Good morning. Good morning. Good morning. Hope you're doing super well today. I just wanted to come to you with a video that's non real estate related. I try to keep all the content on this channel and all my other channels related around how to help you sell real estate, become a better real estate agent, become a better human being, learn how to communicate better, work harder, do the things that you need to do to succeed to provide the best life for you and your family. But today I want to get a little personal, actually really personal, something I don't talk about a whole lot. I have mentioned it a couple times here and there. I made one video about it a long time ago and I've done several posts about my battle with drugs and alcohol that I had at one point in my life. Today's podcast, if you guys are listening to the podcast, you can go listen to today's episode where I get into a lot of the more details and more of the story behind my struggles, what I went through, and how I overcame those struggles. But I wanted to make today's video not about how many views I get, how many likes I get, so on and so forth. I just wanted to maybe share just a little piece of my story and hopefully if you're going through some kind of struggle, regardless of what kind of struggle it is, hopefully this can help you in some kind of way. And I will say this is a pretty, I don't know how to, there's not really a word for it. This is definitely a moment in time for me that I don't really know how to describe it. There's not really a word for it. But here you go. My grandmother is literally taking her last breath right now. For those of you who have been following me for a long time, for maybe the past four years, me and my wife, Carlin, have taken her dinner every Wednesday, spent time with her amongst many other things that we've done to help her try to be comfortable. You know, I thought she lived a lot longer than this to tell you the truth. But you know, when it's your time, it's your time. She lived a great life. She's going out the way that she wants to surrounded by family and her comfort of her own home. She opted out from going to the hospital and she doesn't have COVID, you know, so all those things are incredibly positive. She lived a great life. She had six children, you know, and has an incredible legacy with her grandchildren, which I'm one of them and great grandchildren. You know, so incredible life there. At the same time, my daughter is almost nine months old. So we're experiencing life and death like we're at the end of my grandmother's life and the beginning of my daughter's life. So it really makes you sit back and reflect about a lot of things, you know, and as far as my journey goes. So anyway, I hope this helps you guys a lot. I could tell you story after story after story of so many crazy things in terms of, you know, my partying days and, you know, everything that went down and I'm really lucky to be alive, extremely lucky to be not have gotten into a car wreck and died, not to have overdosed, so many different stories. You know, I remember one time I stayed up all night long doing cocaine and stayed up all night long and then ran a 5k, won the 5k. I literally won a 5k after staying up all night long on cocaine. Stuff like that, just crazy stories.  And I'm proud of who I am. If I had it all to take back again, I don't believe I would, you know, I could sit here and tell you I wish I never did any drugs and alcohol, absolutely. You know, I believe that down to my core, however, you know, that was the course that I was given and, you know, I'm not ashamed of it at all. That's why I'm able to talk about it freely with you and if you or anyone you know ever needs to talk about this, I'm more than willing to share, to listen, to talk, so feel free to reach out anytime.   The year that I quit was April Fool's Day, 2014. I almost died the night before and I said, if I ever, if I did wake up the next day, I would never do anything ever again, never take another sip, never do another drug, nothing. And I've held true to that. That was the first year that I sold 100 properties, the year that I quit and I've sold 100 properties every year since. And if it wasn't for me quitting drugs and alcohol, I would not have made a YouTube channel, I would not have, you know, been a public speaker, I would not have written books, I wouldn't have been able to, I would have been too gone, you know, so I'm super grateful for the experience and everything I went through to bring me to the point of quitting. I just thank God that I didn't, I didn't die through that process, but it's made me who I am and I'm proud of who I am.  And if anything, I hope I can be that, that shining star, that, that north star to kind of lead you, lead you in the right direction.  And as always, reach out anytime there's anything in the world I can do for you guys, much love. Hey, I lay in this thing and learn in the


Yeah, so my name is Chen and I am a software engineer at Meta. So today I would like to introduce AxiTorch, a native solution provided by PyTorch to enable and accelerate the PyTorch model on edge devices. We will be covering the following four items today. So why on device AI is important? Why it's challenging? And what is AxiTorch value proposition? And on a high level introduce the stack. So first, what is on device? It can be on laptop, AR-MR devices, AR glasses, embedded devices, mobile devices, all these are edge devices. One new popular edge devices probably wearables I'm wearing every day. So the advantage of running on device is very straightforward. The privacy and security, performance and energy saving, and we will be covering that in a bit. So first, privacy and security. So for any model running on the cloud, it means I will need to send my data to the cloud and then the model will run inference and then send the data back. So if we can run a model on device, user won't need to share the data to the cloud. For example, I am wearing my watch every day and I use it for running and exercise. My watch can monitor my heartbeats, my cadence, and I rely to be my own coach. In this case, I probably don't want to share my daily routine and other sensitive biometric information. Also it enables training my own personalized model with my own data, without worrying sharing the sensitive data to the cloud. Performance. Running model in the cloud just means everyone has to go through the round trip traffic from device to the cloud and then from cloud to the device. The time spent on the traffic is not avoidable. It also affect user experience. Say for example, I am in a rural place and the network is terrible, then the user experience probably won't be great. Lastly, energy saving. So if we can run the inference on device, the cloud won't need to provision resources. The cost to run one inference will transfer from the cloud to the device. Now for the use case, we can, I guess like, these are like example use case. So for example, the vision model. So some of the most common models include like say segmented image, object detection, classification, and then yeah augmented reality. And in this way usually the input is an image and the output can be different based on the models. Speech model. Automatic speech recognition is one of the like very common speech model which convert audio to text. And then there's some other example, say for example, noise suppression that is very often used in the video card such that we can do the noise, we can do the noise suppression from the background. And then language model. Usually it just takes text and common model include like assistant, intent understanding. The popular large language model we use very often nowadays is also a typical language model. Genitive AI is definitely a hot topic today and yes, we are working on it. Lama 2 is one of our focus and we are also look into other models like EMU for image generation. The Genitive AI use cases is sort of overlapped with the other three but we listed here especially because the uniqueness for Genitive AI usually it's more like a large model and also like compute heavy. For meta, why do we care about on-device AI? One important reason is the family of apps. So like Facebook, Instagram, most users are on mobile devices and they love to create and share experience while AI affects. So for me I like using different filters and in real video cars. Say for example, filtering out the backgrounds, noise suppression, all these are super handy for users. And then for reality labs, mostly for AR, VR. On-device AI is also crucial to enable many applications. Like say it makes reality, we may interact with the visual object in real world and also we have assistant, it's always available for the smart glasses such that I can say use my voice to take a picture or make a call. So here's the question mark. From a PyTorch program where everything happens in Python, what are the options to enable running on-device? We're like say, we're like in the resource limited device only C++ is available. How do we like, what is some options here? This diagram is for the current machine learning landscape. So this is somewhat PyTorch-centric view but if you see the path to accelerating PyTorch model on-device AI, it has to go through, it has to cross multiple boundaries. A trained PyTorch model is often converted to another IR. As intermediate representation, leaving the PyTorch ecosystem from there or directly from the PyTorch model, it enters yet another of vendor specific arena. Then it will be convert this model or IR to the specific IR, the two chains can understand. And then at this step, we generate artifacts that can deploy on-device. This is the current landscape because AI on-edge is characterized by diversity of hardware, diversity of operating system or in some embedded device, there's even no operating system there. And diversity of chipsets, proprietary IPs used by OEMs, platform specific proprietary two chains. Yeah. So it is how it will impact developer. The resulting production workflow involves lowering scripts. Lowering here means like generating platform specific artifacts using the targeted two chains. So such lowering that cross boundary from one domain to another likely will introduce loss of information because each domain has its own language. And the translation from one language to the other language is not perfect. Thus by the time the model is deployed, it is no longer a PyTorch model. And we cannot reason about where the parts of the deploy model come from in relation to the original PyTorch module. To maintain the version of tools is also just not trivial. Like imagine one of the conversion tools just get upgraded. Then the workflow chain here likely is just broken and it's just hard. Now you will say, sure there's a loss of information, but I am able to get an awesome acceleration of my model. So I give that you got performance and maybe some device reach as you deploy some of your models on different devices using different two chains. I say some because these two chains are all or nothing. So if I have a model that is not lowerable entirely, then lowering just fails. It also has a cost because we lost the probability. The production code needs to be aware of the target device as the model loading and running APIs become platform specific. We lose the productivity because the deployment engineers need to use 10 different tool chains and every may result in its own error code. And the round chip from all the way down to the beginning like can be really time consuming. And also we lose like the visibility in terms of profiling and debugging. It is like say for example, if some part of the model is really slow, it is now really painful to figure like where the app is and what is the original pytorch line. Then also like what we lose is the coverage because all or nothing, the nature of the lowering process means it's not composable. Say for example, if I cannot lower part of my model and I guess like then it's pretty much just like we cannot use this hardware to round this model. So now we know the problem. Machine learning engineers love authoring models in pytorch. However, on device deployment requires developers leave pytorch ecosystem by various of converters and platform specific tool chains. And our solution is access torch. It is integrated with partners and platforms with the pytorch ecosystem. By providing a set of integration points, we will, which we will talk in a bit, we want vendors and OEMs that provide on device machine learning acceleration and tool chains become our partners and be part of pytorch ecosystem. The value this bring to users, pytorch users is performance. Now we have for the ability because the API, the same APIs can be used for model loading and running regardless of the hardware it is accelerated on. Why? Because integration to the access torch stack means the user facing API is the same. It also saves, it also saves the pain point of productivity because the standardized upsets and minimum loss of, it also have like minimum loss in translation. Furthermore, being in the part of the ecosystem improves the performance and functional debuggability and we'll cover that in a bit as well. We also improve the model coverage because the composable nature of the solution means say for example, I have a model and part of the model is not supported by the device, the rest of the stack can support that. So here I would like to introduce in access torch from the pytorch program, how do we enable it running on device? So we start with the original pytorch program, we use export an API that is introduced in pytorch 2.0. We export a pytorch core program to a graph representation. Access torch enables transformation and optimization entry points for the target hardware and then it compiles down to the access torch program, which contains instruction that the runtime is able to execute. The access torch runtime, which is installed on the edge devices is able to load the access torch instruction and then do inference. So the runtime pretty much is just passively executed instruction. So let's zoom in a little bit. Let's first take a look at the pytorch program. So this is a snapshot about the pytorch program. Usually a typical pytorch program is touched on the module and usually it's composed of multiple different modules, including a set of pytorch operators. By leveraging touch.export, which is introduced in pytorch 2.0, we are able to capture the graph. The graph is represented as export IR and as we can see here, each node represents the operator. With the graph it's much easier to work on. So if we look at the example code, we can see like there's two convolution and convolution then run a relu and then follow by next convolution and another relu. And then if we look at the graph, it's exactly like convolution followed by relu, convolution followed by relu and then output. So the graph is like with the graph, we are able to recognize what operators is used in the pytorch program. Now the pytorch 2.0 capture mechanism, which we use here is the touch.export, basically can generate a sound graph. Export that is concise yet can capture wide range of dynamism. It will generate standardized core 18 operators and will ensure the consistency between the authoring and deployment. And then here is the compile process. User can pick and choose and customize their optimization through a set of well-defined APIs. Let's zoom in a little bit and then see what are the customizable options. So first, a very common used API is quantization. Quantization is a very typical way to compress the model size and it's also like a very critical part for compressing large model such that we can run it on device. And then the next common and also like, yeah, a common API that user will use is delegate. Delegate means like delegate to accelerator. We can either delegate part of the graph or some of the graph to the hardware or software that is a more performant. Say for example, if we have a library, we know XMPAC is a library and it's a highly performant library. And say for example, in a graph, we have a linear operator and we know XMPAC can run linear away faster than we can delegate the linear operator to the XMPAC. There are some other powerful backends available on device like GPU and other accelerators and pretty much we can leverage these backends by delegation. Lastly, memory planning. So during the inference, there could be like many intermediate tensors that are, we can plan memory ahead of time to save the compute. Say for example, if we add two tensor and then the result of the add will be fit to a next matrix multiply operator and then the intermediate tensor actually can be free and then be reused by other compute. So with all these entry points, developer can pick and choose based on their use case and their requirements as well as the target hardware architecture. By leveraging exit torch SDK, so we provide native support SDK as well, we can provide a tight feedback loop including profiling and benchmarking. Like say we observe some, like say for example, linear is super slow for some reason on device and we notice it's the bottleneck. Then by leveraging the SDK, we can recognize it and further optimizing it. So now we finished ahead of time preparation stage and generate the model. We are now leaving Python and then enter the exit torch runtime in C++. So our exit torch runtime is very lean. The goal for the exit torch runtime is portable and lightweight. The target of it is to run in any platform, mobile embedded device, microcontroller. It's going to be embedded friendly C++ so which means there will be no dynamic memory allocation in hip, all of them is static allocate and minimum dependency on C++ standard library. No assumption of OS or file system, very small runtime size, link only selected kernels that is used by the model, core 18 compliant reference kernels available such that it will be consistent with the original PyTorch model. And then it can link against third party kernels or delegates as they would like. So with exit torch, we also provide native SDK support. For example, I'm taking this one as example, if there's a runtime failure, say for example, in this instruction, we know the node 73 fails. Then by, so the 73 here we call it debug handle. By using the debug handle, we can trace it back to the exported graph, which is the top node that is also 73. Then we can trace back to the exact Python line. We also provide a list of other SDK tools aiming to provide a smooth developer experience. It mainly to focus on our mainly focused pillars, including visualization, profiling, and debugging. We aim to provide easy to use Python APIs to parse profiling and debugging results together in sites. So where we are now, we announced exit torch MVP release in October this year during PyTorch conference. It's already available in open source. For users, feel free to try it out and file issues or bugs if there's any. We will respond as quickly as possible. For contributors, it's not open yet to offer a list except strategic partners developing delegates for exit torch. So if you have your own hardware, if you have your own compiler, you're welcome to reach out or just follow the guideline in the tutorial. We plan to do after release on April, 2024, targeting on delegate enhancement with your SDK and backward forward, compatibility contract. We also plan to do a beta release on October, 2024 with strong out-of-box performance, committed involvement, and ensure full A10 compliance. So how do we get started? Check out our website and the GitHub Rapples. We provide a list of examples to show how to deploy the models across different platforms in tutorials. And we will love to hear the feedback and use cases. Yeah, thank you. And I'm happy to take questions if there's any. A minimum resource required. So currently like our runtime is targeting within less than 50K. And then, but like, I guess like for any models, say for example, if I want to run mobile nets and then we will need some kernels. So when I say 50K, it means the runtime size, and then we will link the kernels that is needed to run, say, mobile nets. And then those kernels will be additional resource that's needed there. So essentially like I guess, if I just take an example to run mobile nets on device, it will be like the runtime size plus the model size, and then some kernel size that is needed to run the model. Does it answer your question?  Now if my model is running, and it has to give like a real time data crunching, so how do you balance the performance versus the battery performance? Oh, that's a very good question. So I think on nowadays, like mostly like a very, usually like the model is running like a pipeline. So usually like say, usually we will hear some, like usually we will have some trigger word. Like the model to recognize the trigger word is very lightweight. And the heavy model will be only enabled like once the trigger model detects the keyword. Say for example, like say hate portal, like it is a common like I guess trigger word. And then a smaller lightweight model will try to recognize the hate portal keyword. And then the next model, like the heavy model will only run one after the trigger model is like detect the keyword and then run afterwards. So that's how we sort of like balance out, like sort of balance the performance and the battery. Does it? So you mean like with the large model running on the device versus the large model is just not running. I think likely like having the model definitely like will add a lot of compute to the. Yeah, because what would be the battery consumption in that case where I just want a lot of information that is like model is continuously running and hard in the CPU, so. I see. Have you taken those things also into consideration in benchmarking? Oh yeah, that's a really good question. I actually not aware of the announcement. Maybe I can check it out later. Oh, okay, okay. Yeah, I probably can take a look later, yeah. That is a very good question. Right now we are C++ only. Extended to the rest is like we had some discussion but it's like likely right now our focus is C++. We may like need to like really look into like if there's a use case or like anything that we want to enable. Oh, we actually can. I mean so I guess like contributors not open like I mean like the extortion in general but if like we want to develop a delegate that is for partner then welcome to reach out or we have actually have a guideline in the tutorial to introduce like how we introduce a safe example. They are on hardware and they are on compiler to the flow. Oh, that's a very good question. I think maybe it's easier to like say I think what I can do is like I can file an issue on GitHub so we can track on that. Cause I think there will be some like we probably need some internal discussion. I think on some like I can think like it is possible but we need some internal discussion. Yeah, yeah, thanks. Oh, I think AOT inductor is mostly on the server side. Which side? Yeah, this one is mostly talking on edge devices. Yeah, yeah, yeah, yeah. You mean like from the PyTorch program and then export it will there be any failure? That's a very good question. So it is working progress. Now it's hard to give a number right now but I think many models can be exported directly and then for the export API our goal is we need some user interaction. Essentially like the Torch.export say for example it fails and then it will give like it will try to give users like say why it's failing and then user can like say oh, I will need to modify that specific of line because of dynamism or like something like that and then we can come back to the Torch.export API and then after we make that change and then we can come back to the Torch.export API and then export model. So I think for the Torch.export actually during the PyTorch conference the number they claim is pretty good. So the goal is like able to capture a sound graph from any PyTorch program. So if your model cannot be exported that's what we want to check and it will be good to like file an issue and then we will look into like why it's failing. Is it better than sorry? Oh Torch.export. So I guess like Torch.export is exporting an Onyx graph is it? Yeah, so I think Onyx graph is for Onyx runtime. Is your question like say successful rate? Oh, that I am actually not sure. Yeah, yeah, sorry about that. Any more question? Oh, you mean like comparing XTorch versus TensorFlow library? I actually not quite. TensorFlow itself is in C++ and there's the front end. There could be a Python front end I remember in TensorFlow but the TensorFlow library if I remember correctly is in C++. Give everyone.


What's going on everyone? Sam here with LaytonSportsGarves and I am ripping 2022 Tops Definitive Baseball Hobby 3 box case break, number 50 random division style. Here is the results again to the random. Leave these up for just a second. You can always find these under the results tab on the website. Scroll all the way down as well as the link in chat if you're currently watching. Let's roll on some definitive. Here we go. I want to see a cut from out of this dude. Like a Jackie Robinson cut or a Roberto Clemente cut in this. Does he really? Yeah. What are we going to die? Oh yeah. He has what, 3,000 right on the dot, right? Destroy the fan right there. Alright here we go. Good luck everyone. Definitive. Definitive. Here we go. Starting off with the AL West. Alex Bregman 4 out of 15 on the three color patch. John A on the AL West. Got 36 out of 50. John Moncada. AL Central. Nice patch there. Oh this is sick. 24 out of 40 for the AL East. Wade Boggs. Favorite player. Oh yeah? Yep. That's awesome. Nice wade. 100% good. She's Harper again. 4 out of 15. AL East.  Just got choked up so big. Congrats on this one Harper. This is nasty. Dude. It's so hot. Compose myself for a second here. You don't have a poker face. Oh I know. Okay. That's fine. You want to come play with us sometime? I'm not letting you take my money. AL West. John A. 5 out of 5. Duelado. Mike Trout. Shohei Otani. Come at John A. This is a monster. A monster dude. Oh my god. And with the trout really quick. Nasty hit right there. Congrats John. Oh my lord. Oh Forrest. You're going to like this one. Gold framed patch auto. 2 out of 30. Dustin Padrella. Heck yeah. Fulled out one of those yesterday. I think I remember that. We've pulled me and Forrest to pull 40% of those now. Dustin Padrella. Sean's also Sean's boy too. Yeah man. Love me some Padrella. Got rookie auto for the NL East. That is Edward Cabrera. Got that Padrella's AL East. Sorry about that. Yeah true. Then we've got a nasty one here. AL West. 1 out of 5. Rod Carew. Nice one there again. You can see the 1 out of 5 on the purple. Congrats on that one. John A. Having a heck of a break here man. Picking up so many nasty cards. That's only box one. Let's go dude. Out of the auto. I know. Sad man. Dude could hit a ball. Oh yeah. Empty. Ooh nice one here. AL Central. 1 out of 15. Miguel Cabrera. Nice two color patch on that one. Kenny G. Corbin Burns. 40 out of 50. Going to the NL Central. Jumbo Patch. Jumbo Jersey I should say. 2 out of 40. Bat auto. That is Will Clark. Nice one there. NL West. Oh yeah. Big time. Got the green. Jumbo Bat auto. That is going to the NL Central. Paul Goldschmidt. 25 out of 25. Here we go. Jeffrey H. Back to back. Nasty hits for your division. How about Joey Votto. Jesse Winker. 21 out of 35. Again. The NL Central. Man love seeing everybody is hitting nasty stuff. Kenny G. Coming in and joining the fun. Purple. 3 out of 5. Aaron Judge. All rise baby. Heck yeah. Your AL MVP right here. Aaron Judge. Congrats. Kenny nasty hit man. Get back this one. Yeah. Got AL West. That is green rookie auto. 19 out of 25. Chas McCormick. Nice one there. AL West. And to finish it off. Jeffrey H. really ran with this box. Barry Larkin. 18 out of 25. Definitive collection signatures. Nice one there. Uh Macaroon.  Macaroon are you a Boston fan then I'm assuming? For your... I almost feel like I fell out of the chair right there. Are you a Boston fan for your American sports? Yeah. How are you from New England? I am too. Originally from New England at least. From Connecticut. Trader. Got going to the NLE.  Zach Wheeler. Nice one there. 11 out of 30. There you go. Harper. Got NL West. Chuck Nasty. Charlie Blackman. 12 out of 50. Why no Bruins? Got 1 out of 10. Gold patch auto Sammy Sosa. NL Central. There you go Jeffrey. Nice slamming Sammy. Book N1 out of 10. Got 17 out of 50. That is JT Real Muto. NL East. There you go Harper. Got this is nasty. 5 out of 35. This is going to be one of the Jersey numbers. That's David Wright and Peter Lanzo. NL East. Congrats again Harper. That's sick. David Wright and Polar Bear Pete. Got gold framed patch auto John Smoltz. 19 out of 30. Is this so solid upside down? It might be. We'll take a look in the recap and that. It might be. Rookie Auto. That is AL East. Josh Low. Green.  NL West. Harper Congrats on this one. One of the few hits this team has. 1 out of 15. Randy Johnson. The big unit on the NL or the World Series MVP with those throwback Diamondbacks Unis. So sick. Congrats on the Randy. That's so nasty. Let's see that. Yeah. All right that'll do it. Gonna do a quick recap here guys. Really good break. NL West. Charlie Blackman patch 12 out of 50. NL East. Zach Wheeler. Blue patch 11 out of 30. NL Central. 40 out of 50. Corbin Burns. AL Central. 1 out of 15. Miguel Cabrera. Yon Moncada. AL Central. 36 out of 50. And AL West. 4 out of 15. Alex Bregman. Gonna go into our non-magged autos. We've got AL East. Green Rookie Auto. 19 out of 25. Josh Low. John Smoltz. 19 out of 30. Gold frame patch auto. NL East. Oh just wait on this one, Simvita. Peter Lanzo. David Wright. Duel. 5 out of 35. NL East. JT Rial Muto. 17 out of 50. Patch auto. NL East. Sammy Sosa. Is that upside down? I don't know. It's so hard to tell man. Weird man. This might be upside down. 1 out of 10. NL Central. Barry Larkin. NL Central. 18 out of 25. Chas McCormick. Green Rookie Auto to 25. 19 out of 25. AL West. And NL Central. Joey Vado. Jesse Winker. 21 out of 35. Dual patch auto. Paul Goldschmidt. 25 out of 25. Green Dual patch auto. Will Clark. NL West. 2 out of 40. Rod Carew. 1 out of 5. AL West. Edward Cabrera. Rookie Auto. 34 out of 50. NL East. Dustin Padrella. 2 out of 30. AL East. And Wade Boggs. 24 out of 40. AL East. Oh geez. Alright we've got magged cards. First up, NL West. Randy Johnson. 1 out of 15 on the big unit. How about Aaron Judge. 3 out of 5. Purple Auto. AL East. NL East. 4 out of 15. Juan Soto. Jumbo Bat Autocraft. Congrats Harper. Congrats Harper on the Johnson. And congrats Kenny on the Judge. And hit possibly of the night so far. 5 out of 5. Mike Trout. Shohei Otani. Purple Dual Auto. Congrats John A. Holy. Yup. That'll do it for the break. Next up we've got some soccer. Good luck and I'm excited everyone.


Imagine that I'm a New Zealand play. What would I be about? Perhaps I have something to say about life here in Aotearoa. I'm looking for audiences to connect to. Each night I'm recreated live to win over the hearts and minds of a new group of people watching me in the theatre. Now the audience for New Zealand plays here are small and my form is not very economic. If I'm to have an ongoing life there is significant appeal in capturing a much larger audience in overseas destinations. I want to know how plays like me have represented New Zealand to the world and how the world has received us in turn. There weren't many plays before the 1960s. New Zealand theatre was ready to be invented. Playwrights sent their plays overseas and from the 1970s more companies began touring productions. Overseas offers opportunity, escape, validation. Plays in this period attempted to show what makes New Zealand culturally unique and they looked for legitimacy that was mostly Pakiha identity via overseas approval. But how do you know that I'm a New Zealand play? Is the Rocky Horror Show a New Zealand work because there's a statue of the author in Hamilton? What about New Zealand's most prolific playwright Roger Hall who actually grew up in England or how about a company performing Shakespeare in London's Globe in Torreo? Yes, yes, yes. Because national and cultural identity itself is a performance, it's the assumptions that become naturalised by the very act of repetition. This is the feedback loop, the self-referential markers of identity that the ideal local knowing audience is meant to recognise as their own. It includes and it excludes and it resonates differently when placed outside of this country. Now, if I want to gain international commercial success, go to the Edinburgh Fringe and then get picked up by producers, what's my best strategy? Is my cultural difference or should I adapt myself for their culture? Many of us have gone for adaptation. Markers of New Zealand identity are exchanged with markers intended to stimulate recognition for the new audience. So Wellington becomes London and New Zealand is written out of the work. Generation of Z, an immersive zombie show, zombies that played in London last year, rejected New Zealand locality in an attempt to enter an international paradise where an endless horde of zombie fans awaits. Now, that's one way to make theatre for a globalised world. Another is to emphasise the local specificity, embrace New Zealand identities, and that's work two. Now, imagine that I'm a theatre scholar. Difficult I know. I have found that audiences find ways to relate anyway and actively search for markers of cultural equivalence. Through research and reviews, I have investigated what does and does not resonate, what remains outside of the feedback loop. Some markers travel, some remain here. My thesis tells the story of New Zealand theatre's overseas experience for the very first time. Thank you.


eeeeeee is it working? ahhhhheee whats going on boys? hey i missed you boys i love you how we doing? how we doing boys sorry my wifi is honestly just not great i really don't have a great connection i tried to start up the stream earlier sh** it bricked out i'm in an among us lobby right now with like with the whole crew and and a bunch of guys actually so it's gonna be really really exciting i'm joining the server right now sorry they're all in there and i'm As always, I'm late, so I can't give you guys a good intro, but if I die early, me, chat, me, and you, we can hang out, like, for a long time, so... I'm excited, yo! Is this like, almost three straight days of Among Us streams, or is it two straight? My camera's, like, really high up. I'm not this small. You guys know I'm 6'5". Alright boys, ay! Trace-mo-feeling story, let's go! F***! I was shovel-f***! So, only two people went up that way. So, I'm gonna pretend I start wires at Electrical, and then I'm gonna go down to the Tree Room.  I don't know. Ooh, I don't even know if I can go that way right now. Yes, thank God. White's body... Oh, classmate. White's body is in the top decontam, like, in the hallway. White has a name? Thank you. So, I left him in the lab, and then I went into specimen and whatever, whoever he was in the lab with last. Do you remember? I don't remember at all. It was a while ago, actually, though. I got a thing. In the bottom left corner, the water wheels. You know how you have to do the right water wheel, and then you... And then what do you have to do? Either one. Wait, wait, wait, hold on. MMG, which one do you have to do next? I can tell you confidently that I've never once in my life done the water wheel task.  Wait, what do you mean? Well, I have the little... There's a yellow outline on that, you know, steel wheel, and I have no... I've never done it in my life. I have no idea what I'm supposed to do. I was clicking all the buttons. I couldn't figure it out. You just turn it. You turn it.  I was running my mouse around the whole time. Am I supposed to... Wait, I actually believe him. I don't... Yeah, I mean, I don't know what else to say, but then I also had a task that was the O2 canisters, so I just straight up left it and did the O2 canisters. Earlier, MMG did lie about setting up his stream when he called a reactor with me. We didn't call that out. I know.   You know, like, you have to... It's a wheel. You know what I mean? Like, it's a wheel. Dude, just think about the first time that you played this game and how confusing these tasks were. If anything happens, Psyderms and I are pretty much confirmed. I scanned, he watched me scan, I did a presentation, and then we were with each other the entire round. He watched me finish, right? Alright, guys, we gotta kill Psyderms and Speedy Galata. They're both of them or neither? Whoo! Okay, I don't know what to say right now. That's too bad. I'm gonna go directly to those water wheels and actually do the task, as everyone told me to. Or, like, pretend that I'm gonna do that. Goddamn, dude. Fuck.  Goddamn.  Well, you know what?  So what is it? Do you usually do this one and then you do this one? Alright, so yeah, I'm gonna stick to that. I don't think I'd pull this avatar right now. I'm gonna keep cams for a little bit. See where people go. See if I can pick up a kill. Shubble's here. I hope she comes with me. Okay, the body is in comms. I'm done with tasks and I'm just playing Admin Andy just looking for bodies. Bodies in comms? Yeah, so the two people in labs should be clear. Yes, ish, ish, 10 seconds.  Cheesy and Speedy were the two in labs, so I don't think they got this kill. No, me and Speedy's good for sure. Oh yeah, Psyderms was clear with hools. I mean, if he pulls his offense, that's so impressive. No, there was two kills.  No, there was two kills on the first round and one on this round. Okay, where are you plating this? I turned the lights on immediately. That was really fast. Yeah, I saw it too. If I was a killer, why would I be ready to... I don't like that excuse. Shubble, what did you do after you finished the download and weapons? I went into O2, I did my canisters and I did the levels on the tree. And then it was going outside and I caught you on the way up to light so they got turned off and I went back. Oh, Hafu, what side of the map do you think a person could have done the kill from? It's from the left side, so I mean, I know the rest of you are on the left side. Well, I saw D4 on the left side, I saw Platypus on the left side. First thing I did was I went and I actually did the wheels task. You have to guys explain it to me. I understand you're supposed to click the wheel and then turn it. I did that. I actually saw him in there doing it. The next thing I did was walk north and then I went to go do lights but Platypus had finished some fastest health. So I walked over to Cams and sat on Cams. If you guys trust me, the killers are between MMG, Shubble and Platypus. I do kind of trust Hafu. I mean, I'm skipping. We are all set with our tasks. I'm done with my tasks. The three are between Shubble, MMG and Platypus. Unless you think of me.  I don't know what I'm going to do here. So I have to do, I probably have to do reactor. I'm glad that vision dead. I got, I got away from my kills though.  Is there another sabotage we could do? I don't know what to do man. Okay. She said the range on your report is insane. Like how did I not do that dude? Where was she? She was in the admin like vitals area. The double kill will end off the game. So I feel like we probably should vote here. We absolutely have to vote here. Wait, how did you know? Well, I thought it was there. I wasn't in the top left. I was in the bottom left. Wait, the reactor. When the reactor. I saw the wrong person then. I just want to point out who was at the top left. Yo, the last time I saw the Lux. So you were like, he was running towards you and weapons. Everything and not that he went in, but you were doing the download. And then you said after the download, you went to go to O2 canisters.  Oh, sorry. Go ahead. No, I know. Yeah. And then shovel. What did you do? Like did you do your upload at all then? In communication? I went through my upload after the meeting. I didn't do it. I mean, I think we should. No, we absolutely have to vote right now. Absolutely have to. Shovel platypus and MMG. Do you have assistance? I had the same alibi for the first three rounds. And I'll go through it again if anybody wants. The water wheel thing for MMG. The water wheel thing. You know, I think it's funny you say that.  Because I saw you on that left side. So for this, for this kill. Yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes. No. That is a production. That's for the. Blood for the blood gods. That should be it.  What do I have to do? We just gotta get a double kill. Yeah. The meiner. The was.  Yeah, right? Oh, yeah. Oh, yeah, you're right people. We have to go for the person like Damn, there's so nice I feel so bad that I have to leave and I'm sorry boys I wish I could play more, but I actually have to go what a dog to end that puppy off It gets me so I did gets me so high. Tell me. I'm not the 1v1 goat Tell me. I'm not the 1v1 goat, bro I swear to God my percentage win rate on 1v1 lying. It's like 85 percent It's 85 to 90 percent, bro. It has to be I love you boys I'll see you the next dream the next time these guys hit me up be the next time I stream because I can't these guys can Assemble nine people to play like nobody else. I can't so it's all good. I love you boys. I'll see you next time. Peace


And the wealthiest nation this universe has ever seen. I'm going to go out and let them say the universe. We can't afford for everybody to live in a condom. I think it is the universe. I've been out there, I've checked. Hello, my guest today is Solana Rice, who is co-founder and co-executive director of Liberation in a Generation Action. So delighted to have you, Solana. I really want to find out from you what organizers do most successfully. What is it that an organizer, the best organizers do when they go into a community and build relationships around hope? I think the best organizers, for one, are from the communities. I'm always looking for the people who are already in the community and who are trusted and who are influential. There's no substituting trusted brokers. Because, you know, we're not always going to win. And even when we lose, we have made strides forward. And I think that's what's honestly the next step for many of our political parties is to realize, like, those trusted relationships have to be there day in and day out. You know, Mo Mitchell of the Working Families Party, for example, talks about the sand castles that we build during an election cycle, right? We were like, okay, stand up everything. Put up the signs. Put up the spaces. Okay, elections over. Tear it all down. Community organizers don't do that. They're there day in and day out. They're there. And I'm old enough to remember the Alinsky movement. I mean, we ever saw Alinsky was saying, you get into those communities, you build relationships, and you talk about and focus on things that are really important to people. You don't talk about ideology. You don't talk about political parties. You talk about the bread and butter issues. That's right. You know, what is it that people are worried about? And I will say, I find that when we are talking to organizers now, what they are saying is that people in their communities want to be talking about the role of racism. They want to be talking about why these problems exist. When you talk about racism, isn't it the case that a lot of the racism that is being pushed in our society is being pushed because you've got some very powerful forces that want to keep people divided. Don't want people to see where all the power is gone, see where all the money is gone. Oh, absolutely, absolutely. You know, Toni Morrison talks about that racism will no longer be useful when it stops being profitable, when it stops being psychologically useful, and it stops being profitable. And I think that racism keeps us fearful. It is psychologically useful because it says that that's somebody somebody else's to blame for why I'm struggling. It's the fear and it's the othering. Both of them together. So that would also include anti-Semitism. I mean, it's including every of the efforts that are being made to divide us, to make us fearful, to create a kind of a society that is very divided and very angry. Yeah, and we've started to codify this in policies. And I think people are ready to start saying, like, look, we've gone too far. All of us are suffering. And the wealthiest nation this universe has ever seen. I'm going to go out and let them say the universe. We can afford for everybody to live in abundance. I think it is the universe. I've been out there, I've checked. We are the wealthiest society in the universe, and we've never been as wealthy. But do we need, in terms of getting people to come together, do we need a common enemy, a villain? Do we need to say that there is an oligarchy that really wants us to be split apart so we don't basically have the numbers to impose a wealth tax, for example? In other words, how important in getting people together is it to get them to turn against a real force out there that is shafting them? I think it's important to name a villain. I think that's a first step, though. What we are fighting against is the role, the role that these folks hold, right? We are fighting against the assumptions that we have to work in fear, that we don't live in abundance, that everybody doesn't deserve dignity. So it is about naming a villain, but then it's also about understanding and fighting against the assumptions and beliefs that uphold those villainous roles. We've got to educate people about the system in terms of the incentives that are built in, baked into the system that are working against most people. And how would you begin to do that? In other words, what we're doing, you and I right now, is we're trying to drill into the process of educating people to come together. But how do you get people to understand the system's dynamics? I think folks understand the system's dynamics. I think everyday folks might not know about the role of the Federal Reserve Chair, but I don't think that's necessary, actually. Good. I get exactly. Because I was starting to worry. What I think is necessary is that folks that do know the power of the chair start to use our role to help get those folks, regular folks, as policy experts. I mean, you have to admit, the idea that a woman that was a bartender and a waitress is now in our Congress and she's rallying thousands and millions of people, that's exciting, right? Imagine if you and I just knew regular folk that were actually making policy decisions. And that's what liberation in a generation and liberation in a generation action. That's what ignites us, is that the people that are experiencing housing insecurity, the people that are experiencing wage theft and wage loss, they're policy experts. They experience the injustices day in and day out. They know the holes in our economy. They do. They know it's rigged, in a sense. But here's the problem.  It's rigged against you.  Yeah. And what I'm so excited about is that we are telling people, don't let anybody speak for you. That we are here to broker this relationship. So I'll give you an example. We've been working with the folks at National People's Action. They have a homes guarantee campaign. This campaign generated directly from public housing tenants. They are the ones that know about evictions. They are the ones that know about how housing and urban development runs their public housing. So we are there to broker a relationship between the highest officials at HUD directly with public housing tenants. That's our job, right? And when we empower those folks to walk into those conversations, we don't say, don't worry, we'll handle this. We've got the talking points. You tell your story. No. We say, here are all the policy options. Which ones sound right to you? You present those to the director of HUD. And that's what I'm talking about, is that anybody listening can play that kind of role. I have a confession to make. I was once a cabinet member in the United States government. I ran a big federal agency. And I was in conversations with a lot of people. A lot of people, a lot of average working people, a lot of poor working people. And they were in the same room as me. And they would tell me what they knew. And I would say, yes, you're right. And then what? I had very limited options. How do you break out of that? It's an inside and outside game, right? We can't just go to DC and expect that meeting or even a series of meetings to change. We have to anticipate that we are building power in order to change those dynamics that you're facing as a cabinet secretary, right? On the outside, too. So in other words, I, as a cabinet member, need those people out there to push me. Yes. And give me the power. Exactly. So I can turn to the structures that are making a very difficult movement for me to do anything and say, look, they're pushing me. Yes. Yes. That's right. And that's it. It's really simple. But wait a minute, what's the timeline? How long is it going to take? I mean, what are we actually envisioning in terms of our model for people becoming empowered enough to change the system? 10 years, 20 years, 80 years? In our generation. In our generation, like 20 to 25 years. But do you know what that means? It means that we can't tinker around the edges. We can't say we're going to do small things to the tax code. We're going to do small things to corporate regulation. Like, we don't have time. We don't have time. We don't have time. So you say 25 years. I mean, I'll be 100. I'll wait. But if you guarantee that things are really going to change. Well, that's what I'm also hopeful to is that, you know, there was a lot of talk in this midterms about the younger generations turning out and the reason why we did have some wins and some, and things weren't as bad as we thought was because younger generations turned out. And so I do think it is incumbent upon all of us to equip younger folks with this understanding about systems, about it's not necessarily just the person you're fighting. It's the actual structure that we're fighting. And this balance of like it's not going to happen overnight. We are living in racialized capitalism today. We can have a foot in something different for tomorrow, but we shouldn't just say, uh, it just won't happen. We just, we can't put up our hands. Cynicism is the enemy of real important social change. My grandparents could have never imagined the world that I live in. And so I'm trying to build a world that my son could have never imagined he would live in, right? Or that I would see for him. And it's not about what I'm going to see in my lifetime. It's about what he's going to see, what his kids are going to see. Um, and we have to have a little faith in that. It does, it is like a little sprinkle of faith. We've got to have faith that we're on the right route. Yeah. And it's not for the faint heart. It really isn't. And it's hard. It's a challenge to walk a path towards economic liberation. We've never seen it. We've never seen it in this nation. There's no blueprint, but, uh, it's still a venture worth, worth going on. So a lot of rice. Thank you for your vision, your optimism, your faith and your work. Thanks. Thank you.


a video that's a little bit different. I'm going to be showing you how to do five quick hairstyles either for school or for work. Now this was a requested video and I'm a little surprised because I don't think I'm that great at styling my hair. Now I can set my hair in a hairstyle, but actually styling that hairstyle, yeah, very lackluster girl. But regardless, I'm going to attempt to do five separate hairstyles for you guys. Now one thing that's going to make this video unique is that I'm going to show you basically how to transform your hairstyle throughout the week. Okay, so let's go. All right, you guys. So the first style is going to be a twisted updo. As a disclaimer, heads up, I will not be using any type of edge control or gel along my edges to lay down my edges only because I like to maintain a healthy hairline and I find that excessive usage of those type of products long term can do more harm than good. However, if you're looking for a more sleek polished look by all means, go ahead and use your as control of choice or hair gel. So anywho, I'm not going to be talking a lot throughout this video because I believe it's just pretty self-explanatory. And what you guys are going to see here are basically five hairstyles that you can wear for either work or school. I hope you guys enjoy this video. Take care. Bye. Ryan Little on the very last hairstyle. And I basically undid one side of my hair and I'm making a loose, large twist. And I'm going to tuck that underneath and create the last style here. Like I stated earlier, if you wanted to add a little gel or something onto the side here to help those flyaways go right ahead. So anywho, I hope you guys enjoyed this video. Take care. Bye.