We continue today with Chapter 8. The Body as Means or End. Attitudes toward the body are attitudes toward attack. To the ego, the body is to attack with. Equating you with the body, it teaches that you are to attack with. The body, then, is not the source of its own health. The body's condition lies solely in your interpretation of its function. Functions are part of being, since they arise from it, but the relationship is not reciprocal. The whole does define the part, but the part does not define the whole. Yet to know in part is to know entirely because of the fundamental difference between knowledge and perception. In perception, the whole is built up of parts that can separate and reassemble in different constellations. But knowledge never changes, so its constellation is permanent. The idea of part-whole relationships has meaning only at the level of perception where change is possible. Otherwise, there is no difference between the part and the whole. The body exists in a world that seems to contain two voices fighting for its possession. In this perceived constellation, the body is seen as capable of shifting its allegiance from one to the other, making the concepts of both health and sickness meaningful. The ego makes a fundamental confusion between means and end, as it always does. Regarding the body as an end, the ego has no real use for it because it is not an end. You must have noticed an outstanding characteristic of every end that the ego has accepted as its own. When you have achieved it, it has not satisfied you. This is why the ego is forced to shift ceaselessly from one goal to another so that you will continue to hope it can yet offer you something. It has been particularly difficult to overcome the ego's belief in the body as an end because it is synonymous with the belief in attack as an end. The ego has a profound investment in sickness. If you are sick, how can you object to the ego's firm belief that you are not invulnerable? This is an appealing argument from the ego's point of view because it obscures the obvious attack that underlies the sickness. If you recognize this and you also decided against attack, you could not give this false witness to the ego's stand. It is hard to perceive sickness as a false witness because you do not realize that it is entirely out of keeping with what you want. This witness then appears to be innocent and trustworthy because you have not seriously cross-examined him. If you had, you would not consider sickness such a strong witness on behalf of the ego's. A more honest statement would be that those who want the ego are predisposed to defend it. Therefore, their choice of witnesses should be suspect from the beginning. The ego does not call upon witnesses who would disagree with its case, nor does the Holy Spirit. I have said that judgment is the function of the Holy Spirit and one that he is perfectly equipped to fulfill. The ego, as a judge, gives anything but an impartial judgment. When the ego calls on a witness, it has already made the witness an ally. It is still true that the body has no function of itself because it is not an end. The ego, however, establishes it as an end because, as such, its true function is obscured. This is the purpose of everything the ego does. Its sole aim is to lose sight of the function of everything. A sick body does not make any sense. It could not make sense because sickness is not what the body is for. Sickness is meaningful only if the two basic premises on which the ego's interpretation of the body rests are true, that the body is for attack and that you are a body. Without these premises, sickness is inconceivable. Sickness is a way of demonstrating that you can be hurt. It is a witness to your frailty, your vulnerability and your extreme need to depend on external guidance. The ego uses this as its best argument for your need for its guidance. It dictates endless prescriptions for avoiding catastrophic outcomes. The Holy Spirit, perfectly aware of the same situation, does not bother to analyze it at all. If data are meaningless, there is no point in analyzing them. The function of truth is to collect information that is true. Any way you handle error results in nothing. The more complicated the results become, the harder it may be to recognize their nothingness. But it is not necessary to examine all possible outcomes to which premises give rise in order to judge them truly. A learning device is not a teacher. It cannot tell you how to feel. You do not know how you feel because you have accepted the ego's confusion and you therefore believe that a learning device can tell you how you feel. Sickness is merely another example of your insistence on asking guidance of a teacher who does not know the answer. The ego is incapable of knowing how you feel. When I said that the ego does not know anything, I said the one thing about the ego that is wholly true. But there is a corollary. If only knowledge has being and the ego has no knowledge, then the ego has no being. You might well ask how the voice of something that does not exist can be so insistent.  There are many instances of how what you want distorts perception. No one can doubt the ego's skill in building up false cases. Nor can anyone doubt your willingness to listen until you choose not to accept anything except truth. When you lay the ego aside, it will be gone. The Holy Spirit's voice is as loud as your willingness to listen. It cannot be louder without violating your freedom of choice, which the Holy Spirit seeks to restore, never to undermine. The Holy Spirit teaches you to use your body only to reach your brothers so he can teach his message through you. This will heal them and therefore heal you. Everything used in accordance with its function as the Holy Spirit sees it cannot be sick. Everything used otherwise is. Do not allow the body to be a mirror of a split mind. Do not let it be an image of your own making, perception of littleness. Do not let it reflect your decision to attack. Health is seen as the natural state of everything when interpretation is left to the Holy Spirit, who perceives no attack on anything. Health is the result of relinquishing all attempts to use the body lovelessly. Health is the beginning of the proper perspective on life under the guidance of the one teacher who knows what life is, being the voice for life itself. And from the workbook, Lesson 63. The light of the world brings peace to every mind through my forgiveness. How holy are you who have the power to bring peace to every mind? How blessed are you who can learn to recognize the means for letting this be done through you? What purpose could you have that would bring you greater happiness? You are indeed the light of the world with such a function. The Son of God looks to you for his redemption. It is yours to give him, for it belongs to you.  This is no idle request that is being asked of you. You are being asked to accept salvation that it may be yours to give. Recognizing the importance of this function, we will be happy to remember it very often today. We will begin the day by acknowledging it and close the day with the thought of it in our awareness. And throughout the day, we will repeat this as often as we can. The light of the world brings peace to every mind through my forgiveness. I am the means God has appointed for the salvation of the world. If you close your eyes, you will probably find it easier to let the related thoughts come to you in the minute or two that you should devote to considering this. Do not, however, wait for such an opportunity. No chance should be lost for reinforcing today's idea. Remember that God's Son looks to you for His salvation, and who but yourself must be His Son. The light of the world brings peace to every mind through my forgiveness. So our teachings from the text and the lesson number 63 all point to a holographic universe, a holographic cosmos, that when you forgive in mind, you see everything as whole. In forgiveness, there is no difference between the whole and the part. Everything is all-encompassing, universal, without end. Everything is infinite, as mind is infinite. Today we make the connection between forgiveness and seeing a completely new world, the real world, the happy dream that leads us back to eternity. We honor the awareness that the body is a means for expanding perception and seeing the whole. The body is not an end. Nothing can be done for the body, but the mind that follows the Holy Spirit can use the body as a means for experiencing holistic perception, true perception, the real world. So today we open up to this function of forgiveness. We see that our function of forgiveness brings light to everyone and everything. We see that there is no mind apart from our mind, as long as we believe in people and in separate minds. We acknowledge that our function brings healing to all minds and to this healing awareness that everything is mind, that there is nothing outside of mind, nothing beyond mind. We practice at every opportunity today making the connection between our function and the salvation of the world. That forgiveness is the means that God has appointed for the salvation of the world. We practice in earnest the light of the world brings peace to every mind through my forgiveness. Amen.


Hey guys, I'm here at the 21 convention with one of the most infamous speakers Steve Maeda is your seventh time speaking That's right lucky number seven lucky seven. It was an amazing speech Can you give the viewers a quick synopsis of what you covered? Yeah, actually?  Yeah. Yeah.  It's always nice. Yeah femininity is good So, you know, what is it about this conference that keeps bringing you back dude? This is okay So this is actually something that I'm like extremely excited to talk about because there's so much here that has influenced my life so since 2008 I've been speaking at 21 convention and First off if you don't change with like the stuff that you're learning in terms of the business and economic stuff You're going to learn that, you know You're going to see people succeed from it the next year and then one of the biggest things is diet and exercise and Man, I can't tell you since 2008. I've seen, you know The same guys coming into the convention and they getting more in shape They're having a better mentality a clearer focus on life and really turning into, you know It's kind of weird but a brand of the 21 convention and really a representation of that and so man for me that's Changed with my diet which helped with my mentality which helped with business and you know There's also other evolutions that hopefully I bring that, you know, step it up to the plate as well Yeah. Well, I know the feeling. Yeah. Yeah, totally. Yeah. Yeah, because last year I know both of us man How much weight did you lose? You know, I don't know at this point. I think I'm like at 160 Yeah, and it's almost like you feel that you're too small, but I also worked out with Keith Norris I haven't been lately, but that's just because the man's a genius and too freaking expensive But I've never been that strong. I've never been in that much shape And I've never seen the benefit to bodybuilding and and he showed me what a tremendous form of exercise That was and and how important that was I mean absolutely here met up at the 21 convention He happened to live in Austin and we made it happen. Yeah, well I don't think it's a coincidence that I lost 50 pounds or whatever right after I think it's like 30 or something for me Yeah, well We're both a lot hotter now. I think Awesome to see you. Yeah. Thanks for coming. Oh, you got it. Yeah. Yeah, and once again Steve mighta guys


Holden and welcome Hey, thanks. Thanks for having me. I'm holding. This is professor timbit Helping me this morning to make sure I don't fall asleep Hello professor can I call him professor? Yeah. Yeah, he goes by professor Okay, his research area is you know, he's not he's not ready to talk about it just yet He doesn't want to get scooped But he had some very exciting papers coming out. I'm sure Okay, they're looking forward to listening to you. Holden one thing Just remember remind our audience that you can start sending questions for holding as as now as as from now Because otherwise when we'll have time apologies for starting a video with delay Holden So any questions for Holden, please, uh, where did she get those fantastic? Bet sheet is also allowed So All yours Holden Thank you. Thanks So, yeah, I'm going to talk about some of the similarities and differences between spark dask and ray And in doing that, they're they're all distributed systems. So we're going to talk a little bit about some of the principles of distributed systems Um, so yeah, my pronouns are sheer her That intro is actually already amazing The only thing that I want to add to that is I also do these code review live streams and I also do some live programming now and live streamed writing of tech books And so if you're interested in in those things, you know, definitely check out my my youtube There's there's a bunch of streams there I'm also Trans queer Canadian. I live in America. I got my green card This year, which is very exciting. I mean it's harder for them to get rid of me And also part of the leather community And that's not directly related to these things But I think it is important for those of us who are building data or ml tools, especially those of us working Uh in open source or in large companies, which can have a really large impact in the world To look around at our teams and you know, if everyone's from the same background as us, it's time to Try and expand the the pool of people that we're working with And part of that is talking about where we're all from as well in our background So I'm hoping you're all nice people You are probably interested in distributed systems if you're here and if not, that's that's okay, too You know, I'll I'll try and have some pictures of timbit to distract you Um, if this isn't your cup of tea So I'm going to talk about distributed system I'm going to talk about data parallel distributed systems and we're going to look at these three systems We're going to talk about how they're different Um, and then we're also going to talk about some common parts Some of the parts where they're very similar And we're also going to talk about some of the mistakes that we've made in building these systems over time So for those of you who aren't familiar with distributed systems, your life is probably much happier There's this wonderful quote from Leslie lampour A distributed system is one in which the failure of a computer you didn't even know existed can render your own computer unusable and I think As as someone who works on on spark, you know, we we do a lot of work to try and make it so that that's not the case but at the end of the day There are still times when the failure maybe not of a computer anymore But at the very least the failure of a rack could very easily Cause our our systems become unusable Um, so why do people use distributed systems scale? Um, generally speaking if your data fits in memory on a computer, that's a lot better. It's it's much less work Uh, you can solve problems by throwing money at it Relatedly the follow-up one is it turns out buying a lot of memory in a single computer Like yeah, you can buy huge huge amounts of memory in a single computer But then computers just get really really expensive Um, and the last one is a bit of a joke, but not completely Um, distributed systems also make traditionally simple problems really challenging to solve Um, and I know for me that that's part of the appeal and from a business point of view like this is terrible Um, but from a like engineering point of view, it means that all of these problems that are kind of boring become interesting again Um, and I think I think that's neat So what are what are the core building blocks that all of these different distributed systems? Uh, builds on top of so there's distributed locks distributed clocks distributed counters Um, and pretty much, you know every sort of fundamental computer science thing a distributed version of x for all of x the locks clocks and counters are or sort of some of the key building blocks that they all depend on and While i'm just talking about data parallel systems today, um, like dask Uh ray and spark it's important to know that there are other kinds of distributed system problems file storage systems if you use hdfs or s3 Those things tend to be distributed systems min i o can be Can be not If you've run folding at home or the distributed net rc5 challenge Um, those things tend to be embarrassingly parallel With minimal or no coordination between the nodes Those problems are really fun And they're really nice because they don't involve a lot of communication between computers And communication between computers just like with humans is where things break um databases not all the databases are distributed but Cassandra's a good example of a distributed database API servers are also, you know, distributed systems frequently these days We we tend to have multiple api endpoints and put them behind a load balancer um and For the most part data parallel systems, which we share where I work Let us ignore a whole bunch of problems that we have to deal with in all of these other ones right file storage systems Super super painful to write. Um, but data parallel systems Not as bad so To a degree, um In data parallel systems, we get to ignore time and ordering of events and this is pretty awesome um The notable exception is when people insist on making streaming systems Which is unfortunately increasingly popular Uh, to a degree we get to avoid network partitions um, not because they don't happen just because We dedicate the the winning partition Is whichever partition happens to have the head node on and that's very very easy Uh multiple clients generally speaking. We don't allow multiple clients. So it's a lot easier when you have a single client leader elections We generally is statically assigning leaders. So there's no election um And we tend to ignore this failure of a keynote thing And this lets us get away With all kinds of problems Because we can essentially just take a distributed system problem And say, you know what? We're just going to solve it on just one computer and we're going to make that computer responsible for it um And you know, that's really cool. Um, the downside is of course if that node fails Everything breaks, but you know, it's it's not too bad. We get to skip a whole bunch of problems But there's there's some downsides to this Um, we'll we'll get back to those. So what's what's left when we when we skip all of those problems? Dividing and coordinating the work reliability on machine failure besides the keynote Um, and the times we allow states. So while we get to ignore state to a large degree um training machine learning models Uh tends to involve like building up a bunch of state. You're you're building up this Uh collection of parameters that represent your model um Transactions sort of matter and this comes from Even without streaming we tend to need to do things like speculative execution Um, and the last one the last one is the really important one Uh bottle and axon the reliable node, right? So once we've designated this keynote The problem is engineers are lazy and we tend to put a lot of things on that one keynote But then it turns out that this starts to get really slow and all kinds of sad So how hard can dividing work be? um, so If you if you're an ic you might be like, you know what that doesn't seem like that much work But if you have a manager or a p.m You can go and ask them how hard it is to divide the work of your team and they might have some opinions Um, but even in computer world things are really difficult, right? Uh key skew, um falls into this problem of dividing work because frequently we try and partition by keys And so the key skew here Uh, it really gets us non-uniform processing times. Uh, so stragglers, um, which if you if you've been using spark You're probably well aware of and pretty much any variant of trying to coordinate Uh and split up work is actually really hard, you know, it sounds really easy until you try and do it And then life just gets all kind of painful Then there's the fault tolerance like how are we going to handle losing a node And different people have different approaches um, so adobe map reduce, um Solves this reliability problem by just saying, you know what none of my workers are reliable I'm just going to save the data out to the adobe file system and that's going to replicate it across a bunch of computers and Then it doesn't matter if my computer fails because there's a replication of it somewhere else And I can just go and read it from there Recompute on failure is the approach taken by spark and ask Into a limited degree ray and we'll we'll talk about that more later But it requires that we keep track of how to recompute data um, and It also really breaks down when we're updating state because When we go to recompute the data, we might update the state more than once Um, and also it gets really annoying if our failures become correlated Historically recompute on failure was really good because failures of computers. They weren't not Like dependent they they were semi independent, I would say But with more and more people moving to the cloud They've become a lot more correlated as people run on things like spot instances or preemptible instances um, the other one, you know math and extra computers like that's that's the paxos approach um And this is really hard. It tends to be the most reliable approach And we tend to not do it very much because it's also really slow And the last one is ignore it. Um, you will be surprised how often that's the approach that people implicitly end up choosing um, and we'll We'll talk about that a little more Actually, I'll talk about it now. So and ray in its in its early versions actually took the ignore it approach um to Failure for anything involving state um And that's not great. That means that if you're if you're Actor, which is how ray represents state was scheduled only know that failed It would just fail and your application would just stop working And you are responsible for managing that and recovering from that and in the newer versions of ray. They've added Framework to to use Actually, it doesn't have a strong opinion on which one of these techniques it uses to recover from failure It's up to you to pick which one, but they have a framework that that lets you implement the recovery logic a little bit Less painfully So why do we have to care about state? um So even if we're doing stateless transformations, there is some state like how far we come along which records have we processed Generally at the end of the day, right like as much as people love functional programming We want to do something with our data. We want to write it out and that's kind of kind of state um We can think of this as has timbett had a bath this month. I think he has But keeping track of that is you know, that state the state of timbett has he has he had a bath Once we add state things start to go to hell um Using specialized systems is often how we deal with it. Um in spark We mostly deal with it by shoving all of our state onto that one reliable node But at the cost of just being really really slow um So there's some options here to handle that failure of the keynote um And generally speaking, I would say I don't see this done very successfully most of the time Uh, normally what people do is they just restart the job on the failure of the keynote um, normally people use something like zookeeper um to keep track of of everything and that's the spark high availability mode uh, but restarting the jobs is non-trivial and so this is like The magic hand wave, but it's about as easy as convincing professor timbett to shake your paw without any treats I'm sorry to shake his paw with your hand um So what about bottlenecks? So spark and desk both fall into this situation of having a central scheduler and that's Really great and that it lets us make all kinds of smart decisions because all of the scheduling logic is happening in one place And we can do all kinds of things like caching and stuff like that inside of our scheduler The downside is that if we have thousands and thousands of nodes and We're trying to schedule so many tasks that scheduler can get get overwhelmed The other one is in spark. We put all of this state on that one node And so that that one node is just very very busy Um, and that's that's not great, right? And the distributed system You really don't want one node to be busier than the rest. That's that's the sign that you aren't doing a good job of splitting up your work um And then the transactions one, uh, so this one's important for speculative execution even if we're just considering Uh traditional data parallel systems It's like non-streaming This is because we generally have multiple regulators, but we have one committer Who is responsible for deciding like hey, am I done processing this move it like market is done So the the next job can know that this data is done and ready Although it turns out that the approach that we we take, uh, which came from htfs Which involves renaming files to indicate that everything was ready Not all file systems support atomic renames And uh, it's really important that these operations be atomic. Otherwise, you know, you don't really have transactions If it's not atomic, uh, you can get like these partial views and that's that's really bad um And the solution to this is to put another system on top of the non atomic system That then gives you an atomic view over top of it, which is the approach taking with us 3a Uh, it's kind of weird, but it's okay So we've talked a lot about sort of the core building blocks, um Of these systems and a little bit about their differences But what are what are some more of the differences? I think a really important one is the apis exposed Um, and this is really important spark, but just exposes high level apis Um, it really doesn't let you schedule raw tasks. It's very much focused on data parallel systems only Another one is this unit of scheduling work and this task overhead, right? So essentially we can think of this as like your manager talking to you Um, if it takes them five minutes to tell you about a task versus it takes them 10 seconds to tell you about a task Um, they're probably going to be comfortable delegating different things to you Um, and so that that task scheduling overhead applies to computers as well And that approach to node loss, right? We could think about like how we handle it when our coworker quits Um, and similarly in spark. It's And ask and rain. It's how do we handle it when one of my computers die? And another one that's really important that I think we often overlook Because we're technologists is what is the community around these tools like? um So more concretely Ray has probably the best approach to the distributed state of of these three Um, strangely enough, it doesn't support the Standard example that we're all used to of word count because it doesn't It doesn't have shuffle Of course, there's an asterisk there. You can make word count work, but it's just really Really painful. Um, you you normally end up running desk on top of ray at that point And it's built in c++. Um, and it has python and java apis and by default ray is Less tolerant less fault tolerant that is And that's that's okay You can change these configurations to make ray behave more like desk or spark From a regards to fault tolerance Handling the state is more than just a configuration change though So you'll you'll have to write some code to handle your actor recovery Dask is notable for having Really kick-ass pandas apis. It probably has some of the best python integrations out there And it also has these really wonderful low level python apis ray also has low level apis But they tend to be implemented in c++ and that's that's great for performance But not Has great for getting people to use them. Um Because they can be a little bit more complicated for people to figure out what's going on Spark is is sort of the one that we're all used to I would say or at least this one that I'm most used to It has really only high level apis And that's that's not a bad thing right these high level apis mean that spark is able to take a much more aggressive approach to fault tolerance it's able to do a lot of really cool things with optimization, but It does mean that you can't schedule raw tasks in the same way It's built in java and it has apis for I would say probably the most languages Python is built in are built in but then there's also a whole bunch of apis for different languages That come from the broader community like C sharp It does have a new panels like api. It's not as feature-complete as dasks It does also have more overhead Ray probably has the lowest per task overhead Dask is in the middle and spark is at the high end And what that means is like in spark for it to make sense for us to be using spark We need to be able to split up our work into sort of like moderate sized chunks And in ray we could use much smaller sized chunks and then ray somewhere in between um And the last one is of course the hiddupe ecosystem If you are working at a place which has you know, a big data stack Spark is a much better job of integrating with the rest of the tools like impala And things like that in the hiddupe ecosystem Your catalog all those things That ray and ask, you know, they can they can talk to hive of course, but they don't understand the hive catalog in the same way that spark does Okay, and we are still Running a little over time. So I'm very sorry about that um But one of the things I want to talk about and this is because it's it's come up recently Is there is there's a lot of conversation around benchmarks? and this is because well one of the vendors um is Let's just say they're they're trying to illustrate that they're they're still relevant By benchmarks, um, and I think benchmarks definitely have their place, right? I think it's very very reasonable to do benchmarking On the other hand, I think that desk and ray and spark all perform pretty well at the medium sized scale of data And if you're at the like petabyte plus scale of data I think it's really important to not just take one of the like industry benchmarks like tpcds Um, you should probably make your own benchmarks that are related to your use case because Like tpcds is a lovely synthetic benchmark But it may not represent very well what it is that you're trying to do Um, and I think I think really for most of us probably the thing to do is to pick the one Which is best suited to our domain and our team, right? Like if you've got a mixture of java and scala and python programmers You know spark looks pretty appealing because they can all work together um on the other hand if you've got some amazing kick-ass data scientists who just like Came here to chew bubblegum and use pandas and they're all out of bubblegum You know desk has probably the best distributed pandas api Any of your options, right? Of course, you know if you want to look at benchmarks, that's cool. I'm not going to go into benchmarks because I think that Well pretty much You can always make a benchmark say what you want to say and it's just it's not worth it To me, so we're we're going to skip this but for preemptive So on that note, um I am three minutes over. I'm very sorry about that. I am working on some new books Namely scaling python with ray scaling python with desk and distributed computing for kids that one's in spark Um, and if you're interested in being an early reader or your kids are interested in being an early reader For any of those books, please DM me on twitter. It's just my name holding caro or email me It's just holding caro at gmail.com and let me know that you're interested in seeing Early drafts of this stuff and I would love to share it with you and get your feedback Another one is I really think all of these things are open source I think the community is of course a little bit different. Um, I've contributed to to all of these projects Um, and if you're interested in getting involved with any of these projects, definitely Please feel free to reach out or just try getting involved, you know I think they're they're great projects And I think that one of the ways that we can make sure that our voices are heard Is by contributing to our our open source tools that we're using Um, and I'll be doing more open source live streams if anyone, you know wants to come and watch and get an idea of what it's like to contribute to these projects in the open source space Um, so I'm hoping that we might have enough time for for a question I know I know we are five minutes over though So feel free to shoot me an email with your questions And I will I will do my best to answer them Okay. Thank you. Thank you so much. Hold them. Uh, that was fantastic. Uh, first of all, what does uh, mr. Professor timber Think about all this Where is he so professor timber? He's he's got a mixed view. I would say um, he does really like Uh The desk people the most I would say um I think that's mostly because they talk to him when we're talking on video together Um, the other people don't talk to professor tim bit as much. He's he's very engaged and in the research, of course Okay, well, there's a question for him Just one so just one for him and we don't have time for more But if you could answer on his behalf because we don't see him. So, uh, oh, yeah, he went back to sleep It's a very busy guest transmitted to him. Uh, we say this is clearly not, uh, a case of one size fits all So in order to choose the right framework Which you obviously explain a few of the differences somebody says Is the option having a data science infrastructure flexible enough to allow for a mix and match mix and match approach Yeah, so I I think what professor timber would say here is that that's definitely an option I think With with kubernetes, um, it's it's quite possible to have a mix and match approach and I think it's it's very solid the downside is um It's it's a little bit more painful to to maintain from a systems point of view, right? Um If you can convince people to pick two of the three, uh, your life will probably be a bit easier Then trying to support all three of them Um, okay. Yeah. All right. In case of doubt, uh, people can email you you said the best way to contact is to twitter dm or to uh, or through your well, obviously watch your Your youtube streams But in order to ask you any questions about which frame and to which to use or which two to use In case of only we can only choose two to dm you on twitter or, um, if we have any questions Are your new books coming out? Uh, yes, that's true any books coming out. We email you Uh, well, congratulations on that green card. By the way Excellent. Thank you. Miss professor team battles as well. Does he go in the package with you? Yes. He's he's included Okay, that's great. He's a lucky. He's a lucky man or he's a man or a girl or a boy or a professor He's a lucky professor Holding thank you so much for your talk I'm sure people will contact you directly We hope to see you again. You know, you're very much loved in uh, big things conference So, uh, I really appreciate it and and thank you. Thank you for the well Which is the the one year that I got in the in the motorcycle crash It was it was very very kind You're very very much loved uh holding so we hope to see you and professor timbert next year if not before that So in the meantime We dm you for whatever we may need Lots of love and see you very soon. Thank you so much


It's safe to say that if you come across a Super NES game that's based on any kind of movie or TV show, it's almost a guarantee it will suck. I know the angry video game nerd has made a running gag out of this over the years, but it's true. Whether it's Terminator 2, Lawnmower Man, Last Action Hero, Bram Stoker's Dracula, Cutsroat Island, Home Alone or Home Alone 2, these are all mediocre at best and that's putting it kindly. And TV shows often had even worse game adaptations. Just look at home improvement.  Who would even consider playing this, then or now? Anyway, it's been made clear over and over again that these games are a blatant cash-in, so if you paid real money for these games, the joke was on you. So, getting to the subject of this video, finally, why should anyone feel any different about Mighty Morphin Power Rangers Fighting Edition?  They got popular well after my childhood, and to me it looked hokey and cheesy for the sake of being hokey and cheesy. As a teenager, I said no thanks, but in the meantime I missed out on one of the better fighting games for the Super Nintendo. It's a perfectly good fighting game for either single or multiplayer, but especially multiplayer where there's eight characters to choose from. Unfortunately, in story mode, you only get two characters, but still, the game is pretty good. The controls resemble Street Fighter, they're smooth and very intuitive, the same kind of deal with the D-Pad where up is jump, and there's varying degrees of punches and kicks from jab to strong.  The game makes great use of the art style here, which makes the special attacks look awesome. Even the little cutscenes are nice-looking, and the sound matches the content and the music fits the game. This game is very similar in visuals and content to Gundam Wing Endless Duel for the Super Famicom, but sadly that game never got released in the US. This is its closest match, though. Plus, Endless Duel usually goes for between $20 and $30 on eBay, where Power Rangers Fighting Edition goes for as low as $12. Not to slag on anyone, but when I tried to look up other reviews of this game, they were all like 12 or 15 minutes long. I mean, Jesus, do I really need to sit here for that long to tell you about this game? It's pretty straightforward. If you like fighting games, you'll like Mighty Morphin Power Rangers Fighting Edition. The goal of the review here is to say, if you've ignored this game because of the words Power Rangers in the title, I don't blame you because I did the same thing. But in the meantime, you're missing out on a perfectly good fighting game. It's pretty simple. It's pretty straightforward. It's to the point.  Where can you go wrong?  How can you go wrong there? Same here. Just ignore the title, but not the game. So go check it out.