different thing. But I went back home. I had saved some money, put it onto the account for my family to feed on. And the following day, I was on the plane. I came back here. And when I came back here, I had drawn all the apartments I wanted. I had the plans, and I was ready to do it. Worked one month. It was already going up.  You know how Africa is like. I got arrested. I was all over the news. And that's even how I came into media. I came in for all the wrong reasons.  I resigned my work. I came here, started construction is halfway. And here I am in prison. And I've left my family in the UK. The judge in the first place had taken me, they called me to remand. They take you some time there as they are investigating. But we managed to fight it. And after a week I was out. But you know, they were after me. Come on, I was making headlines here. I was making headlines. And people were going all over this guy. He came from the UK. He's coming to take his father's things. His father died. And he's here to grab everything.  And I didn't know how to do it. It was false. It wasn't the right thing. And I got one person, you know, a social media person. And I said, listen, I've managed to build my apartments. This was like now after six months. And they're done. I want to do a story about my apartments. Show them to people. Show them that this guy was a security guard. He came in here and he has his property. He's not fighting to take what belonged to the family. He was trying to put sanity in the family. So it was like a damage control program. We sat down. I told people my story, how I was in the UK. And when people saw the apartments I constructed, they were like, listen. This guy was a security guard. And he came up with this. But I was telling them how many years it had taken me to construct. You see, when you're telling your story and people see some genuinity in it, they can actually connect. Exactly. If you tell your story and it has got a background, people can buy into it. There is so possibility in it. Because I told them I was buying this. I was selling this. I was doing this. And then I was being paid this amount of money. I didn't know how it was going to capture the people. The following day when that program was posted, forget about the damage control we were doing. And when I looked at my phone, during the interview I had given out my phone. Oh my God. It was bang bang bang bang bang bang. Listen, I tried to make it in Africa. I tried to construct my house. Oh, my brother ate my money. My friend, we are not even talking. Some people were like, OK, all right, OK. So how do you want me to help? Can you help me? Can you help me do this? I was like, all right, I don't think I can do that. I don't think. And then I was like, listen. These guys are calling me, asking me to help them construct their houses. How do they even trust me? There's a saying that success finds a prepared mind. I was like, listen, I need to be prepared. I've waited for an opportunity. But an opportunity is not going to be packaged. Like, all right, yes, and this is the opportunity. God has sent it today. And I thought to myself, this must be an opportunity. Is it Jack Ma? He says, yeah. Just find a problem. And work out a solution. You get the money. I was like, all right, they're stealing your money. You're working up there. Listen, I've been there for 19 years. I know how it feels. Let us do it. And that's how it started. For two years, I've built hundreds of houses for people. That's why they were saying, look for this guy. Now understand. Understand. Yeah. You know what? Before I continue, I want you to take me around some of the properties that you think I need to say, which is close by. Sure. And then, yeah, we take you from there. Woo, great story, man. Let's do it.