Hello everyone, it's an honor to be here. Thank you to the organizers. So I want to talk to you today about fueling innovation by creating, collecting, and improving stepping stones. And there's an interesting paradox in life. Let's see if this works. It's not working. That's not good. All right. There's a paradox in life, which is that if you try too hard to solve a really ambitious problem, which is to say that you only take actions that advance towards that goal, then you'll fail. However, if you ignore the objective completely, you're much more likely to succeed. So a metaphor for that is this maze here. Whereas if you imagine a little robot that starts at the bottom and it tries to reduce its distance to the goal, if it only takes actions that do that, then it will go north and it will bang its head against the wall forever, which is what this optimization algorithm in the middle does when it incentivized to reduce distance to the goal. However, if you ignore the objective and just explore the set of possibilities, then you can trivially solve this problem. So this is actually a really key and important idea behind all innovative processes out there in the world, whether or not that be science or technological process or even natural evolution. The general idea is that you cannot have one overarching goal that you myopically optimized or invest all your resources in. Instead, you have to invent your own sub-problems and your own goals as you go. And then if you're working on one problem and you suddenly invent something that is actually helpful on another problem, then you catch chance on the wing and you recognize that serendipitous moment and you switch and you do a goal switch and you start optimizing for that other problem, too. So there are many examples of this in life. So if you think about this technology for cooking food and you wanted to say, I want to cook food faster and cleaner and you only worked on things that cooked food faster, then you would never invent the modern microwave. Because in order to invent the modern microwave, you had to have been working on radar technology and then recognize that that melted a chocolate bar in your pocket and say, oh, that's interesting and start adapting that technology to cooking food. Similarly, if you start out centuries ago with the abacus and you say, that's fantastic, it does computation for me, what I'd really like is orders of magnitude more computation. Well then if you only invested in things that improve the amount of computation that an abacus provides, you might get longer rods and more beads, but you would never invent the modern computer because to invent the modern computer, you need to be working on vacuum tubes and electricity which immediately have nothing to do with increasing computation. Similarly, to go from coal-fired furnaces to clean energy in the form of nuclear power, you have to be investing in space and time in the form of Albert Einstein. And in a natural evolutionary example, if you want to go from dinosaurs to things that can fly, well you shouldn't invest in things that get you higher off the ground. Instead, you have to start researching and investing in this new fangled technology called Feathers, which initially are really good for insulation and are not at all good for getting you off the ground. So the conjecture here is that the only way to solve really hard problems is to create the problems while you're trying to solve them and then goal switch between them frequently over time. And so by the way that we can do that algorithmically is we can have our algorithms if they're starting to optimize for one objective and they suddenly recognize they've produced progress at another objective to start optimizing on that other objective too. So imagine a scientist and it wants to create a robot that can learn to walk with like a deep neural network. If that robot during training suddenly starts crawling really well or balancing on one foot really well, we shouldn't throw that out as a failure because it's not walking. Instead we should capture that serendipitous event and start optimizing for crawling and balancing too because those may be essential stepping stones to ultimately produce a walking robot. So the ultimate goal here is we want to produce what we call open-ended algorithms. These are algorithms that can endlessly innovate and I mean endlessly. I'm talking about if you give these algorithms a billion years of infinite planet-sized computation, would they continue to innovate? We have no algorithms like that today but we think it's fascinating to try to figure out how we might build them. Now we know that this can happen. It happened on earth which has been innovating for over a billion years. And if you think about it from this very simple origin of a single-celled creature, we now have jaguars and hawks and the human brain in this endless parade of engineering marvels unraveling from this system. It's also true in human culture which has been endlessly innovating since the dawn of human civilization. So the question is can we make algorithms that do this? And so one of the things that we've noticed is that there is kind of an abstract principle that underlies all of these innovation engines and that is that they start with a set of things that they've already built and they generate permutations of those things and if they're interesting then they keep them and they add them to the set and then they continue expanding outward with this ever-expanding collection of stepping stones. And so one of my goals in this talk today is I wanna sketch out a little bit more how we're trying to build algorithms that kind of capture this innovation in a bottle and produce it, which we call open-ended AI algorithms. And then I actually wanna draw some connections between those algorithms and the open-source community which I hope you'll find interesting. So the first kind of family of algorithms my colleagues and I have been building kind of pushing towards open-ended algorithms or what we call quality diversity algorithms. And the idea here is that we wanna, our algorithm to find and produce a diverse set of high-performing solutions. And a canonical example of this type of algorithm is an algorithm that I co-invented with Jean-Baptiste Moret called Mapelites. And the idea here is that you choose dimensions of interest that you're dimensions of interest that you will care about and then you discretize them. So for example, if you have a robot and you want it to find survivors after an earthquake you might also care about other things like safety and energy efficiency. So you discretize those dimensions as in this grid and then you search for the highest performing solution at every point in this grid. And what's interesting about this algorithm is it doesn't return to you one solution with an arbitrary trade-off between safety and fuel efficiency but instead it returns to you an entire surface that should illuminate the space of possibilities telling you, for example, that you can do really well in the corners of this box but not in the center. So I wanna show you this is a qualitatively different kind of algorithm. If you run traditional machine learning on a problem that has, for example, some dimensions of interest and you only optimize for performance what you get are low performing points here and you don't explore much of the search space. If you add diversity, a pressure to kind of actually explore more of the search space you do get higher performance and we've known that for decades but you still in actuality don't explore that much of the search space. Map elites, which is a quality diversity algorithm is a qualitative difference in a sea change at what happens with search. Here with the exact same amount of compute you completely fan out and understand what's possible in the search space you explore much more of it and what's really interesting is we're finding over and over again that these algorithms actually find a better max performing solution than the other algorithms whose job is solely to find the highest performing solution and that's because they do a better job of expanding out and finding what's possible in this search space. So what's really interesting is that we can observe in these laboratory experiments that goal switching is absolutely essential for success. So if you take one of the final solutions in the map the best thing that map elites ever found for one of these types of problems and you look through the kind of the history what problems were being worked out in the lineage that eventually led to that solution and what you find is that search was not working on that particular version of the problem or even nearby versions of the problem that very frequently you have these long circuitous routes through the search space that you had to be working on this problem and then that problem and switch to this problem and then work on that problem to ultimately solve one particular problem and here you can see some of those traces through time and what I propose to you is that it would be virtually impossible for a human to come up with this curriculum ahead of time but what's cool and what's powerful is that this algorithm is automatically simultaneously exploring many curricula through the space and finding the ones that ultimately work. So we actually created an algorithm called innovation engines and we challenged a whole population of little tiny neural networks to produce one picture that activates the neuron and a deep neural net that is trained to recognize coffee cups or golden retrievers or a motorcycle and what that allows us to do is compare what happens when you try to optimize for a single objective say make me a coffee cup picture or you have a thousand different objectives simultaneously and you allow goal switching between them. So in the single class case this optimization landed on this early motif it's trying to produce a water tower here doesn't look much like a water tower but it's stuck on that local optima and just forever is basically stuck on that theme and makes minor enhancements that improve the score but ultimately don't produce a very high performing picture. In contrast map elites when it has a thousand different classes and allows goal switching initially it lands on that dome or moon like thing which kind of looks like the top of a water tower but isn't very high performing but ultimately something that was doing well on the beacon class swaps in becomes a better water tower and then gets refined to ultimately look more like the water towers in this data set and became much more high performing. So we can quantify this if you have more objectives you do better over time and in fact the more objectives the merrier that as you add more objectives you get more goal switching and you get higher performance ultimately in the system. Now goal switching also enables really good ideas to spread which I love and so this is basically allows one innovation somewhere in the algorithm to spread to a whole lot of different problem types and then become the technological foundation on which further innovation is built and this happens also in nature so biologists call these adaptive radiations in one lake in Africa there might have been an innovation in the efficiency of breathing in along for example and that can spread to all the other lakes in Africa and then that solution can be customized to the particularities of those different lakes. Similarly Darwin's finches started out with one idea and then spread and got adapted and customized to each of the different niches on the different Darwin, the Galapagos Islands. And finally think about the computer that amazing original invention has radiated out into society and then adapted to so many different use cases and is the foundation upon which we all build. So we want that to happen in our algorithms and what's amazing is that we are starting to see that happen in our algorithms. So in that domain where we were generating images this initial innovation of the black dome with a red background initially with the deep neural net thought that that looked like an abaya and that original idea then radiates out through all of these different classes to ultimately become the backbone on which you get a volcano, a mosque, a water tower, a beacon, a yurt, a church, a planetarium and obelisk and a dome. So it's really amazing to see this happen inside of our search algorithms. In 2015, we had a paper in nature that showed some of the power of these ideas. What we did is ahead of time in simulation we launched a quality diversity algorithm specifically map elites and it went out and it gathered all of these different ways to walk using legs in different ways and combinations. And then when a real robot became damaged in the real world, we used an efficient algorithm Bayesian optimization to search through that set of high performing diverse solutions to find a gate that worked despite the damage on the robot and in about 30 to a minute and a half this robot is able to get up, conduct a few experiments and walk away and continue on with its mission. We also use these exact same ideas to solve this kind of long standing kind of grand challenge in artificial intelligence which was this video game Montezuma's Revenge which was one of the games on which DeepMind's original paper scored exactly zero. And many, as you can see all the blue dots here many different industrial and academic labs have been trying to make progress on this task for years now, mostly without success scores between zero and about 10,000. And then we applied these ideas and enhanced them a little bit to have the agents in this game kind of go out and find the best way to go here in the game and go here and go over here and do this activity. And that exploration strategy kind of produced a sea change that took scores from about zero to 10,000 all the way up to on average 660,000 with our top performing neural network here scoring about 18 million and beating the human world record of 1.2 million. So quality diversity algorithms are really exciting for all the reasons I've told you and the question is what's missing? And the answer is that still ultimately their ability to innovate is constrained to whatever problem it is that we're running them on. And this is actually true with almost all machine learning algorithms. So no matter how long you run OpenAI's Dota agent in Dota or DeepMind's AlphaZero algorithm in Go what you'll end up with is a really good Dota agent or really good Go playing agent. But what you won't have is a robot that can wake up and make you breakfast in the morning which is what I would want. And so we want algorithms that can do that that can kind of break out of their sandbox and continue to innovate relentlessly. And so the intriguing possibility that I want to raise with you today is that could we create algorithms that generate their own challenges and to solve them all at the same time? Just as nature kind of invented the problem of trees or sorry leaves high up in trees and the solutions in the form of caterpillars and giraffes that can eat those leaves. And so our most recent work here is the paired open-ended trailblazer or PoET algorithm out of UberAI Labs with my wonderful colleagues here. And the idea is that we want to endlessly generate increasingly complex and diverse learning environments and their solutions simultaneously. So here PoET pairs two populations a populations of environments and a populations of agents and periodically will add a new environmental challenge if it's not too difficult or too easy for the current set of agents and then we'll also optimize the agents to do better on those challenges and we'll allow goal switching between them. We have a deep neural network with a variety of sensors and the degrees of freedom in the environment are listed there. So this algorithm has to learn on its own how to walk and it starts off on easy domains such as flat ground with little tiny stumps or slightly hilly domains, a few steps, a few very, very small gaps. And over time PoET's generating harder and harder challenges for this little deep neural network to learn how to control this robot to walk on this task. And over time the environments increase in complexity. Now we're starting to see bigger gaps or taller stumps in this case but you're seeing them kind of each separate. It's like learning these skills. First I want to take on big gaps and then I want to learn how to climb big stumps and now rugged terrain. It's not yet combining those things but later the algorithm does start to combine those objectives. So now this poor little robot has to jump these big gaps and climb over really big stumps and it's using its sensors to figure out how to run and leap over chasms that are about as big as possible for its body and its motor torques. So here's another challenging environment. We didn't even think this was possible but PoET invented it itself and figured out how to solve it. So what's interesting is that if you take the final environments that PoET has invented and solved and then you directly drop a new deep neural net with optimization in that environment try to solve it, it always fails because there's no gradient or curriculum to figure out how to solve this really hard task.  So you might think that's too hard so we'll try to do kind of what's intuitive. We'll take the solutions that PoET invents and then we'll create kind of a direct path, a series of stepping stones that linearly interpolate between the original simple environment and that hard environment and what we find is that also always fails. Somewhere along the line there's some jump that it can't make because the right stepping stone to solve the first red thing in this chain is not the green thing right before it. So intuitively designing this curriculum never worked and that problem gets worse the harder the task is. So goal switching is absolutely essential for the success of this algorithm. Now I want to share with you one of my favorite anecdotes from this work. So here is an algorithm is learning how to walk and it does so in the original simple environment and if you notice it's kind of dragging its knee on the ground it's not standing up and walking for whatever reason and however PoET eventually says okay you're good enough on that environment we'll create a new environment with little tiny stumps and that knee dragging behavior now no longer works it's getting tripped up on these stumps. So in that environment it has to stand up learns to do that and go over these stumps and gets a better score but PoET is always checking for goal switching opportunities you know is the best solution on one problem a good solution for another problem so it automatically swapped that solution back into the original simple environment where now that it's standing up it gets an even higher score and with further optimization gets all the way to a score of nearly 350. Now we did the counterfactual here we took the original agent and gave it as much computation as was required to get to the score of 349 but let it run in that original environment and it never stands up and it never gets a very good score. So here think about this kind of intuitive curriculum to solve a simple problem you actually had to go work on a harder problem for a while and come back to it. So we can test PoET with and without goal switching and we find that without goal switching it never sol produces and solves these really hard challenges but with goal switching it always does. So in future work in this domain what we'd like to do is take this to much more interesting complex 3D environments such as the one that you see here but I think it's more provocative to think about this algorithm in this context as we get more computation in the coming decades what will happen with an algorithm like this when it has the ability to explore extremely complicated environments with other agents that you have to negotiate with to buy and sell goods you might have to fight you might have to cooperate you might have to climb walls evade aerial predators, et cetera, et cetera and so with a billion years of computation and environments like these these algorithms might do really wonderful things. So ultimately I think that this is a step towards what I call AI generating algorithms which might be, and I argue in this paper that's linked here are potentially the fastest path to our most ambitious goals as an AI research community which is to produce human level AI within computers and the idea is that we might need algorithms that learn as much as possible on their own as opposed to the current dominant trend of machine learning which is trying to hand design the solution piece by piece and the idea is that we could launch an algorithm that bootstraps itself from simple origins all the way up to producing human level or even super human level AI all on its own and we know this is possible because that algorithm has run on earth and has produced all of the people in this room so we can do it algorithmically we just have to figure out how and the idea is that the game plan is that we would have to simultaneously do three things we'd have to metal learn the architectures the learning algorithms and automatically generate their learning the environments, the learning challenges which is what I focused on in my talk today so to conclude kind of the academic portion of this talk automatically generating environments and solutions I think is a really really powerful ingredient to try to propel forward innovation engines they automatically invent their own curriculum they create, collect and improve stepping stones they harness goal switching and adaptive radiations they also hedge their bets by simultaneously trying multiple overlapping curricula and finding the one that ultimately works and I think that these are keys to algorithms that endlessly innovate including evolution and human culture in the form of science, technology and art and therefore they may be the only way to solve really ambitious problems that we have in society and in research and to discover the full gamut of what's possible out there in a search space so if you're interested in more on these subjects we just put a tutorial online for ICML in 2019 on population based methods so now I promised you I would draw some connections between this line of research and the open source community and what I think is really interesting if you think about it is that the open source community acts a lot like a quality diversity algorithm if you think about what people here do they create, they collect and curate and they improve stepping stones which are these different packages and repositories so in the sense you and the people in this room and the people in this foundation are doing exactly what is needed to catalyze progress in society so to the individual contributors in this room I would say please create, creating, collecting and improving your projects and to the people that are community organizers keep enabling those wonderful activities so all of the code, I want to mention we're doing our part all of the code for all of the algorithms I've introduced are online and available and also there are more open source projects at Uber we're really proud of our open source activities there so my final thoughts for this room in the open source community are this and this is kind of what I've learned is working on these algorithms if you find it interesting even if you don't know why you find it interesting or you don't know what it's possible use is in the world put it out there go out there and build it because you never know we're working on that idea that project will take you you may go switch from that to something else and have a serendipitous discovery similarly I would say share it because the more stepping stones the merrier you never know how your idea or your repository or your code or your hardware will be used by someone else you just can't anticipate it so put it out there into the world and something wonderful might happen I'd also say just release it clean code is great but please don't let perfect be the enemy of the good I've seen time after time people say my code's not good enough to release and they deny civilization the opportunity to benefit from that code and that innovation so just put it out there if you don't have time to clean it up so I would say that the least helpful repository out there is the one that doesn't exist so finally I just want to say keep doing what you're doing which is providing the rocket fuel for the open-ended algorithm that is technological progress thank you


Hey, Bolo Buddies, thanks for watching. All right, in this video, we're gonna talk about my big money Bolo's items that I sold on eBay for a big profit. Hey, Bolo Buddies, thanks for watching. On my little pony. Amazing, right? All right, let's get started. I'm gonna tell you where I got it, what I paid for it and what it sold for it. This video is gonna be a little bit different. I'm gonna start the video out with a little bit of footage from the Goodwill Bins of when I found one of my biggest money Bolo's ever. I am gonna link this video down below if you wanna see the entire footage. As I shop at the Goodwill Bins, I pop up screen shares of how I listed everything. Well, this particular day, oh my goodness, wait until you see what I found. These things right here were some sort of like Karmat. So I took those over to Dad Planet and he looked that up for me because I don't know anything about it, but here it is, you guys. Here is my thousand dollar item. You're like, what? It's an envelope. Yes, it's an envelope. And we'll come back to that in just a minute. I actually found four envelopes. Everybody else passed them by. Here's Dad Planet. He's looking up those Karmats for me. They didn't really comp out very well for as heavy as they were, so I decided to leave those behind. But I figured he'd be the person to research it. I found this beautiful necklace. It's sterling silver in the original packaging. Look at this, all right, you see what that says? Stinky stickers. If you ever find stinky stickers, oh my goodness, cha-ching, okay? These are scratch and sniff, you guys. Those are all scratch and sniff. I found hundreds. These are the ones that have already sold. Look at these prices. These are all the ones that are currently listed. All right, so I just sold another one. And if you go over to that video, you can see some of the other ones that I sold. I just sold these vintage scratch and sniff stickers. There's eight stickers here. I sold these for a best offer of $60 plus shipping. Incredible, right? I sold another one of these Wendell August plate covers. If you've been watching me for a while, you have seen me sell these over and over and over again. Some of them are singles. Some of them are outlets. They have all been big money bolos. Some of them are dogwood print. Some of them are pewter. Some of them are bronze. Just if you see this brand, look it up. He makes other things as well. I got this at a garage sale for $1, sold this one piece right here for a best offer of $39 plus shipping. I will say that the same buyer keeps coming back. So is there a huge demand? I don't know. People aren't like, I'm not getting tons of different people buying them, but this one person wants them. So I don't know if he's buying them and reselling them or what's going on, but I'm okay with it. I've got some listed still. He can come buy them all. All right, the next one here are these vintage Barbies. As you can see, this is a parts and repair lot. They are all, they're a mess. I mean, look at their legs. They need cleaned up.  This one looks like she's had a haircut. So yeah, definitely be on the lookout even if things are not in the best condition. So these came from a lot that I picked up from Donna Telebotelino, but I didn't actually pick it up. There was a live auction going on on her YouTube channel. You guys have probably heard this story a hundred times if you watch me on a regular basis because I have sold so many items from this box, but I placed a bid on it and then I stopped bidding and Noelle Farm Girl Scavenger started bidding and she bid this box up big time and sent it to me as a gift and I listed everything and I have a video on it. So I will link that video down below. Definitely check out Donna Telebotelino and Noelle Farm Girl Scavenger. I will have their links down below as well. But I sold this Barbie lot for $65 plus shipping. The next item, you're gonna see this in one of my featured Facebook members share videos because I shared it there as well. I got these at a garage sale for $15. It's Scholastic 45 Books, it's Puppy Patrol, King Street Kennels Playset and Dogs by Jenny Dale. Now these dogs are probably what helped me get more money for this. It came with this, it opens up. Let me show you some more pictures here. How cool is this plus 45 bucks? Books, paid $15, took a best offer of $175 for these books and the little play set. So definitely a Bolo to be on the lookout for. I think it's pretty rare and hard to find, but at least if you see it, you will know to pick it up. Are the books by themselves a Bolo? I'm not sure, but if I saw them and I can get any book sets in a series like that, I will pick them up. The next item is this lot of toys.  But I will say in the future, I think I'm going to have toy shows. I know I'm going to. I'm actually working on getting one together now. I'm gonna have a what not toy show and I'm gonna do small lots of toys. And I think it's gonna be super fun. So if you guys are not following me on what not, there is a link down below. I am Bolo buddies, all one word lowercase over there. If you use my link to join what not down below, you will get $15 to shop. That is a referral link and you get free money. So go check it out, click on that link, get your $15, come hang out. Even if you don't buy anything from me on what not, come check it out. You can use that $15 with anyone, but I would love to see you guys in the chat. So many of you have already come over and hung out or supported by buying something. I appreciate both of both buyers and watchers. It greatly appreciated. So I hope to see you guys at a future show. This was actually purchased by one of my Bolo buddies, members of the YouTube channel, Two Sisters Picker. Two sisters picker. No, two sister pickers. I put the S in the wrong place. Anyway, if you go over and save the seller, you'll get notifications when they list new items. She is also known as She Picker on YouTube. So I will link her down below. Again, she's a Bolo buddies YouTube member. Thank you so much for being a part of memberships. And these are just some of the items in her store. So go and check it out below. And thank you for your purchase. Remember the PEZ dispenser, everybody. Oh my goodness, the PEZ dispenser that I lost. All right, so here's the story on this. I listed this PEZ dispenser and I believe it was auction style. I can't remember the whole story, but I went to try to find it and I could not find it. It was broken for parts. And if it would have been fixed and in good condition, I probably could have sold this for, I forget what it was, I did a video on it, maybe 100, 150, the comps were really good, but I didn't do my research. I listed it on auction, it was broken, kinda had a little bit of seller's remorse, and I did a video on it. But I would definitely honor the sale always. If I sell something and it sells, I'm gonna sell it. The it's going to that buyer. Well, it turns out I could not find the item. And I'm sure a few of you thought that maybe I was fibbing, but I wasn't. I looked all over for this thing. I probably spent hours looking for this thing. Drove me crazy. Well, I did a short on this. I was going through my plush one day and it was sitting in the bottom of a plush tote. So I must have somehow got it mixed into that tote. When I sold this item originally, I told the buyer, I am so sorry, I cannot locate this item, but as soon as I do, I will get in touch with you and I will honor the sale price, the $52. So when I found it, I reached out to the buyer and I told them, do you still want the item? I found it, I finally found it, I was so happy. And she said, let me double check and make sure that I did not already get it. She did not have it. So I honored the price at $52. You see here it says 300 or best offer. The only reason I did that is because I did not want somebody to go and buy it before she could get to the listing. So I priced it ridiculously high and she made a best offer of $52 and I accepted. That is what I sold it for to her the first time. Then I couldn't find it. So yes, I did honor the original sales price. So for all of you doubters out there, I found it and I honored the original price. So could I have had my husband fix this and maybe gotten more? Yes, probably so. But if you ever see these pes with no feet, definitely do your research because some of them go for big money. The next item is this animal adventure Dalmatian stuffed animal. I can't remember if this came out of the free plush that I got from a viewer or where I got this. I didn't mark it down for some reason but I ended up selling this for a best offer of $50 plus shipping because it was so big the buyer was all in for $8603. This is the time capsule that I talked about in my recent this is my Bolo video. If you guys have not seen that, go check it out.  So you get to see other people talk about their Bolo's. Super fun. Check it out. You're gonna be amazed by what people find and sell for profits. This is a vintage 2000 Chuck E Cheese Millennium time capsule souvenir prize. Really hard to find. And it's not anything super, super special. And I sold this for a, actually, let's see. What did I sell this for? No, I sold this for my sale price of $55.25 plus shipping and I still have one left. I got this at a garage sale for a quarter. It was free. It was sitting outside for free being it's a vintage seven moon and troll books. I had no idea. They just looked vintage and they were in a box set. So I just grabbed it and it sold, sold really quick. This is a Maddie Mod Barbie clone doll and I got her at a garage sale for $1.00. Sold her for a best offer of 55 plus shipping. I had her priced higher. I thought she was gonna go for more but I just wanted to sell her. It was one of those slow days and somebody offered me 55. So I just went ahead and took it. Could I have held out for more quite possibly and maybe not. So maybe 55 was the going rate and I just had it too high. This is a Nintendo Wii console and it's a bundle. I picked this up at a thrift store for 20 bucks. I did not hook this up to a TV and test it but I did show that the light came on and it was a pretty big bundle. I thought it was a really great deal and I ended up selling this for my asking price of $90 to a viewer pinky secondhand store and she is going to resell this. So I'm guessing she's probably gonna part it out. So definitely check her out and this is her store and you can save her as a seller by clicking on the heart. This is a Lion King action figure or cake topper. Cake topper is a great thing to put in the title if the items will go on top of a cake and people look for these but I sold these for $40 and 30 cents plus shipping and I got these at the Goodwill Bins. Now, if you ever buy anything on eBay or any other platform and you're going to put it on a cake definitely watch the items first because these are just toys. This is a vintage Snuggle Bear plush from 1986, Lever Brothers fabric softener. It's got the original tag on it. I got this at a thrift store for 50 cents. It sold really fast for 42, 25 plus shipping. The next item I ended up taking a best offer on I do have footage in a video of when I picked these up. Another one that I got the same day sold for big bucks just a big money bolo. This one I ended up taking a best offer of $75 plus shipping. She had a little chip on her nose and she wasn't, I wasn't getting the attention that I thought I was gonna get on her. So I just went ahead and took the 75 bucks and I paid a dollar for her to garage sale. So I was thrilled with that, but look at her cute little shoes and a dollar into 75, I'll take that all day. This is DMC embroidery floss. I have so much of this that I still need to list but this person bought four of these and one of these, a total of five items for $50 plus shipping. I still have 19 of these left. So if anybody is looking for red three, two, one, I've got you, I've got you covered here. And the next item was this lot. This was actually an international buyer and same thing I priced it high so nobody else would come in and snag it and told them to make me a best offer of the $153 that she had offered me. So originally what had happened is she had bought each one of these individually and it was through the international shipping program. So there was no way for me to combine shipping. So her shipping costs were crazy because it was what nine different listings and she was getting charged shipping on all of those. And it was the global or I'm sorry, it's called used to be global shipping but now it's the eBay international shipping. So there was no way for me to bundle it. So what we did is we canceled all of them and I created one listing for her at the total of 153 plus shipping. And again, this went internationally. I can't remember which country. This next item is a brooch. I believe I picked this up for 50 cents. I don't remember where I got it. It was signed pole Chini. I think that's how you say it and it's just a deco lady profile pin. It's a brooch. I ended up taking a best offer of $55 plus shipping on this. All right, you guys, what do you think about those big money bolos? Thank you so much for being here. Thank you for watching.  If you haven't subscribed already, I would love for you to subscribe. Let me know how you found the channel and thanks for watching.


Siren Head and the Long Horse creatures from the Trevor Henderson lore are actually way older than you think they are. Let me explain. It's common knowledge that the Long Horse, the Cartoon Cat, and Siren Head have been around for a long time. And not just on the internet, but within the universe of Trevor Henderson's creatures. But recently, cave paintings have been found that seem to depict both Siren Head and the Long Horse. Well, they don't seem to, they literally just do. Anyways, if you know anything about history, you know that cave paintings are a thousand plus years old at the youngest. Why on this good earth did the ancient peoples know about Siren Head and the Long Horse? In the paintings themselves, the Long Horse seems to be surrounding and protecting people, almost as if it's a guardian or something. Which fits its description of it being a very benevolent kind entity. But Siren Head seems to be literally attacking the people and they're throwing spears at it. Which fits its description too. Leave a like if you're on Team Long Horse and subscribe if you're on the Team Siren Head. Bye!


Okay today we are going to be converting XML into JSON. Why? I just like JSON better. I think it's cleaner looking. I think it's easier to parse through. I mean, XML is great in fact that it's just plain text. So if I needed to, I could always just grep through a file, maybe use cut and awk and set and stuff to pull information out of it. But I'm very used to working with JSON format. So it'd be great if I convert XML to JSON. And I'm going to show you how to do that with a program called JC, which a viewer of mine recommended to me. I wish I looked up his name before I started recording this. But it is an amazing tool. It's in the current repositories for newer Debian systems. If you have an older one, you might have to go to GitHub or I think it's in the Python package manager. But on my version of Debian, it is in repositories. So the pseudo app install JC and it will convert not just XML, but all these different file formats and outputs of different commands. We'll look at that more in future videos. But today, today we're just going to take some XML. So I've been doing videos on my arcade in the other room, which is running emulation station. And almost all the configuration files for that are XML. I pulled a file off that XML file that has all the information for all my Nintendo games, you know, the art, the description, ratings, and the names of the games. Well, I'm going to use that to as an example, we're going to use JC to convert it and then JQ to parse through it. Let's go ahead and jump right in. So again, the name of the file is game list.xml. So I'm going to run that and you can see, yeah, it's XML. Let's go ahead and just pipe that into again JC. And again, if you don't have JC, try pseudo app install JC. I already have it installed. If it's not there, look at their GitHub page. I'm pretty sure it was up on GitHub. And anyway, we're going to run that into JC. So we're going to output it, but we have to tell it what type of file it already is. We're going to say dash dash XML. And again, it supports ridiculous amount of formats that you wouldn't even believe. But I'll go over that in future videos and it will convert that XML into nice JSON here. It's not pretty. So if we want to make it prettier, we can just pipe that into JQ. Again, that should be in repositories. I've done many videos on that. It's a great, great tool for parsing through JSON. And since now is JSON, we've got it pretty. If I was to just quickly run that same command and say, head, we can see the top of this file. And we can see that we have game list game, which is an array. And then we can go for the file name if we want to know the game name. So let's go ahead and run that command again. But we're going to say dot and I'm just going to copy and paste game list game. And again, that's an array. So I got to put the brackets there dot name. And that will give us a list of all Nintendo games I have. Now it's in quotations. If I didn't want quotations, I could always run that same command with the dash R option. And that will just give us the raw data. And there you go. I have a list of all my games. So this is great XML. That was just an example. But again, we're going to look at this JC command more in the future. It does a lot, a lot of amazing things that allow you to convert all these different things to JSON. I'm very excited about it. I hope you are too. I thank you for watching. Please visit filmsbychrist.com. That's Chris for the K. There's a link in the description, support section there, Patreon, LibrePay, PayPal, can't do those things. Like, share, subscribe and comment. I thank you for watching. And as always, I hope that you have a great day.


Anyway, it's my honor now to introduce Kathy Kelly, who I've known for many years. She's a peace activist whose efforts have sometimes led to her living in war zones. Are we got Kathy there somewhere. Okay. Anyway, oh there she is hi Kathy. War zones and prisons, believing where you stand determines what you see. Kathy and her companions have lived in war zones, alongside ordinary people in Iraq, Afghanistan, Gaza, Lebanon, Bosnia, and Nicaragua. During the Cold War, she was arrested dozens of times for resisting US intervention in Central America was sentenced to one year in prison for nuclear disarmament action. She planted corn on a nuclear missile south site and became a lifelong war tax I was with Kathy one year at the Fort Benning at the School of America's protest, and Kathy went through the fence down to Fort Benning. And I remember, and Kathy's not a big person. And they roughed her up really bad with the soldiers, please rough for program Kathy asked them why are you, why are you doing this to me I'm not resisting. And then she went to jail but when Kathy goes to jail, she organizes. So anyway, I love you Kathy it's my honor to introduce Kathy Kelly everybody. Hello and thank you. Thank you for that introduction thank you to everybody who has participated what a important crucial time it is to be receivers of this education and of course to be active. You know, Frank had written and said please say something about Yemen and so I want to start by saying that I was very surprised to see that the Saudi kingdom, the kingdom of Saudi Arabia has hired consulting companies, well beyond Washington DC and New York, and places like Maine, and North Carolina, Des Moines, Iowa, and their strategy is to improve the image of Crown Prince Mohammed bin Salman, who has gone on Saudi TV to say that the long hideous war in Yemen is actually in their favor. They want to improve his image amongst people whom they're pretty sure really never heard of Yemen in the first place, and who wouldn't really be troubled by the murder of Jamal Khashoggi. Well, I think that's a signal to us of how important the work you're doing today is to be able to go out to the heartland and make sure people do know where Yemen is and what's happening in Yemen. And to people in Yemen are no threat whatsoever to people in the United States. Earlier, Daniel Ellsberg had talked about how the annual subsidization of aerospace industry goes on in the United States and greatly benefits companies like Raytheon he mentioned that Raytheon now will have the contract to install nuclear weapons underground intercontinental ballistic nuclear weapons and these will bring enormous enormous rewards financially to the Raytheon Corporation and other subsidiary groups, and Ellsberg just kind of begged people don't let this happen we should be organizing regionally to try and prevent it. And, as Frank mentioned, I will my first lengthy imprisonment will not likely compare to the hideous long sentences that are handed out to so many people but I did spend a year in prison for planting corn on top of nuclear missile silo sites. And I think it is crucial for us to resist this ground based defense deterrent strategy that is completely dependent on people believing that we should be afraid of Russia we should be afraid of China we have to enter into a new Cold War. And of course, what we actually so much need when the greatest terror we all face is the threat of what we're doing to our own environment. What we need is an enhanced capacity to cooperate to collaborate with Russia and with China to be learning their languages to be better understanding cultures to see what kinds of problems they mainly face to see how scientifically, we can join our resources and our communities and particularly not only facing climate catastrophe but also a time of pandemic and with rising numbers of people fleeing from the various wars that we've started so. And there's a, a tale that I think points us in a such a different direction I want to speak briefly about people in a village in Yemen called. And these were people who were poor, rural farmers, herders, and they were in great troubles because they were running out of water, their flux were thirsting to death, and they couldn't irrigate their crops and so they did something that was risky and they were so very needed, or they all might not survive. They pulled their resources, and they collectively hired a rig so that they could dig and dig down deeper into the earth and hope to hit water. They weren't successful at first and it got to be very very agonizing they thought well we've you know given money we didn't have our borrowed money now we're going to find out that this was a wasted project or maybe we've been in an unfair group anyway, one night finally they hit water, and it was like eureka they had a wonderful celebration people danced and they sang into the wee hours of the morning. And as they were sort of heading back to their homes. And I heard that the Saudis were in the habit of bombing wells in Yemen. But they thought well we're so removed from any places, strategic or crucial nobody's going to waste a bomb on us but they were wrong. Coming from Arizona, a Raytheon manufactured bomb was dropped by a Saudi pilot a dangled in the air on a fuse and then when that fuses cut three thin sprout out the bomb comes to life and that bomb hurdle down to exactly where those people were celebrating having collectively found water had been heading back home and immediately when the bombs nose count hits the ground. Then that releases two tons of explosives the shards of the bomb travel eight times the speed of light. If such a shard were to hit your body you could be disemboweled you could be decapitated. Your limbs could be cut off from your torso. And this is what happened to the revelers who are celebrating having hit water. Jeffrey Stern us with people.  And Jeffrey Stern felt the presence of metal and the man's cheekbone in his forehead. And he said it was an amazing thing to travel from Arizona to point us collectively toward working constantly to put an end to war to wean ourselves out of the military industrial Congressional complex, and to find ways to deal with the real terrorists we face the terror of what we're doing to our own environment. Thank you. Thank you Kathy so much. Okay, we're someone's freezing sometimes.


Specialists, read here to demonstrate how you're going to get out. It's a three-step drop. So when we land, we'll open the doors, one, two, three, drop, and then we'll take off after everybody's out. So we come out from Amari Air Base to train up some of the Estonian Defense League soldiers that we're going to be doing an exercise with on Friday. So we're training them basically how to get in and out of the aircraft safely and correctly and tactically for their training on Friday.  We don't want it getting stuck to the aircraft and we'll take off with it or end up somewhere in the field somewhere. Put the rifle up. Yes. Since you got the... But we're focusing concerning the machine guns. Machine guns? Yeah. We're going to be in the field. If you're going to be in the field, we're going to be in the field. It's good. It's good. It's good. It's good. It's good.