Good morning, welcome to Y in the morning and if it is Tuesday, it is all matters pertaining in entrepreneurship Tuesday. My name is Michelle Ashira. You can find me across all my social media handles that is at Michelle Ashira. You can also follow us across all our socials that is at Y254 channel. In this session, we dive into an interview, an interesting interview that looks at a small space farming. In the studio I'm joined with Masi Monena, she is the founder and CEO of Shamba Connect. So she'll be taking us through what it entails when it comes to, you know, what is the word? Vegetable gardening and also commercial rabid farming. So it's an incorporation of a little bit of everything. Good morning Masi. Good morning. How are you doing? I'm fine, thank you. How is your morning starting off? How you feel like? It's a beautiful morning. What are your energy levels looking like? I'm excited. I'm happy to be here. I'm excited about the things that are happening in the business and also in the country at the moment.  Would you mind to share? I think for me, I look at every day as something new that comes, something special, something for us. So for me, it's something new. So you're intentional like that in your life? Yes, yes. I am intentional because every day is a gift. Every single morning is a gift. Yeah. All right. That's beautiful. That's positivity all the way. We need that energy as you are. We need that especially as entrepreneurs. Yes. Yes. Yes. Starting us off, tell us who Masi, Amunene, who is she? Where did the love or the passion breathe from when it comes to a professional vegetable gardener? Thank you for the name. Okay guys, behind the scenes before we started this conversation, Masi was like, that's actually a new title. You can add someone in the series. Yes. Where did that love or passion breathe from? For me, it started at a young age. I used to have issues with my stomach, especially when it comes to vegetables and being able to digest vegetables and all these things. So I struggled a lot because my blood levels were low and at the same time I could not handle vegetables. So I tried to figure out what could have been the issue, what was making me not be able to digest vegetables or to handle them. And along the way I found that the reason was the pesticides and the chemicals that we use in making of these vegetables and growing them because you can tell the difference in taste between the ones that have been grown with chemicals and pesticides and all these things versus the ones that have been grown organically. So for me it started a movement. I want everyone to start growing their own vegetables because eventually the solution to the food crisis that we are having now and will continue to have is us being able to utilize the resources that we have to create vegetable and gardens. So it breathe from you having acidity issues. Yes. Okay. And you know some of us out here, even if you're going through that situation, will not be in a position to be like, what is the real problem? Let me now find out and actually just create a solution, come up with a solution to bridge that gap. What pushed you to do that? I had been in hospitals, in and out of hospitals. So this month I'm okay because I'm on medication. Next month I'm down again.  So it was like back and forth, back and forth. Yeah. One step forward, two steps back. So I decided to find out what could be the issue, what is happening behind all this, what could be the reason. And for me I found out that if I'm able to consume these vegetables, they're actually going to give me a solution to my problem. Okay. Yeah. All right. So what is the difference between when it comes to vegetables that are grown from the chemicals that you're mentioning, pesticides and just organically? How different are you, as Shamba Connect, how different are you doing it? For us, we ensure that when we're installing a garden for you, we take you through the process, we train you on how to ensure that your gardens remain organic. From how we, the kind of manure that we use to the kind of fertilizers, I mean to the kind of pesticides that you're going to use, which are not chemicals. Okay. All right. So when was Shamba Connect? When did it all began? Take us to that particular journey. Yeah. I used to be an employee somewhere. Just to be a side hustle. It's just one of those things that as I started, as I realized that if I could create a solution for myself, I could be able to do it for many other people. I started doing that for myself and now for friends and family. And I realized that this could actually be something that could sustain me. And along the way, I was happy to realize that it was not only sustaining me, but a few other people here and there. Because I could not do it all by myself. Okay. It's very manual. It's intense.  Because when it all started, it was a side hustle. Yes. So take me through the first project, the first project that you did and the mental space that shifted everything. And you saw this as something you can do full time. I always believe the first customer should be me. Okay. And I did that and it worked for me. And I could see my budget in terms of my expenditures in the garden and versus now what I used to spend in the market. And I could see that I'm actually saving a lot by using my own vegetables. Instead of buying your vegetables. I didn't have to buy any land anywhere. It was just within my space. I realized this could be a solution to so many other people looking to save money and looking for other avenues to raise money. So for me that was an exciting journey. When I did my first project, it was exciting. You know when you get that first customer, the excitement of I have to show up and do everything right and show that everything is well. And deliver. Yes. And of course along the way I have been able to learn because I also failed quite a few times. You could do something and then it fails and you go back and ask yourself why did this not work out? And I mean we have been getting better and better. Okay. And before we get into the failing stories because that's what makes us do better through those particular stories. I'd like to find out how did you find your business in terms of just accessibility on finances? How did you go about it? Because it's one thing.  But now it gets to the point where you have the picture of a business. Right?   Employment is not bad. Employment is good because it gives you the base on which you can be able to do your other things other than the stability of a salary which is really good. You find that there are many other things that you can be able to do with the same salary. What else can you do? And that is one of the things that young people need to be trained on when you're employed. Many of the people get into work with the excitement of oh, I'm looking forward to the last day of the month or the payday so that I can go out and drink and entertain yourself and do all these other things. But the truth is if we live for today and we have no reserves for tomorrow then we are shooting ourselves in the foot because the truth is we have seen the last two years. Employment is something good. However, when the economy is going down and you find that what you used to hang on so hardly, so tightly to, you find that they are also going through their own seasons. Oh, yes. And COVID-19 timeframe proved that to us. It proved that. I think employers also realize we don't need all these people to deliver the same results. We don't need to use all these offices. They also, everyone is looking for ways to reduce the expenses and increase their revenues. And so how can we do that? And that is where, for me, it has been a journey. All right. So here you are. You've started off. Take me through the first project. How was the feeling? Where were you at mentally? Excited? I want to deliver the response from the clients. I had both the raw materials and I waited. I advertised. I told people and friends, family, everyone. And we don't want to take a risk when someone is starting off. We just want to first sit on the side and see how it goes before we can buy into it. And, well, that is what happened for me also. I waited for some time. When I got my first project, I was anxious. I was anxious. I couldn't sleep that night because I was thinking, now this is something I'm doing for myself. And I have to really prove that I can be able to deliver. And so I ensured that I put everything together, ensured that everything went perfectly well. However, I mean, with all this planning, there are these things that still come up, the setbacks and all that, the things that you didn't even foresee. All right. So I'll leave it at that. Masi will be right back, guys back at home. Are we having a commercial break? So we'll be right back. And Masi will be taking us to her journey. Was she able to actually acquire more clientele? And how did she go about that? So make sure you stay tuned to so much coming your way on this particular conversation of looking at small space farming. So we're having a commercial break. We'll be right back. Welcome. Welcome back to Y in the morning on your number one news station. That is the Y254 channel. And if it's Tuesday, the hashtag to use is hashtag entrepreneurship Tuesday. So today we are looking at small space farming. And in studio, I'm joined with Masi Munene. She is the founder and CEO of Shamba Connect. So Masi, before we went for a commercial break, I would like us to, we were touching on your transition from employment to now you being self-employed in this sole proprietorship. Yes.  They have that fear, right? When you're in employment, you are sure that there's a check at the end of the month. It's true. And you're sure that you'll take care of your bills. Yes. But now COVID happened. There's a time COVID happened. That timeframe that actually proved us wrong that you can wake up the following morning and you have lost your job. Yes. So how did you take that step of faith? And where were you at your mental space just overcoming the fear of moving from certainty to an uncertain space in your life? Let me start by saying, first of all, that a side hustle and an eight to five, juggling the two is something good. However, don't blame why your side hustle is not giving you enough. If you're only giving it between 5 p.m. and 8 p.m. or 10 p.m. because that is the match you have invested into it. Because with your 8 to 5, you have invested your 8 a.m. to 5 p.m. and sometimes even beyond 5 p.m. So one of the things that our fear is based on is that your side hustle is not giving you enough. And if you wait until everything comes together for you to step out, it will never happen because your side hustle works between 5 p.m. when you're tired to whatever time some people work overnight. And that is the time when you're extremely exhausted. So don't expect it to give you as much returns as your 8 to 5. Yeah, that is one of the reasons why we... one of the reasons why we are afraid to step out. But also I think having the big picture in mind, having a vision of where you want to go, because the truth is we don't have forever to live. We have this one life. It's true. We live only once. But you also have to look at what else can we do with our lives. There's so much that has been invested in us when, even when you look at the Bible, we have been invested with talents, things to do with ourselves. And yet we just want to have the comfort of employment of just going to do the bare minimum and also expecting the bare minimum and just moving from month to month to month. There will come a time when that energy that you could be able to invest right now will not be there. You might have the money, but not the energy. So for me it has been a journey. Self-discovery. Self-discovery. Which is absolutely good. Masi, you'll also agree with me. And also people who have already done it, people who have already taken that step of faith and they've gone into something they love, they've studied something, but they're on the starting stage now. Did you get to a place where you were looking back?  Because gardening takes patience. Any business takes patience when you're starting out. Did you get to a place where now you're looking back? Did I make the right decision? Now you're second guessing yourself. And how did you deal with that? I did. I would be lying to say that I did not. Many times I did. In fact, I would wake up in the morning and wonder, okay, what am I going to do today? Then I think about my colleague that I left who's probably earning that amount of money that I am no longer earning to be where I am. So the truth is, I looked back, I asked myself, did I really make the right decision? But now for me, the bigger picture is what I had in mind, the flexibility of time. And now I'm a family person. I have young children. I want to bring them up. I want to be there for them. And this is the age where they need me the most. And that is what I think I was struggling with in the area of employment. Because employment would take up the whole day. By the time I would get home, I would be all tired. They would also be tired. So I find that I'm really asking myself hard questions. Am I really the one raising these children? Yeah. Okay. I take me through, how did you build your client base or even because you're still building the network, how do you go about it? One thing I have learned is that personal development is the greatest development you'll ever do for your business. Because if you do not invest in yourself, you will find you have nothing to offer. You find that you're constantly struggling with your competitors because they always have something ahead of you. But you're not taking the time to invest in yourself and grow yourself because if you do not grow yourself, don't expect your business to grow. They say that your business will grow as far as you can grow. Therefore that means if you're not growing yourself, your business will never go ahead of you. So for me, I have prioritized personal development. If there is anywhere I can learn something about business, I am available. I am ready. You are there. I am willing. I am there. So personal development, you attend, I would like to assume business seminars, just to build on your development. What are the other ways that you build the capacity? The other ways are hanging out with business people like myself. When I started off, I really struggled with every single thing.  It's a full-time job by the way. When you go along the street and you see something nice and you start thinking because you don't have the assurance of salary at the end of the month, now you have to really think hard. Do I really need this? That is what is called bootstrapping. You actually go to the basics. For people who start from where I started from, you should be comfortable living with the basics. You know some of us, especially when you're employed, you're used to a certain kind of lifestyle and when you get into business, my friend, just come down to the basics. You come down to the basics. You hang out with people who've been where you are. And I think having people guiding me, having a mentor along the way has really been helpful for me. Who's your mentor? I have a few. It would be great to first get their permission before I start. You don't have to share your blessings. I will, in fact. You don't have to share your blessings. Next time I'll be happy to bring him along. I have quite a few and I'll be very glad to bring them along because they have really worked with me. They get into the muddy places and really helped me to get out of where I am because they have also worked their path and they are willing to help me around through it. So next time you know it's a date. Finally, she has agreed. So how do you market your business? First, I use referrals. I always ensure when I do work for someone I ensure that it goes perfectly well. The funny thing about agribusiness is that it's not a one-time thing overnight. So you have to be patient. You wait for the work to be done. After that you have to wait for each to grow and see the joy that people are having in their homes. Finally, involving their children in the things that they do. Using your time to actually have your free time, distress, relieve yourself, go through your garden and all that. Now at that point someone is happy to refer you. And I can tell you referrals have been my greatest source of new clients. Other than that, I have grown through social media. It's something I just grew into that I'm still growing into. There are many aspects I've not yet reached. And it's an area of development for me. Fantastic. You've mentioned something which is very critical, which is referrals. Reparals are the best when it comes to gaining new clients. Just as you have mentioned. I'd like to understand what is your strategy when it comes to building a good customer relation experience? How I look at it is when I go to see a client, I always ensure that I know I get to understand where this customer is at. Sometimes as business people, we go with our minds. We go and say, this is what you need to have, this is what you need to have, this is what you need to do. But you don't understand where this person is coming from. And what they need. Yes, what they actually need. Because I've worked with people who are fine. They just want something to hang around with. I can tell you like in my rabbit business, there are clients who just come for pets. So if I go and tell them, I'm going to give you this big breed that is going to grow in the next four months to be something humongous. You can be the main distributor.  And even if you sell to them, what you think is what they need, you'll be very surprised because what they're probably looking for is something that will stay tiny and small and sweet and lovely and cuddly and fluffy and all that. I love rabbits. They have different colors. It's red, it's blue. That's always something that I've never had to grow. It's always interesting. But I don't like the smell of... That's still a struggle. Oh, the smell of the urine. Oh, when you start to see the benefits of it. Oh well, you will forget that. You will forget that. These are the things that have become... Irrelevant. Irrelevant, for sure. So what is the most effective vegetable to grow on a small space? Let's say like a backyard that I have in my... And I would like to maximize on that particular space. So what would be the most effective vegetable to grow? First of all, I get to understand what the client needs. For example, you find that there are some clients who like... I've worked with people who are like especially medics and they tell you that there's no juice like celery juice. So all I want in my garden is celery. Fill the place with celery from here to the end of the world. Therefore, once I understand what the client needs, I'm able now to tell you which works for what you have. At Shamba Connect, we design gardens based on the size of your space, your personal style, and then the budget that you have. So based on that, we are able to design a garden that is suitable for you and also that advices the crops that are best grown there. I've found that spinach is one of the vegetables that we cannot do without.  I was like, spinach will actually do well. And it's one of the vegetables that are very, quite very loved. And they can go for like, you can... They're nutritious, you can go with every meal. So my mind was at spinach. So we can let's look at some of the home garden designs that you have worked on and probably can take us to those ones as well. So if my director is ready, we can get to have those particular images. Yeah, so the vegetables, God is needful sunlight. Here we go. Probably could take us through that. So we have a... I don't know which vegetable is that? Oh, that's spinach. Spinach. Yeah, spinach. How long do spinach take to actually develop to this particular... the way they look like right now? Clients keep asking that. The moment you plant, they start asking, so when? So now when? And this takes three to four weeks. I normally advise that you start harvesting at the fourth week because by then the plant will have grown enough leaves to cook. Let me just use the layman language to cook enough food for the rest of the plant so that when you harvest two or three leaves, you still have enough for the rest of the season.  You have to leave some of them to continue cooking food for the rest of the plant. Okay, you're using the good layman's language now. Yes, I understand. Because to this design and the vegetable... Now that depends on someone's space that is made of wood, very attractive. Actually that has been one of the most attractive planters that we have that is popular. So there you can plant, actually you can plant either vegetables or flowers. Some people love herbs like basil, sage, oregano and all those. Can tomatoes do well as well? They do, they do very well. But they require lots of water, right? No, actually we take it through the... They don't require... It depends on the variety because they are the greenhouse ones and the open field ones. All right, probably we can have another picture, another image. All right, so tomatoes can actually do well at anyone's small backyard. At anyone's small backyard, yes. So I can have skooma. Oh, and also the traditional vegetables. Yes, managu and teriyaki are very much loved and saga. All right, take us to this. There is a young man there. That is another type that we also get. And that one has... We use something else, not the soil on the garden itself. And then we use something to cover it and it works very well on the rooftops. So there are vegetables that don't require too much sunlight? Yes, yes. Because especially like skooma, you'll find many people complaining that the more they eat, the more acid. Oh, yeah. The more bitter they become. Yeah, that's true. Sometimes it's because of the conditions that you put them in. Because again, if we expose them to very harsh conditions, be sure. And also the breed of skooma that you plant. It matters, yeah? It matters. The conditions. Yes. They're just like human beings. Exactly, that's what you... When you come from a harsh environment... What do you expect? Anko person. Yes. All right. Those ones work very well for pastures and balconies and even cemented places. Are they flowers? No, these ones are vegetables. They are Chinese cabbage, Chinese spinach. You can see there's some herbs there. Yes, that works very well. If you don't have a garden itself, so we come with the whole kids. We call ourselves a plug-and-play company. We just need to be there. Yes. So do you also do maintenance for your clients? Yes, we do. We do maintenance for our clients. All right. Yeah. Is this still... This is skooma. Yes. And I can tell you this skooma is normally very soft. It's not like the one that you find in the market. This is very soft and it's organic. It's something very beautiful about having organic, soft vegetables that you can present to anyone. They're old people, they're children and they won't have issues with them. So it takes the same timeframe of four weeks to harvest? Yeah, four weeks. All right. This is us at work. Maintaining a garden. Which vegetable is this? That must be spinach. Spinach. You have to go back and tell me what's going on. That is spinach. Let me tell you something about spinach. I can't say it's from afar like that. Tell me something about spinach. Spinach is very versatile. We use it on salads. We use it on juices. We use it on almost every meal. For children who are being wind, that is the best vegetables because of its softness and its level of iron. And what can you do not put in a vegetable garden? What can you put in a vegetable garden? Maybe potatoes. That's impossible. Okay, what if this looks like a house? It's the same. Maximizing one piece. Yes, this is a self-drift kit whereby there are people who farming is not their cup of tea. Therefore we give them this that is automated. It's not automated completely but you don't have to bend to look for water and all that. You just water the orange part and then it drips. It drips. It's like drip irrigation on a kitchen garden. Oh, I've seen that one. In fact, in an office. But it has no flowers. So it looks really, really beautiful. That's interesting. We have seen what we cannot do in a vegetable garden is potatoes. Why are you attacking us? We cannot plant and not back yet. Actually, we have a design for a rule also. There are some bugs that we use, specialized bugs that you can be able to, once they are mature, can be able to actually just go and open the bag and pick up the potatoes. But this one would not work on potatoes. Take us to what you do as a company when it comes to knowing you've met a client and you've found out what they need. So how can you get to pick the best site for a new garden? The first thing that we do after the initial call and understanding what the client needs, we do a site visit. Once we do a site visit, we are able to ascertain which is the best site to install a kitchen garden. And this would be based on the direction of the wind, the amount of sunlight the space would be able to contain, maybe access to water. Yeah, and some people also have pets that they want to ensure they don't get to the gardens because also pets are very... They're also lovers of plants, yes. Let's shift gears a little bit and look at rabbit farming and also on a commercial space. So how do you go about that? First of all, rabbit farming can also be brought down to the level of a kitchen small space farming whereby you can actually... We put together a kitchen garden and a rabbit farm on the side. So they're able to work together. And the reason I say that is that you find that even the meat that we are consuming in the city sometimes is polluted. Sometimes we don't know what kind of medications have been put on the animals. And so we are living at a different time where we also have to be careful on what we put on the table for our families and for ourselves. And so I found that with rabbit keeping and if you look at Kenya and other countries, rabbit keeping is one of the ways that you're able to get your meat, white meat in the comfort of your place. They don't make noise. The smelly bit can be managed I don't know if you mentioned that. That's what I was saying. And actually we use everything from the rabbit to maintain the garden and also get food on the table. So when you look at how much disposable income that households are having right now, you find that it has gone down because the cost of food has gone up. The cost of fuel has gone up which has affected everything else. So what I always do when I train people I also train groups of people on how to ensure that they are able to have a higher disposable income. There are two ways. You either increase your level of income or reduce your expenses and you find that in this season many people are living at the bare minimum. You really do something when you really have to do it. So when you're talking about how can we increase our levels of income and rabbit farming comes to mind. Why rabbit farming? You find that with rabbit farming they grow very fast. If you ask anyone, they'll tell you they multiply very fast. In fact, one of the things that we have to do is control the breeding to ensure that they don't over-multiply and fill the whole space and you don't have... In a timeframe of three months. You know, for rabbit farming the gestation period is 28 days. 28 days, yeah. So you find that if this rabbit in fact if it delivers today it gets access to a male at that time it's going to give birth again. They're very next month at the same time with young ones already here. So that's one of the things you have to control. So what I'm saying is that... But that is a good thing for business? Yes and no. Because when we are able to control then we are able to assure you of the quality of produce. Because what happens to anyone is if they are overused then you find that the quality goes down in terms of how much weight it has the immunity levels and all that and so you find that the mother will get tired if it's served every month and giving birth every month. But at a home level you find that if someone is able to keep two or three rabbits they are able to maintain their household expenses and it's white meat very summer friendly actually very digestible. They say that even in the hospitals when invalids are recovering that is one of the best white meats to give them when children are rich six months and they need to be wind white meats, talk about rabbit meat because it's very sort of easily digestible and absorbable into the body. Yes. I'm still at the maintenance when it comes to the urine. We use that for the kitchen gardens because it's one of the ways of ensuring that you use you do not use pests to control pest sites to control pests. So we use it to add nutrients into the soil for the plants and also to control pests into the garden. Take me through when it comes to rabbit farming you do it on a commercial level. Yes. So how do you gain your clientele? For clientele, I think same strategy applies in both areas. You find that they are interconnected somehow when you give someone access to fresh organic vegetables they'll also find is there another way then is there a way I can also be able to access fresh meat, organic and all that. So what is the strategy when it comes to building a strong ownership team? Because when you started off you mentioned that you cannot do this alone. It requires time and teamwork as well because we have seen the kind of designs that you people give your clients. It's something that requires teamwork. So I would like to know how do you come up with or rather build a strong ownership team to just have a sustainable business? The journey to get to that team the one first of all when you are alone you are the CEO, the finance, the accountant, the tigger, the secretary, the everything but now over time you realize that you need to grow and you can limit your growth if you want to just do everything but now when you want to grow you need to get the right people. It's important to get right people because I've also gone through that journey of getting the wrong people and so one of the things that I do is I ensure that someone has a passion for what they are doing. You find that many people come from the farm from the rural areas and they come and they say I do not want anything to do with soil or plants or I want a white color job. Hey, okay. It's not that color realizing that food is so important and what our grandmothers used to do. It's not so bad. So how do you identify that someone is really passionate about what they want to do or what they have been doing in terms of them joining Shamba Connect because they have to buy into the brand, right? So how do you pick that this person is actually passionate? No, we say we can work under pressure. We are passionate. I can do this. So how do you get to actually now see beyond all that and actually pick that person has already bought in on the brand? It's also a journey because on day one you may not be able to understand everything that goes in. I'm sure even when you go to an interview in a job, you just know the basics but once you get into it, you get to see oh, this is how we do this. This is how we relate to clients. This is how we handle clients. This is how we deliver. This is all that. So you find that at the point of interview I'm able to find out. As a hitter, let me tell you, in business, you become all this into one. You wear every hat. You wear all hats available. And so you are able to pick out this person is fine and they are talented and they have the passion for giving people solutions in this area. And I'm happy that I have a really good team. They also help me. They really help me because if they don't, if I do not, if they don't, I cannot shine without them helping me. All right. What does the bigger picture look like for Shamba Connect? For Shamba Connect, I'm looking at every household being able to own their own food, own organic food. And while people are complaining out here there's no food. You know, you cannot be assured of the kind of food you're getting where it's coming from. I'm looking at a place where we are able to sustain ourselves as a country. And so we are in a place where we do not need to get support all the time on everything. Some of these things you can be able to get solutions for ourselves. I'm looking at young people leaving employment and getting into solutions for other people. Because one of the things you find that really helped the economy is when you are able to open a business and employ other people. Give people a place to live. Give people a solution. And when the country is healthy every family is able to get their own food. People are employed. I think it will be a good place to be in. I'm really excited about where we are headed. All right.  Looking back, do you have any regrets? My only regret is I wish I had overcome the fear earlier. Why do we ever say that? No, it's true because Why do we ever say that? I wish I started earlier.  So if you don't have food, so if the house is locked. The bills, everything. And sometimes you don't look at the possibilities available to you. The amount of talent that you have within your hands the amount of resources that you have that you can actually get solutions for yourself and for many other people we don't look at the possibilities. I think our minds are normally conditioned to the things that cannot happen. The bad things that would happen the things that you're going to lose out on and we don't look at what you're going to gain. So we operate on fear we operate on fear and it's not a good thing. We need to open our eyes to the possibilities that life has for us out here. Oh yes. A couple of challenges that you have faced through this journey and also something that you're also a couple of things that you're also working on. I think when it comes to agribusiness you find that it's also capital intensive. You find that you're constantly looking for funds to expand and we are always looking for is there another is there a way I can be able to quickly expand and ensure that I reach there and of course with the new technologies coming up it's a good thing. It also means that it's a good challenge that I always have to constantly be on the lookout for what is new what is in the market what can I do better at a lower cost, at better yields and all that. Also people people can be something I think being able to be at that comfortable place where you have the right people doing the right jobs is a good place to be in but I think it's everyone's every entrepreneur's challenge to be able to retain their best talent. Yeah. Alright, for someone who's watching this conversation and they would love to be part of what Shamba Connect does because you also mentioned that they are on that you also do trainings how can they get to you? You can find us on Facebook you can find us on Facebook as Shamba Connect on Instagram as Shamba Connect Yes. Are there any upcoming training that we are looking out for? Yes, we train we train people on rabbit farming because that is one of the things that every time we get new clients they want to know how do we go about it from step one to the eventual step of rabbit keeping a business and we have been able to to help people come from making it as a home project to being a business and so we have people who are always looking out for that and also looking to work with organizations that want to get into the even into the communities we have been training people in the communities on how to ensure that they always have food and one of the exciting things for us has been training women on how they can be able to use the skills and the resources that they have to transform their households from just being a stay at home mother to doing it as business so you find that people are creating employment for themselves and for other people Fantastic. So as we wind up I would like you to give advice to someone who is in a state of the ready transition of employment which is an intentional decision as they get into something they love but now they are stagnant in whatever they they are working on in terms of whatever they love it could be a business it could be another space they are into in terms of different industries that they were actually in prior so what would be your advice to someone who is in that position they took the step of faith Congratulations for taking the step of faith. Yes another into something of their own but now they feel stagnant and they are looking back One thing I would like to tell you is congratulations first for taking that bold move and secondly personal development is very important and it will help you not only to grow into the space that you are looking at but also it will give you opportunities and possibilities of what else you can be able to do then mentorship is very important in what you are doing look out for people who are already advanced in what you are already doing and you will find that people are really helpful people are willing to help people are willing to hold your hand to work with you this journey of entrepreneurship and they will give you ideas on how you can be able to accelerate, how you can be able to pivot, how you can be able to make it make sense to you. Wow perfect love that one yes just keep the faith going and ask for help and build self-development right yes yes thank you very much Mashi for creating time to be with us and taking us through a small space farming and what it entails we appreciate you and you are always welcome as Shamba Connect Thank you so much Michelle for having me Karibusana alright so that is Mashi Munenet the founder and CEO of Shamba Connect we are looking at small space farming and what it entails and how you can go about it so right make sure you keep the conversation going at Y244 channel is where you can find us across all our social media handles at Michelle Ashira is where you can find me across all my social handle so keep the conversation going we will be right back in a few.


Okay, I know we're running behind time, so I will try and go as quickly as possible. I'm just curious, how many people are using machine learning already in the room? Okay, a few. How many of you are using deep learning already? How many are interested in getting into deep learning? All right, a lot more hands, cool. Okay, so some information about me. I'm a Google developer expert for machine learning and deep learning. I'm co-organiser of the TensorFlow Meetup in Singapore, based in Singapore most of the time. I spend pretty much all my time working on stuff to do with deep learning, a lot of it to do with language and dialogue, and some things to do with voice. Is there audio coming out of this, no? There is, okay, cool, that's right. I've basically done the whole startup thing. I'm currently in a startup. We started this last year called Red Dragon AI, and we do a number of different things. One of the things we're doing at the moment is a lot of deep learning, consulting and prototyping for people. We also do education and training in Singapore and around the region. So if you're interested in learning, taking courses and stuff like that, we currently do some stuff with SG Innovate, so I'll tell you a bit about that later on. No? Okay, but also the most reason we're doing those things is because we're working on products at the moment and research costs a lot of money in this field. And the things we're working on is stuff to do with conversational computing, natural voice generation, and knowledge-based interaction and reasoning. And so actually, my co-founder and I had a paper at NIPS conference, which is a big machine learning conference last year about this, and we're currently working a lot on natural voices. So this is where we'll see if it works. I am very pleased to be at Fust Asia with you today. I hope you will enjoy the talk from one of my creators this afternoon and that you will start your journey in deep learning. So this is a voice that we're currently prototyping that's built all entirely with neural networks. We're basically a neural network. It uses deep learning for the whole thing. And it's built on training data and then you can basically, you can just read English and then speak it straight away. Very different than the sort of, probably what you've heard of, like the phoneme systems and stuff like that. Okay, so let me get started. I meet a lot of people who ask me questions. The questions I get a lot are things like, you know, what is the best way to learn deep learning? What's the fastest? What's the easiest? What's the most efficient? Do I need to know math? Can I just write it in the current language, the programming language that I already know, that kind of stuff? And I get these questions really regularly. So my response ends up being questions back. And the questions I ask back, I was, okay, what do you want to use deep learning for? And what do you want to do with it? How deep do you want to go? How much do you want to learn? What do you already know? And then also sort of the way you plan to use these skills. So I'm in the lucky position now that I've seen a lot of people learn deep learning through teaching them and also just through, you know, people, friends learning and stuff like that. I've kind of seen what works, what doesn't. I, so I want to go through some of those things today and then maybe even get you started by teaching you some of the sort of key concepts to understand this stuff. I'll say that it's not hard, right? Everyone sort of looks at people in deep learning and stuff like that as, oh, wow, you know, you've got this amazing ability. But for the majority of things, it's gonna be quite simple for you to get started and probably to build projects. So goals, you know, I think if you're gonna learn something like deep learning, you need to have some core goals for yourself. You need to have something that you want to build. So everyone in here is a coder, is that correct? Yeah? Anyone not a coder? Okay, a few people, mostly coders in the room. Okay, so the deep learning field itself is so big that I could just talk for a whole day and not even finish all the different types of models that are out there. So you've got to be very specific when you're gonna go into this about what it is that you want to achieve, what is it that you want to learn. The other thing I want to sort of get across today is that don't be stopped by sort of hitting roadblocks of where you don't understand certain things. Deep learning education is still extremely new. That said, in the last year, there's a hell of a lot of new things that have come out and it's getting easier every day. There's lots more tutorials, that kind of thing. So my goal for the talk is to basically give you a 10,000 foot view and then go into some of the key concepts and show you some key resources for this kind of thing. When I talk about sort of learning, deep learning, I think of deep learning as sort of three levels of proficiency. The first being sort of just, and this is even a sort of step above what the IOT speaker Maku was talking about. He's talking about using pre-made models. Any developer should be able to do that quite easily. It's just peeing in API. But you can very quickly also build your own sort of very simple models without customizing them that much to make your own APIs. And I think that's one of the first levels that you want to be able to get to. The second is being able to customize things. The first level is extremely easy. The second is basically customizing things now where you understand a little bit of how deep learning works and you're trying to make it fit your particular situation. This is, yes, more challenging, but it is definitely still very easy to do. The third level is basically sort of where you're creating techniques and methods for new kinds of data and problems. This is very, very difficult to do. This is what I spend hours sort of working out, trying to get voices sounding more natural or get certain algorithms to work better. So first off, what is deep learning? Well, the media thinks that deep learning is this. They seem to think that it's sort of like Terminator meets some sort of oppression system that's going to take over the world. I find that most people, when you talk to them, think that deep learning is some sort of icy board with lots of dots all over it. And you see these in logos a lot for AI companies. But really deep learning is just a subfield of machine learning. It's actually neural networks is a subfield of machine learning and then deep learning is a subfield of neural networks. Neural networks have been around for a long time. This is not new stuff. People were working on this stuff back in the 1980s and even before then for certain models. But certainly it's really only become viable in the last eight years or so because of, one, the amount of data that we've got that these things require to learn. And also, two, more probably more importantly, the amount of compute that they need to learn. So as Chris was showing you in the last talk, to do some of these big models, you need an extreme amount of compute. Now you shouldn't let that discourage you when you're starting out because nowadays, actually, just with your laptop, you can build models that were impossible to build 10, 15, 20 years ago. So it's all relative is my point in that. The other thing that's probably making deep learning so famous is there's pretty much every breakthrough that you're seeing or certainly the majority of breakthroughs you're seeing in AI and AI products, really it's deep learning at the back end of that. Whether you're talking about something like AlphaGo, whether you're talking about self-driving, whether you're talking, all these things are actually using mostly deep learning to do the stuff. So when we talk about deep, why do we use the word deep? So here's a model called Inception. This is Inception version three. So this is quite a famous model. It won the ImageNet competition a few years back. Certainly it's sort of like one of the stock standard models that you will find around. Google to this day uses this for a lot of things. In fact, I was suspected a lot of those pre-made APIs, not auto ML, but the pre-made ones are using Inception v3. Now, when we talk about deep, we're talking about the number of layers this way, in this case. So on the left here we've got the inputs. On the right we've got the outputs.  And to do this, you really have to run it on something like GPUs. To give you an example, I've gotten, I think this is like 2014, 2015, this model came out. When it first came out, it took Google probably around about, it would cost them about $30,000 to train the model for just the amount of compute and power and stuff like that to do it. Now, we're seeing such an acceleration in GPUs and stuff like that. If you've got a decent GPU card, you could probably train this at home over a period of a few days to a week or so. So what can deep learning do? I sort of at you that you should pick a project and go for the project. So let's look at some of the things you can do. So you can do classification. And if you look at this picture, some of these are chihuahuas, some of these are muffins, right? Now, it turns out that actually this is a very simple classification task, right? For any sort of computer vision algorithm can spot this quite simply. But it does sort of show to you that, often we find that using these things for classification of images is a big factor. In fact, this is probably the most common thing that you hear people talk about. If you think, for those of you who in here for the IoT talk before, a lot of what he was doing was just classification. They're all sort of stock standard classification things. But you can go so much further. And this is where, and I would say, so if you've heard some of the talks over the past few days about AutoML, Google's new product, AutoML, in many ways that is replacing sort of a lot of the need for you writing your own classification models. Because it could just do it so much better and so much quicker. But there are so many other models that are really important. So segmentation models, these are used a lot for things like self-driving. And really what we're doing in these kind of models is we're doing classification, but at a pixel level. We're classifying each pixel as is this a car? Is this a person? Is this a traffic light? And more and more, these are starting to be used for a whole bunch of different sorts of products in industry and stuff like that. We've got image captioning models. So where basically you can train up an algorithm to just look at a picture and then write text about that picture and write up a description of that picture or write up something like that. And you'll see that a lot of these things are, this is an example of where the model has basically worked out that even though we can't see that the kite is connected to the human, the model is working out that the probability is high that this human is flying this kite so it can therefore basically give it a caption. This kind of technology is also being used in industry to basically talk about spots on LCD screens and describing problems in products and stuff like that. We also have things like generative models and this is where my passion lies a lot. So these two things that you're seeing here are things that are models that I built early last year. The top one is what's called a super resolution model. It basically is an 8x super resolution model. The picture on the left is the input in the middle there is the predicted output and the picture on the right is the real world output. So you can see that it learns pretty quickly to get it pretty damn accurate. The one at the bottom is a colorization model. Again, the picture on the left is the input. The picture in the middle is the prediction. The picture on the right is the output. In this case, it's kind of interesting because you can see it doesn't mess up with all the bad lighting effects. You can see in the one on the right the lighting is very hot and that's why you've got that sort of yellow tinge but it's able to work out that, okay, this is what it thinks it should look like. We've also got sequence models for doing things like text and NLP and doing time series analysis. In fact, we've got so many different types of models that I could just keep talking about all the different models and keep going on. We've got neural machine translation, object detection, speech recognition, Q&A systems, a whole bunch of these different things. In fact, inside Google, this is an old thing but pretty much sort of the middle of last year, they were up to over 6,000 directories where Google stores its repos for its code, contain TensorFlow code or code that was being used for machine learning and deep learning. It's pretty much being used in the majority of Google products, now if not almost all of the products. So we talk about models and this is something that you heard Chris talk about, you heard the previous talk before Chris talk about. So everyone should be wondering what the hell is this model thing? So I wanna go through just some of the basics of sort of what the building blocks of models and get you to sort of think about knowing what they are so that then you start to know what sort of questions to ask when you're actually starting to learn this stuff yourself. So here's a very simple model and what you can see we've got some inputs which are going into a number of hidden layers and then we've got an output and then we're basically taking what's called a loss function we're comparing the prediction to the loss deciding okay how accurate is our model and then we're using an optimizer to basically update the weights in those hidden layers to then get better at learning and this is where we talk about the learning concepts going on. Basically what's going on is that we've got all these variables that are weights and biases and we're adjusting them so that as we pass inputs through the model gets to learn something about them and is able to make predictions. So when we're talking about the building blocks the really the key things to think about are your inputs and outputs, your architectures which I'll come back and I'll talk about what you think of are those as like the design patterns or the different types of connectivity inside models, different types of losses and loss functions and then optimizers. These are the four things that you will find in pretty much every deep learning model. So once you know that these four things exist you then just starting to think about okay let me learn the different types of those things. So I'll sort of just go through that with another graphic quickly. If you see here we're passing in the data and we've got labels that we've at the start already because we're training. We pass the data through the whole network it goes through the hidden layers. You see that on the left there we've got weights and biases that are basically being used for the mathematical equations. We then take the loss of the network and work out okay how accurate is this network and then we basically use an optimizer to update it. Now that loss we basically compare what's coming out of the network with the labels to see that okay are we getting things right or wrong. So let's look at some of the types of inputs that you can have. People tend to think of things like pictures or something like that but you can actually have whole varieties of different inputs. So hopefully what I'm trying to get through to you today is if you're coding an app or if you're doing some sort of web app even or something like that you can start thinking about okay what sort of inputs could you then use to make some sort of prediction that you want as an output. So we can have lots of vectorized data, continuous data, categorical data. Obviously images and if you think about it images really are just sort of three channels maybe four if we have an alpha channel in what we call RGB format or BGR format. We can also process audio by converting it to an image by making it into a spectra and then using that spectra as a picture and running a model on that picture. We can do the same with 3D medical scans of being able to take mammography scans or something like that and use the exact same sort of thing. We can do sequences. So sequences we'll see a lot for things like text models where we're basically looking at sequences of either words or characters, speech. Even sort of images again can be sequences if we're talking about video or something like that. Then we come to the actual model architectures. And this is where you hear a lot of the buzzwords sort of talked about because what people are talking about is what kind of model is it? And really currently they fit into sort of three kinds of models. You have your fully connected models. You have what's called convolution models which basically pass a filter across an image or something like that and then basically sort of do a dimensionality reduction and play with it and do a number of different things like that. Then the third type we have what's called recurrent networks.  So the first type, the fully connected one, a great example of this is the TensorFlow Playground. So I encourage you just go along to the TensorFlow Playground, open it up and start playing with it. You can't break it, it's very simple. You can add layers to make it to see, so the hidden layers is where we're defining how deep the network is. And you can basically play around with this for hours trying to work out like, okay, what's the right system to be able to predict the different patterns? And they give you a number of different datasets to play with. Losses I kind of explained already, I think you're basically just comparing your predicted outputs with your real outputs. Optimizers are basically, I don't want to go into too much detail, but we're trying to find what's called a local minima for the weights. So the optimal point for those weights, so we therefore use optimization functions and a variety of them, which basically try to look around and search that space to find the right point or the right value for each of those weights in the actual network. And then eventually you get something like this, which is a convolutional neural network that can predict the cat or a dog. And you can see that you should be able to look at this model now and understand this, right? That okay, the input layer is the image, right? Each one of the pixels that is going in there, the output layer is predicting either cat or dog. So that's why we've only got two neurons that are right at the end. And the other ones are the hidden layers as we go through them. So one of the big things that I get a lot of people ask me about is all the math that you need to learn for this stuff. So yes, if you are gonna sort of go deeper into it and start to get more involved, you're gonna have to learn some math, right? That said, you can already get away with so many things without knowing the math. So don't be intimidated. It's more like, I would say for most people, it's better to just go along without worrying too much about the math, learn how to build stuff, and then at some point you're gonna start wondering, gee, I wonder how this particular part works, and then you'll wanna learn a little bit of the math to understand what's actually going on in the code there, or that kind of thing. So I put down some of the things there that you can do. I've also put a link back to a video that will explain back propagation quite easily. I don't have time to go there today. Programming languages, a lot of people ask me about programming languages. Okay, yes, you can build neural networks in any programming language, but really there's only one language that counts nowadays, right, which is Python. So if you're gonna be building something for the cloud or something like that, you definitely wanna do it in Python. Even if you're building a model that you're gonna then deploy to something like TensorFlow Lite or to Core ML for Apple's mobile neural network models and stuff like that, you're gonna build those models in Python, and then you're gonna export out the model and it will get converted into the local format. So you definitely wanna learn Python. The cool thing is Python is a great language and it's very simple to learn. Do we have Python programmers in the room? Yes, okay, agreeing, it's very simple. The syntax is very easy to read. So much so that when we do our developer course for the deep learning, for the people who don't know Python but are already coders, we literally just give them this one video. It's a one-hour video that teaches you Python in one hour. And if you can code another language reasonably well, you can go through that entire video and pretty much walk out understanding all you need to know about Python for this kind of thing at least. The other thing is, okay, frameworks. A lot of people ask me about frameworks and there are a lot of different frameworks. And this has been one of the challenging things for sure. You've got, certainly TensorFlow is one of the biggest frameworks today. You've got PyTorch is coming on very strong. You've got MXNet from AWS. You've got CNTK from Microsoft. The cool thing is this one in the middle is Keras. And Keras basically is a higher level abstraction for deep learning, which can run on pretty much all of these frameworks. So I would basically say to anyone who wants to start out, start out with Keras. It's very simple for you to get started. Like I said, it is a separate version that you can pick the back end or you can actually use Keras that is built into TensorFlow. The cool thing with Keras is that you have access to a lot of pre-built and pre-trained models. So you can just download those very simply. I, like Chris was sort of showing you in some of his code, I think you were using VGG16 or something like that, or one of the models, something you were doing a pre-built model. It's very simple to basically load those models and you're then basically getting all the work that other people did in building those models. And you can use it straight out of the box. You basically just change it for whatever inputs you want to use, whatever outputs, and then go for it. The other cool reason I think Keras is really worth checking out is that pretty much now, because Keras has been around for a few years, there's almost examples of any type of model that you want in Keras on GitHub. So if you're not sure how to build something, you can basically just go on to GitHub, do a search for Keras plus image object detection or something like that, and you'll find something that someone has probably built already, often in the actual Keras repo itself under examples that you can then sort of use. So just quickly, I'm gonna show you a very quick example of Keras. Okay, so for those of you who don't know, what I'm using here is Jupyter Notebooks. Jupyter Notebooks is a great way to run Python code. It's very simple. It allows you to see the output of each cell as you run it. And what I'm doing here, I've just got a bunch of imports to basically import some libraries and stuff like that. I'm using Keras, and I'm using TensorFlow as the back end in this case. I actually, let me go to the one I want to show you guys. And here I'm basically importing a dataset very simply. And what we're gonna predict is if you can't see it, this is basically an image of a handwritten digit. It's quite famous dataset called MNIST. Here's some examples of actually plotting them out. So you can see these are basically just handwritten images. There's 70,000 of them in this dataset. And we're gonna try and classify them between zero to nine. So I put a little bit of sort of theory in here to explain some of the things. What is a tensor? Tensors are just the different shapes. For those of you who have never come across this before, think of them as sort of multi-dimensional arrays is one of the ways that you can sort of think of them.  And it's only one channel because it's just black and white. So we've got a grayscale image there. But then we have like batches of them. We actually have 70,000 of them. So we've got 70,000 deep. So then we wanna basically build, so here I'm importing them. You can basically say I'm setting them up to basically, this is what's called one-hot encoding, where I'm telling the model that, okay, this is how I want you to predict each of these digits. So show me by showing me 10 numbers and then put a one on the one that relates to this number. Okay, the key thing is that I wanted to show you was this. So this is the entire code for building this model. It's literally with one line, we define that we're building a sequential model. We then add in one hidden layer, just like we would in TensorFlow Program where we press plus and add in a hidden layer. We tell it basically how many, so I've sort of set these up before, how many neurons we wanna have in each of these hidden layers. We go through, we add it, so we've got two hidden layers, then we've got an output layer. Cool thing with Keras, very simply, we can check out what our model looks like just by doing dot summary. And we can see that this is the shape of our model. This is the number of parameters that we're using. So what Keras has done is worked out based on the amount of neurons that we're using, how many weights and how many biases do we need for each of these layers? And you haven't have to write the code for any of this. This is already done for you. Then we basically compile the model by telling it what kind of loss function. Remember I talked about loss functions are one of the four things that's really important. And the other thing I talked about is the optimizers, what kind of optimizer is really important. So we're basically telling it that, okay, we wanna use a loss function called categorical cross entropy. And we're gonna use an optimizer called stochastic gradient descent in this case. And we're gonna basically get it to work on accuracy. So we're basically optimizing for it to get the highest score possible of predicting these numbers. We can then basically just train it by doing this. So we basically just fit the model. So we're basically passing in both at the start. We're passing in those images. We're passing in the labels for what those images are. And we've got a few things there like epochs, which is the number of times through. We wanna do this. We're gonna do two times through. And then we're gonna validate. So I didn't have time to explain the whole concept in machine learning that you never validate on the material that you actually train on. You keep a separate data set for validation. So we've got a separate data set here that we're gonna validate on. And you see, we'll run the model and very quickly we're up to 94% accuracy with just a very simple model. And probably 20 years ago, this would have been state of the art. We can then even sort of take it to the next step and make it even further by making it a convolutional network. By making it a convolutional network. So the one I just showed you there was what we call a fully connected network. By making it a convolutional network, again, the model is just slightly a little bit more complicated but you can see it's not many lines of code at all. Again, showing you what the model actually looks like, compiling the model and then training the model. And here, with not that long, we're up to 99% accuracy on this data set. And this is training from scratch. If we were using something like a pre-made model, we could actually train it much quicker and we could do much more advanced images and stuff like that. And it would still be able to do it really quickly. This one I deliberately ran through on my laptop before to show you, it's not gonna take forever on a laptop. It's taking about two minutes or something on a CPU. On a GPU, this will take like, yeah, two, three seconds per epoch. It's much, much quicker. We can see this is, we can get some accuracy and sort of see what's going on in our model. Okay, I will go back to finish up. So if you are interested in learning more of this sort of stuff, these are some books that you should definitely check out. I would say the one on the left is probably the most difficult. So start with either of the other two first. In fact, the book on the right there by François Chalet, François Chalet is the creator of Keras. So that book actually goes through everything, using Keras and explaining to you how to do it. And this is something you can just sort of do as a passion project in a month. You can get through that book and learn all the key concepts for deep learning. If you're looking for courses, definitely, it's worth checking out Andrew Ung's course. Jeremy Howard has a good course at fast.ai. Jeffrey Hinton has a course which is somewhat out of date now but is still worth going through because he's pretty much one of the key founders of deep learning and neural networks. And also Stanford has numerous courses online. If you want to come in person and do a course, this is a course we're going to be doing in Singapore. I think it's three weeks from today, the 14th of April. So if it's something that you're interested, come along. The whole idea with this course is that we want people to bring projects along and we're going to start going through and the whole idea with this course is that you should be able to build a basic project by the end of the three nights. We did this once before where we just did everything in one day and realized the problem with that was that people went home, tried a lot of stuff out, more stuff out and realized they had all these questions. So we've now added evening sessions to basically answer questions and go through and help people who are not understanding certain parts of it. Okay, just to sort of wrap up, I would say have a clear application of what you want to build. Realize that this field is too big to know everything. Even for people who are professional researchers in it, I can tell you that having been at the Had a Paper at NIPS conference last year and talking to a lot of professionals, people specialize, right? So don't try to learn everything yourself because you're going to get totally overwhelmed. Well, another thing I would say is if you don't understand one person's explanation for something, find someone else's. This material can often be very difficult to explain and often people tend to click with one explanation versus another explanation, both of which might be quite valid ways to explain something. Again, go back to the basics. It's all about just the IO, the connectivity patterns, your losses, your optimizers. And inclusion, I'd say everyone who succeeds learns deep learning in a different way, but everyone who fails fails in the same way, they give up. So it's not that difficult. If you're prepared to put the time in and do it, it's something that you can certainly do and I encourage you to do it. Thank you. Interest of time, no questions.


Everybody forced here ripping 22 23 origins basketball hobby hybrid 10 box number three random teams You can always find these results on screen anytime late sports cards calm. Let's go ahead and get started. Here we go Supposed to mass peas pulling auto You put not you put something that wasn't your name on the On the shipping address God taking the leap Lera via Murray guys euphoria Zion Peyton Watson rookie to 115 for the nuggets Nucleus Thomas DeMars and Lambier to 99 euphoria John and the Kaminga Warriors. You said that was in limited number 16 Yeah, I don't see anything that says me on Limited number 16 catapults Russell Westbrook Basketball towns and taking the leap of Blake Wesley. We got you. So last initial see there's an Andrew C in there So I guess I can't see anything Okay, yeah, we got how low got chat a Kaminga euphoria Yeah, I had no idea about that one either Like a Shopify issue problem. It literally says that's exactly what it is hardaway mullin and Mitch Richmond So is a Shopify thing barry Smith Basketball Sohan and catapults and bead or some Shopify nonsense. No, yeah, we get you. You're fine We want to make sure it's correct to to 99 a Barclay Elijah on a Drexler. We don't want we don't want issues Taking the leap Dale and Terry for Chicago AJ Griffin rookie to 115 for Atlanta or euphoria is Luca Donchich popular show euphoria Yes, HBO show Popular got a nucleus Barclay Elijah on Drexler Jalen Williams catapults Jalen Brown jade naive Durant to 115 for the Nets taking the leap is Oshag Bajie for the jazz My guys prism up next just had a couple teams left. Let's get those filled up Be there pink catapults the 99 to Westbrook Lakers Chat home grand origins euphoria or euphoria there. It's all origins Euphoria was Damian Lillard to 60 Jalen Green Rockets and Nucleus green KP J and Jabari Smith Nice Ryan Imagine that's super tough in a hangar pack 616 you want to go last team left on the next prism is the Nets and they are $44 Nets just the Nets everybody. I'm sorry to link there Yes, the Nets Yeah, a lot of spots coming to now. Thank you very much everybody We'll keep going with origins H2 here. We'll be pulling a snake guys are a big bang We pulled both in the last 10 boxers, so oh Let's take a power play goal I'm down. I'm I'm good with that So on you got Nucleus Rockets again there old Rockets are Easton catapults Jalen Brown Just a good feed advantage of the power player rebound basketball Evan Mobley Cavaliers taking the leap of Bajie jazz Shade on sharp pink catapults down of a Mitchell Cavaliers in 99 Donnie's boy Yeah, you're a Cavs fan I mean your best player You for you Damie Lillard Keegan Murray Nice Mather and the 115 Pacers Rookie there Nucleus young Rockets that time Brown James Nucleus Celtics Pierce Allen and KG Safari Smith Christian Brown rookie auto there we go Congrats Nuggets Tough pull an auto in Origins H2 There we go nugs good stuff to nice hit Dominique Wilkins catapults It's brown actually confusing it's like a straight-up German pronunciation of it Van Vleet to 115 for the Raptors taking the leap Keegan Murray Malachi brand I'm taking the leap rookie Chet Holmgren to 99 for the Thunder euphoria Larry Bird Wesley Van Vleet to 115 for the Raptors and Nucleus Wade O'Neill and morning. That's pretty cool Shaq flash and zoe five boxes left here. We already hit an auto. Let's see another auto SP here about or all of it Let's see everything Yeah, I can't so you know gotta do what you gotta do just pull every cool card Nice next one sold out So 38 sold out that's prism next 39 has three to go and then they're seven to go on 40 All right, then there's Star Wars and football for the rest of the night everybody after the prism four boxers So let's get a filled up. There's a Matherin Watson catapults LeBron Do Donnie when you give you Bogey only get Bojangles. Let me know so I can get some Taking a leap Johnny Davis Yes, that's a great idea so Han It's a 115 chilli green rockets. It's so far away from you. I'll literally pay. I'll literally pay for your lunch, Donnie. Oh Just to bring me some Bojangles As long as 20 bucks in fetus both Pink taking the leap AJ Griffin the 99 for the Hawks, I think I think I should cover that I'm not gonna get like a drink or anything You don't get a meal. No I'm gonna get like five chickens and wings siacum Barnes of Ampleet By that I mean just like one or two Jaden Hardy Basketball Van Vleet catapults Vince Carter Ratchers or more Come on snake eyes. Come on Can roll snake eyes all the time before I do a random Like exclusively those catapults Kevin right there the night I rolled like six straight nines Having a check out issue Natty. Yeah, you're not the only person. It's the second person I've heard say that Paloban carrot taking the leap Not everybody's having that issue though. I don't know what it is Make sure you clear your cash and try to check out that must be something that's going on with it Someone's having issues to PayPal earlier too But orders are still coming in so I don't know what specifically be causing that It's a 60 Zion Williamson for the pelicans for you curry to 25 Zion euphoria. There you go pelicans to Zion numbered cards in the box Now revolution soccer has been coming out for a few years now like three or four years He can marry nucleus for the bucks there Rory broke Shopify again. Yeah, it makes sense basketball Jamal Murray catapult Zion They were having issues earlier though Shopify was Like it was actually down for a bit the back end the front the front end of the site was working the whole time orders are still coming in but like We couldn't access the back end of the site whatsoever It's not a it's not a it's not a day. It's not a day ever that shop about having some sort of issue Nemar nucleus pelicans there Ingram Williamson and bison Daniels Word on Merlin soccer. What do you mean? Like what's what's up with it? Jane Hardy catapults kawaii Leonard Release I haven't seen a release date for 22 23 Merlin. It'll be it'll be it'll be later. It'll be later this year Origins Andrew Wiggins and the basketball taking leave Yovitch You can see here. It's not on the calendar yet Merlin 98 heritage already released with Merlin chrome. It'll it'll it'll be Months before that before that comes out Everything really Nothing released on time paint to 99 Magic Johnson Lakers except like series 1 baseball series 2 baseball things like that release on time Terry Jalen Williams Euphoria Jalen Brown Hey, nice Natty. Yeah, maybe she's thinking we're just got to try a couple times. It'll work. I don't know Dear and Fox swiper to 115. Let's go Kings like the purple beam God Booker Paul and Aten They should have a sound effect where it's like a light saber Igniting cool That would be cool. Let's set guys Paulo Chet Holmgren Teals catapults Deontay Murray taking a leap is when no more more spots coming in Usmane Jang Basketball Yovitch Euphoria Tatum. Yeah, the cup was three years late. That's true We're like two I guess it always came out like Afterseason stuff catapults a 99 BAM out of bio heat Nucleus Lebron Davis and Westbrook Jane Hardy the 115 rookie for the Mavericks catapults Yanis Last box coming up here Sorry guys a lot going on right now Nucleus Hawks catapults Paul George Davis to 60 pink for the Lakers taking the lead shade on sharp Blazers Terry number card coming up is catapults to 25 Jason Tatum Celtics Ivy there Euphoria Jalen Green Donovan Mitchell the 115 Cavaliers and Nucleus and bead hardened and maxi that'll do for the break everybody. There was a Christian Brown auto and some numbered cards We're moving on to prism now. Thank you