Welcome to the final of four presentations to help prospective candidates learn more about BC general local elections. This year's general local election may take place during a pandemic. It may be important for prospective candidates to contact their local government to understand any procedures they have in place to administer this year's general local election during COVID-19. So you've been declared a candidate and have been busy campaigning. This presentation will give you an overview of the different voting opportunities, your responsibilities, and taking office if you are elected. All local governments will hold advanced voting on Wednesday, October 5, 2022. Local governments with populations greater than 5,000 are required to hold at least two advanced voting opportunities. Check your local government's election procedure bylaw for details on advanced voting opportunities. Your local government will also publish details about advanced voting. Candidates may wish to vote in advance because they may be busy on general voting day. Local governments may hold special voting opportunities at places like hospitals and long-term care facilities to provide electors who may not otherwise be able to attend a voting place, an opportunity to cast their ballots. Through their election procedure bylaw, local governments may choose to allow electors to vote by mail ballots. If they do, all electors are eligible to vote by mail and can contact their local government to find out more information. The big day, what can you expect on general voting day? General voting day will take place on Saturday, October 15, 2022. Voting places will be open from 8 a.m. to 8 p.m. local time. Your local government will provide you with information about the location of voting places. Candidates may only be present at a voting place to cast their ballot, but must not campaign or place signs within 100 meters of a voting place during special, advance, or general voting. It is an election offense to campaign within 100 meters of a voting place during voting proceedings. After the polls close, election officials will begin to count ballots. Candidates and candidate representatives, like scrutineers or the official agent, may observe the ballot counting process. The chief election officer, or CEO, will provide candidates with information about observing the ballot counting. The chief election officer may release preliminary results after the ballots have been counted on general voting day, while official results must be announced by October 19, 2022, at 4 p.m. If the same number of votes were received by two or more candidates, the chief election officer must refer the election for judicial recount. Under specific legislative circumstances, an eligible elector, candidate representative, or the CEO, may apply to the provincial court for a judicial recount. This application must be made by October 24, 2022. After the general local election, all candidates that have been elected or acclaimed or have withdrawn or lost the election must file a campaign financing disclosure statement with elections BC by January 13, 2023. This requirement also applies to electoral organizations and third-party sponsors. Congratulations! You've made it through the election, and now you can take the oath of office and attend your first meeting. Every municipal council member and electoral area director must make an oath of office or solemn affirmation before they can assume their positions. Oaths and affirmations must be made within 45 days after the official election results are announced. Municipal council members formally take office at the first regularly scheduled council meeting following the general elections, which must be in the first 10 days of November. The term of office for regional district electoral area directors begins at the first regularly scheduled board meeting in November. There is a new legislative requirement that elected officials must consider whether council should establish or review its current code of conduct within the first six months of taking office. Thank you for making a commitment to your local community. Please watch the videos available on the Ministry of Municipal Affairs webpages, on the characteristics of locally elected officials, local government decision-making, and the roles and responsibilities of locally elected officials.


Neural networks are good for learning lots of different types of patterns. To give an example of how this would work, imagine you had a four pixel camera. So not four megapixels, but just four pixels, and it was only black and white. And you wanted to go around and take pictures of things and determine automatically then whether these pictures were of solid, all-white or all-dark image, a vertical line, or a diagonal line, or a horizontal line. This is tricky because you can't do this with simple rules about the brightness of the pixels. Both of these are horizontal lines, but if you tried to make a rule about which pixel was bright and which was dark, you wouldn't be able to do it. So to do this with the neural network, you start by taking all of your inputs, and in this case our four pixels, and you break them out into input neurons. And you assign a number to each of these depending on the brightness or darkness of the pixel. Plus one is all the way white, minus one is all the way black, and then gray is zero right in the middle. So these values, once you have them broken out and listed like this on the input neurons, it's also called the input vector, or array. It's just a list of numbers that represents your inputs right now. It's a useful notion to think about the receptive field of a neuron. All this means is what set of inputs makes the value of this neuron as high as it can possibly be. For input neurons, this is pretty easy. Each one is associated with just one pixel, and when that pixel is all the way white, the value of that input neuron is as high as it can go. The black and white checkered areas show pixels that an input neuron doesn't care about. If they're all the way white or all the way black, it still doesn't affect the value of that input neuron at all. Now, to build a neural network, we create a neuron. The first thing this does is it adds up all of the values of the input neurons. So in this case, if we add up all of those values, we get a 0.5. Now, to complicate things just a little bit, each of the connections are weighted, meaning they're multiplied by a number. That number can be one or minus one or anything in between. So for instance, if something has a weight of minus one, it's multiplied and you get the negative of it, and that's added in. If something has a weight of zero, then it's effectively ignored. So here's what those weighted connections might look like. You'll notice that after the values of the input neurons are weighted and added, the final value is completely different. Graphically, it's convenient to represent these weights as white links being positive weights, black links being negative weights, and the thickness of the line is roughly proportional to the magnitude of the weight. Then after you add the weighted input neurons, they get squashed. And I'll show you what that means. You have a sigmoid squashing function. Sigmoid just means s-shaped. And what this does is you put a value in, let's say 0.5, and you run a vertical line up to your sigmoid, and then a horizontal line over from where it crosses, and then where that hits the y-axis, that's the output of your function. So in this case, slightly less than 0.5. It's pretty close. As your input number gets larger, your output number also gets larger but more slowly, and eventually, no matter how big the number you put in, the answer is always less than 1. Similarly, when you go negative, the answer is always greater than negative 1. So this ensures that that neuron's value never gets outside of the range of plus 1 to minus 1, which is helpful for keeping the computations in the neural network bounded and stable. So after you sum the weighted values of the neurons and squash the result, you get the output. In this case, 0.746, that is a neuron. So we can call this, we can collapse all that down, and this is a neuron that does a weighted sum and squash the result. And now instead of just one of those, assume you have a whole bunch. There are four shown here, but there could be 400 or 4 million. Now to keep our picture clear, we'll assume for now that the weights are either plus 1, white lines, minus 1, black lines, or 0, in which case they're missing entirely. But in actuality, all of these neurons that we created are each attached to all of the input neurons, and they all have some weight between minus 1 and plus 1. When we create this first layer of our neural network, the receptive fields get more complex. For instance here, each of those end up combining two of our input neurons, and so the value, the receptive field, the pixel values that make that first layer neuron as large as it could possibly be look now like pairs of pixels, either all white or a mixture of white and black, depending on the weights. So for instance, this neuron here is attached to this input pixel, which is upper left, and this input pixel, which is lower left, and both of those weights are positive. So it combines the two of those, and that's its receptive field, the receptive field of this one plus the receptive field of this one. However, if we look at this neuron, it combines our this pixel, upper right, and this pixel, lower right. It has a weight of minus 1 for the lower right pixel, so that means it's most active when this pixel is black. So here is its receptive field. Now because we were careful of how we created that first layer, its values look a lot like input values, and we can turn right around and create another layer on top of it the exact same way, with the output of one layer being the input to the next layer. And we can repeat this three times or seven times or seven hundred times for additional layers. Each time the receptive fields get even more complex. So you can see here using the same logic, now they cover all of the pixels, and more special arrangement of which are black and which are white. We can create another layer. Again, all of these neurons in one layer are connected to all of the neurons in the previous layer, but we're assuming here that most of those weights are zero and not shown. It's not generally the case. So just to mix things up, we'll create a new layer, but if you notice our squashing function isn't there anymore, we have something new called a rectified linear unit. This is another popular neuron type. So you do your weighted sum of all your inputs, and instead of squashing, you do rectified linear units, you rectify it. So if it is negative, you make the value zero. If it's positive, you keep the value. This is obviously very easy to compute, and it turns out to have very nice stability properties for neural networks as well in practice. So after we do this, because some of our weights are positive and some are negative, connecting to those rectified linear units, we get receptive fields and their opposites. You look at the patterns there. And then finally, when we've created as many layers with as many neurons as we want, we create an output layer. Here we have four outputs that we're interested in. Is the image solid, vertical, diagonal, or horizontal? So to walk through an example here of how this would work, let's say we start with this input image shown on the left. Dark pixels on top, white on the bottom. As we propagate that to our input layer, this is what those values would look like. The top pixels, the bottom pixels. As we move that to our first layer, we can see the combination of a dark pixel and a light pixel summed together get us zero, gray. Whereas down here we have the combination of a dark pixel plus a light pixel with a negative weight. So that gets us a value of negative one there, which makes sense because if we look at the receptive field here, upper left pixel white, lower left pixel black, it's the exact opposite of the input that we're getting. And so we would expect its value to be as low as possible, minus one. As we move to the next layer, we see the same types of things, combining zeros to get zeros, combining a negative and a negative with a negative weight, which makes a positive to get a zero. And here we have combining two negatives to get a negative. So again, you'll notice the receptive field of this is exactly the inverse of our input. So it makes sense that its weight would be negative, or its value would be negative. And we move to the next layer. All of these, of course, these zeros propagate forward. Here, this has a negative value and it has a positive weight. So it just moves straightforward because we have a rectified linear unit, negative values become zero. So now it is zero again too, but this one gets rectified and becomes positive. Negative times the negative is positive. And so when we finally get to the output, we can see they're all zero except for this horizontal, which is positive. And that's the answer. Our neural network said this is an image of a horizontal line. Now, neural networks usually aren't that good, not that clean. So there's a notion of, with an input, what is truth? In this case, the truth is this has a zero for all of these values, but a one for horizontal. It's not solid, it's not vertical, it's not diagonal. Yes, it is horizontal. The arbitrary neural network will give answers that are not exactly truth. It might be off by a little or a lot. And then the error is the magnitude of the difference between the truth and the answer given. And you can add all these up to get the total error for the neural network. So the idea, the whole idea with learning and training, is to adjust the weights to make the error as low as possible. So the way this is done is we put an image in, we calculate the error at the end, then we look for how to adjust those weights higher or lower to either make that error go up or down. And we, of course, adjust the weights in the way, then make the error go down. Now the problem with doing this is each time we go back and calculate the error, we have to multiply all of those weights by all of the neuron values at each layer. And we have to do that again and again once for each weight. This takes forever in computing terms, on a computing scale. And so it's not a practical way to train a big neural network. You can imagine, instead of just rolling down to the bottom of a simple valley, we have a very high dimensional valley and we have to find our way down. And because there are so many dimensions, one for each of these weights, that the computation just becomes prohibitively expensive. Luckily there was an insight that lets us do this in a very reasonable time. And that's that if we're careful about how we design our neural network, we can calculate the slope directly, the gradient. We can figure out the direction that we need to adjust the weight without going all the way back through our neural network and recalculating. So just to review, the slope that we're talking about is when we make a change in weight, the error will change a little bit. And that relation of the change in weight to the change in error is the slope. Mathematically, there are several ways to write this. We'll favor the one on the bottom. It's technically most correct. We'll call it DEDW for shorthand. Every time you see it, just think the change in error when I change a weight. Or the change in the thing on the top when I change the thing on the bottom. This does get into a little bit of calculus. We do take derivatives. That's how we calculate slope. If it's new to you, I strongly recommend a good semester of calculus just because the concepts are so universal. And a lot of them have very nice physical interpretations, which I find very appealing. But don't worry, otherwise just gloss over this and pay attention to the rest and you'll get a general sense for how this works. So in this case, if we change the weight by plus one, the error changes by minus two, which gives us a slope of minus two. That tells us the direction that we should adjust our weight and how much we should adjust it to bring the error down. Now to do this, you have to know what your error function is. So assume we had an error function that was the square of the weight. And you can see that our weight is right at minus one. So the first thing we do is we take the derivative, change in error, divided by change in weight, DEDW. The derivative of weight squared is two times the weight. And so we plug in our weight of minus one and we get a slope, DEDW of minus two. Now the other trick that lets us do this with deep neural networks is chaining. And to show you how this works, imagine a very simple trivial neural network with just one hidden layer, one input layer, one output layer, and one weight connecting each of them. So it's obvious to see that the value y is just the value x times the weight connecting them, w1. So if we change w1 a little bit, we just take the derivative of y with respect to w1, and we get x. The slope is x. If I change w1 by a little bit, then y will change by x times the size of that adjustment. Similarly for the next step, we can see that e is just the value y times the weight, w2. And so when we calculate DEDY, it's just w2. Because this network is so simple, we can calculate from one end to the other, x times w1 times w2 is the error e. And so if we want to calculate how much will the error change, if I change w1, we just take the derivative of that with respect to w1 and get x times w2. So this illustrates, you can see here now, that what we just calculated is actually the product of our first derivative that we took, the dy, dw1 times the derivative for the next step, DEDY, multiplied together. This is chaining. You can calculate the slope of each tiny step, and then multiply all of those together to get the slope of the full chain, the derivative of the full chain. So in a deeper neural network, what this would look like is, if I want to know how much the error will change, if I adjust a weight that's deep in the network, I just calculate the derivative of each tiny little step all the way back to the weight that I'm trying to calculate, and then multiply them all together. This computationally is many, many times cheaper than what we had to do before of recalculating the error for the whole neural network for every weight. Now, in the neural network that we've created, there are several types of back propagation we have to do. There are several operations we have to do. For each one of those, we have to be able to calculate the slope. So for the first one is just a weighted connection between two neurons, a and b. So let's assume we know the change in error with respect to b. We want to know the change in error with respect to a. To get there, we need to know db, da. So to get that, we just write the relationship between b and a, take the derivative of b with respect to a, we get the weight w, and now we know how to make that step. We know how to do that little nugget of back propagation. Another element that we've seen is sums. All of our neurons sum up a lot of inputs. To take this back propagation step, we do the same thing. We write our expression, and then we take the derivative of our endpoint, z, with respect to our step that we're propagating to a, and dz, da in this case is just one. Which makes sense. If we have a sum of a whole bunch of elements, we increase one of those elements by one, we expect the sum to increase by one. That's the definition of a slope of one. One to one relation there. Another element that we have that we need to be able to back propagate is the sigmoid function. So this one's a little bit more interesting mathematically. We'll just write it shorthand like this, the sigma function. It is entirely feasible to go through and take the derivative of this analytically and calculate it. It just so happens that this function has a nice property that to get its derivative, you just multiply it by one minus itself. So this is very straightforward to calculate. Another element that we've used is the rectified linear unit. Again, to figure out how to back propagate this, we just write out the relation. b is equal to a, if a is positive, otherwise it's zero. And piecewise, for each of those, we take the derivative. So db, da is either one, if a is positive, or zero. And so with all of these little back propagation steps and the ability to chain them together, we can calculate the effect of adjusting any given weight on the error for any given input. And so to train, then, we start with a fully connected network. We don't know what any of these weights should be. And so we assign them all random values. We create a completely arbitrary random neural network. We put in an input that we know the answer to. We know whether it's solid, vertical, diagonal, or horizontal, so we know what truth should be. And so we can calculate the error. Then we run it through, calculate the error, and using back propagation, go through and adjust all of those weights a tiny bit in the right direction. And then we do that again with another input, and again with another input for, if we can get away with it, many thousands or even millions of times. And eventually, all of those weights will gravitate. They'll roll down that many-dimensional valley to a nice low spot in the bottom where it performs really well and does pretty close to truth on most of the images. If we're really lucky, it'll look like what we started with, with intuitively understandable receptive fields for those neurons and a relatively sparse representation, meaning that most of the weights are small or close to zero. And it doesn't always turn out that way. But what we are guaranteed is that it'll find a pretty good representation of, you know, the best that it can do adjusting those weights to get as close as possible to the right answer for all of the inputs. So what we've covered is just a very basic introduction to the principles behind neural networks.  Here are a few resources that you'll find useful. You'll want to go and learn about bias neurons. Dropout is a useful training tool. There are several resources available from Andre Carpathi, who is an expert in neural networks and great at teaching about it. Also, there's a fantastic article called the Black Magic of Deep Learning that just has a bunch of practical from the trenches tips on how to get them working well. If you found this useful, I highly encourage you to visit my blog and check out several other How It Works style posts. And the links for these slides you can get as well to use however you like. There's also a link to them down in the comments section. Thanks for listening.


What was it like in practice going against BM Manning? Well up to that point I played with some pretty good quarterbacks but nothing compared to him. He was by far the most prepared. I'm not even going to say he was the most talented you know physically but he knew where to put the ball. He was always a step ahead. You know God's rally around him. He's a true leader by example. He's going to yell and scream and make sure everything's almost perfect. I mean unfortunate for me I was in my 12th year or 13th year by the time he came so you know it is what it is. I relished that time at the at the time. It was you know I think every player should have that privilege of playing with a great player like that to really see what it's like to be a pro.


By the end of this year, the deficit will be down to less than half of what it was before I took office. The only president ever to cut the deficit by more than $1 trillion in a single year. President Joe Biden's claim that he's responsible for the largest reduction in the federal deficit in history is, in short, bullshit. In the roughly 18 months since he took office, Biden's policies have only added to America's long-term budget deficit, which is getting worse and worse as government spending outpaces tax revenue. Let me remind you again, I reduced the federal deficit. All the talk about the deficit from my Republican friends, I love it, I reduced it $350 billion in my first year in office. And we're on track to reduce it by the end of September by another $1,500 billion, the largest drop ever. So what is Biden talking about? He's taking credit for expiring emergency spending measures. Because of the COVID-19 pandemic, the federal government ran a $3.1 trillion deficit in fiscal year 2020, which fell to a $2.7 trillion deficit in 2021. Now that the pandemic spending is finally over, the deficit is expected to fall again to $1.4 trillion, according to the Congressional Budget Office, though we won't have a final figure until the fall. Once we eliminate other outlier years like 1943 when the U.S. was participating in a global war, this year's budget deficit will likely end up as one of the largest in American history as a percentage of GDP. So Biden doesn't really have anything to brag about. Let me remind you again, I reduced the federal deficit. No, you didn't. As expiring COVID spending made our fiscal situation look better on the surface, Biden was busy adding $2.4 trillion to the long-term deficit, according to the CBO's most recent estimates. There was Biden's American Rescue Plan, the bipartisan infrastructure package, and the $1.5 trillion federal budget that passed in March. If the president's multi-trillion dollar Build Back Better plan had somehow become law, as heritage analyst David Ditch has pointed out, we'd be in even worse shape. Before Biden took office, projected budget deficits through 2031 looked like this. And here's how they look a year and a half into his presidency. The federal government's budget is on the road to hell, warned former congressional budget office director Douglas Holtz-Eakin back in 2011. That was before Obama, Trump, and now Joe Biden each went on a spending binge that has made our fiscal future even less stable. Biden wants you to focus on just a small part of the overall picture. The deficit for the past two years and this year are a misleading indicator of America's overall fiscal health. And to claim that he's responsible for reducing the deficit is worse than misleading. It's an outright lie.


If you try to be what you thought someone wanted you to be and it still didn't work, then the pain you're having is about you abandoning you for them. It's not about them and their rejection of you anymore.


Hi everyone, Tasha with Start at School Crochet. Welcome to part two of the Heart's Baby Blanket Cal. Please subscribe now if you haven't already and check out part one. In today's tutorial, we're going to go over joining these beautiful squares and also steam blocking. This tutorial picks up where part one ended. So if you haven't watched part one, make sure you head over and check out part one. And let's get started. I did nine squares and I haven't blocked these and each one of my square measures approximately 10 and a half inches by about 9 and a half inches. The total blanket width is not that big if you're doing a baby blanket, but if you want to make a larger blanket, you can create more squares or make a larger border which is also part of what we're going to do too. The border is going to be a lot of fun. I'm going to try something new. Using different strands of yarn. So this right here is about 30 inches wide, which is not big. That's a baby blanket. But like I was saying, if you want to add more, you can add more border around the edges. The way to do that is you just use normal stitches, one in each stitch around, and then to corner out your stitches, you do two of whatever stitch you chose, a chain in between, chain one or chain two, and then two more of those stitches all in the corner, and then you work your others. So you could work basically colored treble around the edges here if you wanted to add another inch onto each one of these squares. So there are tons of ways that you can modify your blanket to make it larger. I'm not. I'm going to keep it a baby blanket size, and the end product should be about 30 inches by about 32 inches. So something fun will plus the border, so maybe a little bit longer than that. Now I'm going to weave in these tails, and if you don't know how to weave in tails, here's a little quick tutorial on that. I usually grab a needle that has a very large eye, and this one has a very large eye, especially for using this plushy yarn. I twist the top of the yarn, and then pull it through. And then for C2C, I always think, you know, first you're going to pull it tight just to make sure that there's no looseness to it. And I always look for the double crochets right here, like this. And my first thing is I skip over the first double crochet, and then go back through the bottom of the next two, and pull it tight like that. And then I will skip over this first one, and go back through the other two, and then kind of weave it back through the next batch up to a space where I'll be back at the bottom of the next set of double crochets. Then pull it through, but don't pull it too tight.  So make sure you pull it loose. Then once I'm there, I'll go back through, like I did by skipping that first double crochet, and going back through. And there, I just kind of do back and forth, mishmash, wherever I feel like I need to put it, and if it's weaved in enough, and I think that's good enough. So I'm just going to cut that. I'm going to pull it tight just a little bit, and then cut it. And then I'm going to stretch it out just a little bit, just to hide that tail in there. And you should be good to go. This yarn ends up having a lot of fuzzies, so you end up having to kind of pick them off and just brush them away. But that's how you weave in tails, if you're not familiar with it, or if you're new to it. So go ahead and continue weaving in all of your tails. And then when you're finished, we will end up working these squares together, and you're going to need your crochet hook and whatever chosen yarn you want to use. I'm going to use the cream colored because I just really like the seamlessness of the floating hearts. I don't really want them to be blocked out or squared, so I just want them floating hearts inside of my blanket. But I have a feeling that it would look super cute if you also rounded out or cornered out, or if you blocked them with color. But I just really don't want squares. I just kind of want my hearts to float inside the white, which is what the look I was going for. And then I want to have a colorful border with kind of poofies. I don't really know yet what I'm going to do, but we're going to do this together on camera. And then the pattern will be available on my blog. You have to get the zen feeling of weaving in tails. Okay, have you decided on how you want to organize your colors yet? Have you blocked them yet? That would be the next step to do is to block them. Let me grab my blocker, my steam blocker, and I'll show you how to block squares, and how to make them all the same size so that your blanket is uniform and it looks nice. And also remember whichever side is your front side, and you can tell the difference between the front side and the back side just by looking at the way that the half double crochet is on top.   The back side doesn't really do that as much as this side, and I'm sure you can tell the difference just by looking at it. See how this has more of a squared out, and this one just doesn't have that same look to it. It kind of looks puffy right here. That's how you can tell the difference between the front side and the back side of your squares, because that will be important when we're actually seaming these together and crocheting them together. I saved these little tails so I can make TikTok videos. I use these blocking mats. You can use anything that's easy for you to use. You can use the foam mats that your child plays on. You can use anything that's pretty thick that you can stick pins into safely, and you want to make sure that you get some nice pins that aren't going to rust up when you actually do your blocking.  Each square on a blocking mat is one inch, so you can kind of make all of your squares the same height and the same width just by following the little squares on your grid. I have these. They're knit blockers by Knitter's Pride, and I'll put a link for these in the description down below, because I really love using them. I think they're great. They have two different sizes. They have a large size and then a corner size. So what I normally do first is I'll block out the corners, and I'll start in a place where I want my corner to be, so I'm going to move this over to right here where it matches up with this corner here and put it right up there to that corner. I'm going to stretch this out just a little bit with the edge of my blocking, and I'm going to put it straight through like that. And I'm going to do the same with this one right here. And then from that starting point, I'm going to stretch it out and make sure that it's all the same and kind of stretch my stitches out. And it doesn't look like it's going to be matched up completely because it was about nine and a half, so I'm going to go to the middle of the square up here and block that right there about nine and a half inches. I'm thinking that's nine and a half. Let's count. Let's see. We've got one, two, three, four, five, six, seven, eight, nine, ten, ten and a half. So I'm going to stick that right there through the top of my stitches. And I'm going to put that right there. Then I'm going to go down and kind of bring these over to that same space along the edges. So it looks like it'll be about nine and a half inches. So it'll be one, two, three, four, five, six, seven, eight, nine. So now I'm going to pull this up to match the height of these up here. And I'm going to do the same on this side and match up all over here. And I blocked it out, so that looks good to me. I'm going to set that aside. Then I'm going to grab my steam blocker and show you how to steam block. I did a full tutorial video on using steam blockers, but I really love this steam blocker. It heats up really super fast. You just push this button on the front and the light comes on and once the light stops flashing it's ready to steam, which makes it so easy. So I just set it aside. I did a full tutorial video on this though. If you want to check it out, I'll put that link in the description below as well for how to steam block. But it's basically what I'm showing you right now too. So my light's already ready, which is great. I'm just going to depress this and you'll see the steam start coming out. You don't want to actually touch your project with the steamer. You just kind of want to make it go over your stitches, but since these are pretty thick stitches, I'm going to probably let it really do its work and I might even try to stretch them out a little bit while it's working. I think that's good. I'm going to stretch these out just a little bit while it's still kind of... or it's still kind of soft. So mine were about nine and a half by ten and a half. I'm going to go ahead and take these off and start putting these back where they belong in here. It's always good to put them back because these are sharp. Ta-da! Alright, there's our finished square that's all blocked and it looks really pretty and it's all the same size. So you can tell the difference between a blocked square and a not blocked square. You can see that the stitches have kind of settled down a little bit. They're not as puffy as they were before and you can see that it's not curling on the edges and it's a little bit wider.  You can block them before you join or you can join and then block everything afterwards. But I find that blocking your squares before you join makes it look a lot neater and a lot easier because then you know you're matching up everything together. So you can definitely tell the difference in something that's been relaxed and something that's not. I really love the puffiness of the not relaxed ones but eventually this is what it's going to end up looking like because of wear and tear and all that kind of stuff. So it's not going to keep this big puffy look forever just for a little while. Alright, so I'm going to finish blocking my squares and then we'll come back and I'll show you how to do the joins. So once we have all of our squares joined we'll be able to actually start crocheting the border around the entire blanket which is going to be really neat and I did a bunch of colors as you can see. So we're going to join through and create a stitch that gives a little bit of a raised look to the center of each of the squares. You can use a mattress stitch if you want if that's easier for you. Kind of wanting to play around with this a little bit. You can also use a different color in between your squares if you want. Like I was saying before I kind of want my blanket to just have floating hearts and just the design of the square inside just to kind of make it simple. But I'm really excited about it. So let's get started.  So I'll put a little graphic up there and show you how I did my colors. I tried to mix and match the cools and the warms. Reds are warm. Yellow is kind of a neutral. Greens are cool. And the blues are cool. So any blue purples are kind of neutral in between. So you can organize it however you want your blanket to look. Figure that out and then once you're ready to join and you figured out your pattern come back and we'll start joining. And I'm going to do the same. Okay so I decided how I wanted mine to be joined. This is the bottom row that's on top right here. And so what I'm going to do is kind of lay this out the way I want it to be joined. That's the bottom row. And I just like the way this looked so I kind of played around with it a little bit and this is what I'm going to do. Okay so once you have all of your squares laid out in the order that you want them we will start working our squares in a way that you're going to work up one whole column and so you're going to join basically six of your squares first and then you're going to take these other three and start joining up that way. And after they're all joined that way you're going to work across this way. So let's get started and I'm going to leave these the way they are because I don't want to mess up my order. So how you start and I'm joining these using a method that I saw on Bella Coco Crochet which I really liked because I went searching for a bunch of different method, join methods and I was like hmm which one, which one, which one but I really liked this one because it was simple and not only that but it was a little bit different and if you wanted to use a different color yarn it creates this really cool kind of like arrow pattern on the inside. But I'm using the same color yarn and I'm kind of tempted to use a different color but nope, I'm going to stick with my original plan. So how we start is you want to create a slip knot on your hook however you like to do your slip knots. Then we're going to find the corner that we worked for which was between the two half double crochets so mine's right here. We're going to insert our hook from top to bottom into the first corner square and then do a single crochet stitch around that. Then we pull our stitch out just a little bit and then head on over to the other square find that same corner we're going to go in from top to bottom on that then attach your yarn back attach your yarn onto the hook again and slip stitch through then you're going to find the next half double crochet and work into that and do a single crochet then we pull up again let go find the corresponding stitch put your hook back on your loop and then slip stitch through and you work that along the entire length of joining so we do a single crochet then we take that out go into the next stitch slip stitch through then go back into the next stitch on your other piece and do a single crochet and remember you can hold on to that loop so you don't lose it and also if you want to do a variation of this you can do working through the back loops which I might try then do a single crochet I can tell that tension is probably going to be an issue with this stitch so you might want to test it every now and then bring it down and see it does kind of create this a little bit of like a herringbone look to it when you get going so this is going to be really pretty and I'm really excited about that and I wonder how easy it frogs that's the real test does it frog easy yes it does frog easy okay so let's start joining these squares and then once you join all your squares remember you're going to work up your columns up here and then work across that way and let's see how that looks with a different color yeah that looks really pretty I really like that so get to joining your squares and then when we come back we'll be working the border that goes around the entire blanket check out part 3 for the border pattern and happy crocheting thanks for being here


What if I told you that there were five traits that you see far more often in people living average and mediocre lives Versus people living and building these exceptional lives that they've always dreamed of Well in this video, I want to share some of the biggest lessons I've learned transforming myself from being a pretty average mediocre dude in every quality and in every sense Into someone that is gradually creeping his way up into Greater and greater states of high performance in all aspects of my life, not just my career What's up guys Alex Hine author of the book master the day now the first link in the description I've included a series of journaling exercises that you can do to go from being mediocre to being a high performer in life So if you click the first link in the description and download that worksheet, you'll also get a free email every couple days That's going to teach you how to use journaling to go from average to exceptional So you can check it out down there below now the first trait is that average people live a life based on the imitation of other average people Now I know that might seem really obvious But we take it for granted that the people we most often emulate in our lives are other average people We're literally following and taking advice from people that have gotten average lives Which they've gotten because they think average thoughts So we're creating carbon copies of people that are mediocre Now there's nothing wrong with being mediocre But the point is if you want an exceptional quality of life You have to emulate people that are doing and living what you want and you may not know any of those people personally You may have to go and seek them out Like if I listened to everyone in my world when I was in my early 20s I never would have booked a one-way ticket to China to go become a monk and study with kung fu masters Because I didn't know a single person that had ever done that who the hell books a one-way ticket to another country I didn't know anyone who did it, but I had to do it because I had to become my own role model and think What would the exceptional person that's built an amazing life do? So either think like that or emulate those people that are only doing it The second trait of very mediocre people is that when they're told no or it isn't possible They listen to that advice Dude listen, it's ridiculous the number of people Taking advice from people that have never done what they want to do So of course your friend or your mom or your dad is going to tell you no, it's not possible to be an astronaut No, it's not possible to be a youtuber. No, it's not possible to lose all that weight or to Attract that dream person that you want so deeply in a loving relationship Of course they're going to say that because they probably haven't even so to them They're arguing more in favor of their own limitations than they are in favor of your limitations They don't even know where their ceiling is because they're so in the box that they've usually done nothing with their lives and Yet we take their advice as if it's the word of God be very careful with this The third trait of very mediocre people with mediocre lives is that they trust other people's advice Even that of experts over their own intuition When it came time to write my first book master of the day I had a strong feeling that I wanted to create a book That was something that could really help my clients at the time by embodying all my weight loss and my goal-setting Personal growth philosophy in one, but it was specifically weight-loss metaphors for how to live a better life Now, you know how many people told me do you know the success of books? You don't know how to write a book. You don't know how to market a book You know how little money authors make all of these BS stories that the average person was telling me of course Told to me by people who were not authors And guess what this book master of the day not only has performed so well in terms of impact But financially this is definitely in the top 5% of books even compared to traditional authors I have a lot of friends traditionally published that this makes way more money then it's not all about money That's the struggle part of being an author Now if I listen to my friends and my family about the struggles of being an author rather than me listening to my intuition and figuring it out This book wouldn't exist nothing in my life would exist So consider that the fourth trait of very mediocre people is that they do as little as they need to Versus getting the job done You know when I became an entrepreneur I was kind of weirded out by the fact that we work 9 to 5 as just a general cultural thing in america or a long Or probably and we don't even take for granted the fact that or we take it for granted that This is just 9 to 5 we get paid for how many hours we're at work But not even based on our performance But when you become an entrepreneur you only get paid based on performance If you need to make three thousand dollars that month to survive It doesn't matter if it takes you zero hours per day or 100 hours in a week It doesn't matter. You have to do it So if you can write a book in one hour a day That's called being a smart entrepreneur That's called being an effective efficient entrepreneur But if it takes you 10 hours a day to write a book Then that's what it takes you And so many of us we choose to do the little but just the easiest thing the smallest thing Towards building our dream life and our goals. We don't want to work too hard. We don't want to sacrifice too many things Screw that Do what it takes including if it means working evenings working the weekends Sacrificing friends in the short run and just seeing each other on the weekends Do what it takes not what is cushy and easy and comfortable be uncomfortable Now built off of that the fifth trait of mediocre people Is that they think and they brag about how much they put in in terms of time Rather than focusing on the performance what they're getting done You know, I don't start watching netflix at 8 p.m. Because it's like 8 p.m. And it's time to watch netflix I start watching netflix when my crap is done for the day I'm doing a doctoral program and running a business and building out new things like if I had to write a new book I write until my quota is done for the day a lot of days. That's 11 p.m. Or later Don't stop. It's like oh, it's 8 p.m. It's time for my quality of life Let me pour my glass of wine and take it easy and then just Watch game of thrones hell no when you Look, we're all entrepreneurs if you consider yourself the entrepreneur the business owner of your own life who's architecting it And at the same time by that same token You have to be performance oriented If you want to lose weight your goal has to be of course to feel good But also to lose the damn weight if your goal is to get into a healthy relationship Your goal in the short run of course has to be happy But also get a friggin person to date if your goal is I need to get better sleep because it's affecting my health Your goal of course is to still enjoy your life, but get the friggin eight hours of sleep. You need to be healthy We beat around the bush by by not pushing ourselves by being so concerned with oh, I've done my 40 hours It's time for my work life balance Screw that do what it takes to build an exceptional life Focus on performance not the hours invested Now the cool part is that really productive people can do in a fraction of the time What the average person does in terms of productivity I've built my whole business in less than three hours per day and now while i'm doing my doctorate I work less than three hours per day every single book. I've written in one hour per day One hour per day consider that So these things should not discourage you, but they should inspire you So I hope these are some traits for you to reflect on in your own life And of course check out the first link in the description Which is for a journaling worksheet You can use to go from mediocre to beginning to become more exceptional in your own life And of course after you check that out and download it come check out this next related video right there and there


Today we're making one of my favorite meal prep recipes. Sheep pan, Greek chicken meatballs, and potatoes. You start by quartering one and a half pounds of baby potatoes. I like using gold. You can also use Yukon gold potatoes and just dice them to a similar size. Toss them with two tablespoons of olive oil and season with two teaspoons of Greek seasoning and a teaspoon of dried oregano. I like using cavenders. They add a bit of MSG, which is extra tasty. But you can use any Greek seasoning. There's tons of recipes out there if you even want to make your own and you have a well-stocked pantry or spice cabinet. We'll add these to a half sheet pan. Give them a good toss. Make sure you get any extra olive oil in the mixing bowl on the potatoes. We'll roast at 450 for about 20 minutes, 15 to 20 minutes while you prep the meatballs. To start the meatballs, we're going two ounces of crumbled feta, a tablespoon of minced parsley, and a tablespoon of minced mint. Another teaspoon of our Greek seasoning. Then we're going about half of a red onion or around 100 grams. Pretty finely diced. Then we're going a fourth cup or 30 grams of breadcrumbs, a a zest of one lemon, and then about a tablespoon or 15 grams of lemon juice. Mix everything together. You could break up any extra large clumps of the feta. You want these to be pretty evenly sized, just so you don't get any giant chunks in one of your meatballs. We'll add one large egg and a pound of 97.3 ground chicken. You can use ground turkey. You can use even a fattier cut of ground chicken. It doesn't really matter here. You want to mix everything together by hand. Don't over mix it. We don't want to paste. Just bring everything together. Then we'll measure out 16 50 gram meatballs. A food scale is really handy, but you can eyeball it and then kind of redistribute as needed. By the time you have your meatballs ready, your potatoes should be good to pull. We will use a spatula to scrape these to one side of the sheet pan. Be sure to get all the crispy bits stuck off the sheet pan and push these to one side and give them a good toss. These got a little toasty here. Ideally, you work a little bit faster and you're not building yourself and make your meatballs. If you notice these are too toasty, you might throw some aluminum foil over one side so they don't get too brown, especially in the back half of broiling your meatballs. Once you get these pushed on one side, we'll drizzle a little bit of olive oil. You can also use cooking spray if you want to save some fat and calories. Then we add the meatballs to the other side, evenly spaced these out. We're going to go back in the oven at about 450 for 10 to 15 minutes and then we'll finish under the broiler for the final five to 10 minutes to get some nice browning on these. You want the meatballs to be fully cooked 165 degrees internally and nice and golden brown on the top.