What's everyone takes in here welcome back to another video today. We're gonna be opening up finally this stitch mystery box Which we got from Ottawa Comic Con Which cost $80 and this is heavier than the last one this could we do one that we did? So really excited to see what is inside but the last one didn't really have the value of $80 inside So hopefully this one has something more of value inside. So let's open it up and see what we did it gets So they did say just like the last one all of these are supposed to have between seven to eight items each Each of the mystery boxes the last one only had five I'm just gonna grab the first item, which I think you guys just seen But it is this Stitch plush kind of looks like the face of the original Funko, which is really neat But Ashley has like two or three stitch plush now That is still pretty cool, and it's actually a pretty good quality one The stitching doesn't look cheap. It doesn't look like a cheap one. It is actually Their brand as well the buckle down Which is pretty neat. So that's a pretty good item compared to the last one that had a plush of a sandwich for the screw We do one next up if you do have a really big Christmas mug. This is stitch with a Santa hat That's actually really really cool. Ashley is gonna love that nice blue inside It's really heavy, but that's a very large Christmas mug Next up we do have a keychain. This is the little Hawaiian flower That's actually a really good quality one just like the last one the screw you do keychain is the best item This is a pretty good quality one as well You do have just like the last video or the last box. We do have a wallets This is I think a little bit better than the last wallet though. You could see stitch perfectly That looks like a really good wallet Good quality It actually feels like a really decent wallet Next up if you do have a lanyard this looks like Lilo's dress design on it It's a cool lanyard So what do we have this is already five so this box actually had seven items in it, which is awesome Next up we do have the Lilo and stitch Unbreakable mini collectible cups like mini sharp glasses almost of different characters You have the evil one his girlfriend stitch and then the little doll which is neat And lastly you have this Disney Looks like little cloth masks you actually get three inside. That's actually pretty cool kind of unneeded now, but An official Disney product at least so at least this box we did get Seven items compared to the Scooby-Doo one which we only got five items in it And this one here has a lot more value in it You have a way better plush than the last one a huge mug Which actually is actually going to use a good keychain so I think this one's way better than the last one as far as the Quality of the items comment down below what you guys think compared to the Scooby-Doo box that we opened is this one way Better and do you think the value is there for paying $80 for this mystery box? Hope you guys enjoyed this video and I'll see you guys for the next one. Please take care. Peace


This is a Weird Darkness bonus bite. An unidentified flying object spotted in the evening sky over Liwardewahu prompted witnesses to call 911 on Tuesday last week. The sighting happened about 8.30pm. There are multiple videos of what appears to be a glowing oblong mass both in the sky and in the water. Officials from the Federal Aviation Administration say there were no aircraft incidents or accidents in this area at the time, but multiple witnesses reported seeing a large, blue object fall out of the sky and into the ocean. In one video, a woman could be heard saying, �Something is in the sky. What is that?� Mestina Sape told Hawaii News now that she captured the image at 8.26pm near Haleakala Avenue in Nanakuli. Not long after, a woman named Mariah spotted what looked like the same object passing over Princess Gahanu Estates. �I look up and then I was like, �Oh crap!� she said. �I started calling my husband and them because they were all in the garage. I was like, �Hey, come look up here. See if you see what I see.� They all said, �Yeah.� The 38-year-old says she�s never really been a believer in UFOs, but the bright blue object had them so intrigued they jumped in the car and started following it. �I don�t know what it was� she said. �This one was going so fast.� The journey ended less than three miles from where it began. She says they stopped the car on Farrington Highway in front of the Board of Water Supply Building after the object appeared to drop into the ocean. In one of Mariah�s videos, she can hear her say, �It went land in the water, whatever it is.� She described it as being larger than a telephone pole and says she never heard it make any sound. �We called 911, Mariah said, for have like one cop or somebody for come out and come check them out.� While officers were on scene, Mariah says they spotted a second light. �My husband went look up and he�s seen the white one come� she said. �The white one was smaller, was coming in the same direction as the blue one.� They lost sight of the object after it passed over a nearby mountain. Thursday morning, we asked Honolulu Police of Investigators figured out what fell in the water. �The spokesperson told us they didn�t have any information.� Meanwhile, FAA spokesperson Ian Gregor said the agency received a report from police Tuesday night about a possible plane down in the area but had no aircraft disappear off radars and no reports of overdue or missing aircraft. Although Mariah�s had a couple of days to think about it, she says she is still baffled by what she saw. �To this day, I don�t know,� she said, laughing. �If you guys find out what it was, I�d like to know, you know.�


Hey everyone, and welcome to the Cube's presentation of the AWS Startup Showcase, Analytics and Cost Optimization. This is season three episode two of the ongoing series covering exciting startups from the AWS ecosystem. I'm your host, Lisa Martin. And today we are excited to be joined by Cube alum, Ash Munshi, the CEO of Pepperdata, and Kirk Lewis is here as well, Senior Solution Architect at Pepperdata. They're here to talk about the benefits of real-time automated cloud cost optimization. Ash, Kirk, thanks so much for joining us today. Great to have you back. Our pleasure. Thank you. All right. Ash, tell us a little bit about Pepperdata and specifically some of the problems that it was founded to solve. Sure. So Pepperdata was founded, I guess about a decade ago now. So we're sort of an old startup. It was founded by a couple of people out of Yahoo who had actually played a pivotal role in developing MapReduce and the applications of MapReduce for large-scale computation. And as some of our audience may know, MapReduce then evolved, and then we wound up getting Spark, and Spark is now sort of the infrastructure for being able to do a lot of large-scale distributed computation, batch computation. Originally, the company's intent was to make MapReduce accessible in a number of different ways. So at Yahoo, we knew how to scale it. We knew exactly how to implement it. We knew all the tricks that were there to be able to do that. Other people found it really difficult to make that happen. So what we did was Pepperdata provided the metrics that were required to run something efficiently, how to monitor it, how to actually make sure things were running properly, and then be able to do that for near mortals and let enterprises make that work. The company raised a bunch of venture capital in that day and age, and big data was the cool thing, like AI is the cool thing now. So we were the AI of that day.  We started getting some good traction, and we had two components to our product offering. One was detailed understanding or visibility about what was going on. And at the same time, the ability to actually tune it and make it work. In those days, Cloud was still cloudy. We were still doing things on-premise. Lots of people were trying to implement this stuff on-premise. And so we focused very much on helping customers on-premise. The kind of customers that we focused on, because this was not for the faint of heart, were people who were spending millions and tens of millions of dollars on-premise building large-scale infrastructure to make it all work. And they needed two things. They need to know how to use it. And the second thing they needed was how to reduce cost on it. The how to use it part was the observability part that we built out. So collecting lots and lots of metrics to be able to show them what exactly was going on. And as a result, the company built a bunch of extremely good IP around large-scale collection of time series data and extended an open-source time series database to be able to do things that were massive scale. To give you an idea, the kind of data that we collected would be the equivalent of what Uber collects on all of its cars and all the movements of its cars today. And we were doing that roughly 10 years ago. Wow, so tremendous volume of data. Incredible volume. Over the 10 years, over the decade, since pepper data was founded, what are some of the concerns that you're seeing today from customers where cloud cost optimization is concerned? So, you know, we had this big transition. People went from on-premise to cloud. And at the beginning, everybody thought cloud was free. Why not? I mean, it's not part of your fixed infrastructure cost. It's part of my budget. Don't worry about it. Let anybody go and do cloud. Well, it didn't quite work that way because the clouds turned into a rainy day. And it wound up being that, you know, everybody was suddenly, oh my God, this is costing us a lot more than we expected. And the CIO suddenly got involved again in the discussion. And the CFO suddenly said, oh my God, everybody's budgets are going out of rummock. Let's go control the costs. It turns out that the stuff that we did on-prem for controlling costs was applicable to the stuff on the cloud as well. So in particular, one of the things that happened on the cloud was we took infrastructure that was happening in data centers and moved it into the cloud. So the big data stuff became essentially EMR. EMR is essentially MapReduce and Spark that we did on the old days in physical infrastructure now cloudified. So the same tricks that we were playing before on local infrastructure we could do, but we have one extra interesting dimension. And that dimension was the fact that we weren't constrained by the number of nodes that we had physically. We could add more. We could actually have this flexibility, this dynamic ability to grow and shrink capacity, if you will. And that added an interesting dimension to our optimization that we were already doing. So what we did was, we took what we did on-prem, which was extremely successful. And by the way, used by the world's largest companies, and I really do mean the world's largest companies, I think Fortune 10. And then we moved that into the cloud and then added that additional dimension of the scalability or flexibility that you could wind up getting. Sort of the dynamic nature of the cloud. We married the two together and we realized that we had a really good offering for people working on at least big data stack on the cloud. Then what we realized is the market was embracing Kubernetes and was also embracing microservices. And traditionally, the market for big data, which is sort of more batch oriented, was very different than the market for Kubernetes. There were two different places. There weren't, even the people running these things weren't the same. And with Kubernetes, we started seeing more and more of an intersection, more and more people saying, hey, why do we have two separate islands? Let's try to put everything together. So there has been a trend afoot, which basically says, let's take all this big data stuff, all this microservices stuff, and unify it under Kubernetes. So the logical thing for us to do is to say, hey, if that's happening, let's take a look at the optimizations that we do on the sort of the big data side and also move those into the microservices side. And that's exactly what we've done. We're about to introduce a set of products that essentially take our time-tested, proven, scalable capabilities with some of the largest companies in the world, move that into the Kubernetes space and offer the same kind of optimization, if you will. I think the distinction between us and sort of the other people that are out there is that while we provide recommendations for if you want to be able to use them and do them manually, what we recognize is that there's a lot of stuff happening on these machines. If there's a lot of stuff happening on these machines, it's impossible to do one-off tuning. And the other thing is that even if you do one-off tuning, you have this coupling thing that happens, right?  As a result, it's hard to actually get your hands on everything without having it happen automatically. And that's something that we do from a natural perspective because when we were dealing with large distributed systems, think about the world's largest spark jobs. You have these executors going all over the place, taking information, moving stuff around, taking resources, it's a big mess. God bless you if you think you're going to actually be able to do this by hand. We did this all automatically. Well, it turns out the same kinds of things happen in the other parts of the space as well. So that same technology we've now extended to make it so that you don't have to touch it, we actually recognize the inefficiencies that are happening dynamically, take corrective action dynamically, move things around in the right way, and then be able to deal with the scaling part of the auto scalers in an appropriate way so that not only are we taking advantage of the hardware that's already allocated, but also the hardware that you're going to allocate that you're growing into so that both of those two things are solved dynamically in real time. Got it. That means a big advantage of us. Oh, sorry, Ash. You mentioned pepper data being used by some of the world's largest companies. You mentioned Fortune 10, but I imagine that you see the imperative for cost optimization across companies of every size. Is that right? That's correct. We absolutely see it. Look, if I look, if I sort of go back a year, maybe a year and a half, even startups had plenty of money because VCs were flushed and they were just writing checks. So they didn't really care about that. What they cared about was market dominance. Let's get out there. Let's go do things. Bigger companies were starting to see issues, but financing was also relatively free for them. So there wasn't much of an emphasis on it. All of a sudden in the last year, year and a half, as we see recession coming up, bell tightening happening, we've got all kinds of people. I would say everybody across the spectrum suddenly says, hey, I can help you save money. Hey, I can help you save money. So what was interesting is we had a fantastic value proposition, but in some sense, we were the only people kind of talking in this tunnel. Because if people don't care about saving money, well, you can talk all you want. Nobody listens. All of a sudden, we were getting a lot of listens, which is great. So we're getting a lot of interest in being able to save money. I think business is coming back to what normal business should be, where you don't do everything because of cost optimization, but you make sure that you're actually managing your business in a way that it's not a runaway cost structure. So that's happening. That discipline is very welcome by us, and our mess is therefore lands on very receptive ears, and we are in fact seeing from the smallest companies to the largest companies a great desire to be able to go and save money. If you look at the whole saving the money spectrum, the large companies have incredible purchasing power. So what they can do is they can actually go squeeze. They can say, hey, you're offering me this on spot, but I wanted actually on my reserved instances. I want my pricing this way, et cetera. So they can structure their fixed cost on instances in a very, very nice way. Startups don't have the ability to do that. Some of the other people in our showcase here are actually offering effectively that type of service where you can actually arbitrage sort of pricing on the reserve instances and on spot and things of that sort. So you can lower your instance costs to be able to do that. Naturally, that happens automatically inside large companies where we play. But in both of those two cases, even if you've done that, you still have the ability to now take what you've got and run it efficiently. Running it efficiently is extraordinarily important. Either side of the coin you're on. And we make that happen seamlessly, automatically. So I would say, to a great extent, we're sort of complimentary to some of the other things that we're going to be talking about. Do whatever you can. Find the best instances. Do the stuff that you want and then turn us on. We're complimentary. We will give you extra savings above whatever else that has been contracted. Extra savings sounds good. I know we want to get Kirk in here for a demo, but just tell me really quickly about the pepper data capacity optimizer solution that's for Amazon EMR and Amazon EKS. What makes that unique? And then we'll throw it to Kirk for the demo. Certainly. So the core technology that does the cost savings is in fact capacity optimizer. It essentially has two components. One component is to deal with resources on the machine that you already are using. What we realize is that there is a gap between what is asked for in terms of resources and what is actually used dynamically. And as a result, that gap allows us to actually put more capacity effectively or create virtual capacity on a given node to be able to do that. That's our capacity optimizer. That same kind of concept happens when you try to scale. Well, when you scale, what happens is you suddenly say, I get five more instances or I get 10 more instances. Well, maybe you don't want five or 10 more instances. Maybe you want two. What's the right number to pick for you to be able to go and do so that you're incrementally growing that because your cost is a function of how many nodes you've got active at any given point in time. And so the fewer the nodes, the more packed and more efficient they are, the lower the cost is going to be. And we operate on both those dimensions. And that loosely speaking is our capacity optimizer. Kirk, keep me honest. Absolutely. And Kirk, you've got a demo here that you're going to share with us. So tell us what you're about to share. Certainly. So as Ash spoke of the components of pepper data, the way it operates is in two facets as well. We've got the components that operate on cluster to do the optimization. And then we've got a dashboard that shows you what's happening and shows you how much you can save and how much you are saving. So to start, I've got an example of our dashboard showing a plain vanilla cluster that is not being optimized. You can see components where it says capacity optimizer is not enabled. And I've got a nightly workload that runs and we're using 82 instance hours to run this workload. And this is what you pay for on your cloud bill that's going to say instance hours. So we're speaking the same language as our customers so they can see in plain English, what are we talking about here? You spent 82 instance hours to run this workload. And your core usage, meaning the compute that you spent was only spent at an average of 30%, it was only 30% efficient. And at peak, you were 77% efficient. And for clarity's sake, you're paying for 100% of the resources that you deploy. So this 30% and 77% are suboptimal. You'd like to be at 100% in a theoretical space, but you want to get as high as you can get. Memory is the same kind of story here where it's an average of 35 and you peak out at 91. And this comes from that lower level that Ash talked about where in, yes, you can have spot instances. Yes, you can get these boxes for as little costs as possible. But the applications that we're talking about here, especially for things like Spark and microservices, there's always that component where people will size what they need for that peak. And we know that there's always a usage curve there. So this chart that we have of instance hours is showing the auto scaler launching these nodes as soon as someone asks for resources. So I go from three instances up to 50 instances really quickly here. And I hover there and I do auto scale up and down as that demand sort of fluctuates. But the problem is there's always going to be that delta between what you've asked for and what you've used which quantifies the waste. So when you use capacity optimizer what does that look like? What changes? So now I've got an example of a cluster that is running capacity optimizer and you can see the full fidelity of what we're showing you here. Core hours saved. We've increased utilization across these nodes on average by about 50% for cores and about 40% for memory. The number of containers the units of work you were able to run was increased by about 30% and over 100% uplift at peak meaning there was over 50% waste that we found inside the running applications. And the big number here is we only use 35 instance hours to run that same exact workload. So there was an over 50% increase in the amount of work you got done and how we pulled that off if you look at this line here not only is your number of instances not increasing to 50 but it increases out a much less aggressive rate. And to Asha's point about how we augment the autoscaler what we did here is for every node that is launched and is running we augment the autoscaler in a way that says don't launch any more nodes until capacity optimizer packs that node to about 80% which is configurable but we shoot for about 80% efficiency at the node level. So when you do that the autoscaler doesn't have to run as many nodes to do the same work. So we can see as a product of that our average usage at the node level goes up peak goes up to 90 and 99% here. So really we've solved the problem of you know developers have to size those boxes that they're launching at peak but we know that there's always going to be some waste there but by doing things dynamically and sampling those applications every few seconds we don't have to do things like model the workload or restart the applications or retune applications. This all just happens on the fly and developers of course like it because we're not touching their code and the DevOps guys like it because we're showing them everything that's happening there's no behind the scenes magic here we're showing you what we're doing and how much you're earning or how much you're saving as a result. So that is how things operate at the node level at the autoscaler level and then at the cluster level and what we're doing now is we're serving a different audience in the realm of Phenops and the executives who are not so much concerned about the individual clusters they want to know what's the overall account look like what's the enterprise look like so this new UI treatment that we've done on top of capacity optimizer is showing me for example over the last 30 days what's the total compute cost across my organization I may have multiple cloud accounts and I rolled that all up here to about $170,000 compute cost I can see which clusters or how many clusters are managed by pepper data and we've got a couple that are not the potential savings here is savings that have not been realized yet and when I look at this next row I've got the total compute cost and then this breakdown of realized savings versus potential savings speaks to what is capacity optimizer doing and what could it be doing for you and from there when you see something like potential savings the next natural question is okay well how do I go realize those savings so we show you your top 10 clusters by compute cost and you can see the most costly cluster doesn't even have pepper data installed so this is an example where because these organizations are so dynamic someone may have spun up a cluster and not installed pepper data so we're showing you that as an opportunity even though pepper data isn't installed there yet and then on the optimized clusters I'll take for example this one is being optimized at a low level and when I click into it I can see this control plane for capacity optimizer where if I want to optimize more aggressively I can do so and the workload here and the metadata is a batch workload we know that batch workloads are sometimes not as sensitive to like transactional workloads in terms of the SLAs so I can move this up to high I can introduce the auto scaling optimization and I can apply this change and I can do that all from this panel without having to log into these machines and make changes so it's really great on the operational side and then my potential savings are from this point on because we are dynamic in real time we'll start to transfer over to this realized savings bucket so all of this happens without developers having to change anything without the DevOps guys having to get involved we do have support for change control systems on the back end so that things are also tracked but what we're doing here is we're serving that audience that wants to know at a high level you know how am I how am I doing in terms of efficiency and when I look at the product of this across the board there was that cluster that didn't have pepper data installed and I'll show an example here of a install it's very simple in that in this UI we're giving you what you need to cut and paste to install pepper data and you can see it's only three steps so there's not a drawn out install and as we mentioned once you're installed and you click apply on the button you start to realize those savings so this all rolls up from a simplicity standpoint to the FinOps and executive audiences now have this total roll up of realized savings across all of the accounts all of the different business units and then you get sort of a scorecard of which accounts are optimized or not optimized in a percentage wise basis and then you can see how their clusters are doing within their accounts so you get this entire view of the clusters rolled up to the accounts rolled up to the business units and you can also do something about it from here so with that it's really the entire spectrum from the low level to the high level to show how pepper data capacity optimizer is impacting and quantifying the ability to see and optimize what's being wasted in these environments Wow, it's a great visibility you can see core usage identifying waste with capacity optimizer I saw you mentioned core hours saved more than a 50% increase in work done and you get that whole holistic picture of what the enterprise looks like guys thank you so much for coming on the program talking to us about pepper data capacity optimizer and the huge impact that it's enabling customers to make we really appreciate your time and your insights Thank you and we want to thank you for watching and say keep it right here on the cube for more action your leader in tech coverage


Hey guys, what's up? It's Andy. Listen, I'm gonna ask you a question. You wanna be the top sales pro in the world? Do you really? You wanna earn a quarter of a million to half a million dollars a year? You might ask me, Andy, how would you make that happen? Well, I'm gonna show you in this video. I want you to pay attention. So I'm a visual person. My entire life in sales, I've always been a visual person. And as I'm here in my 10,000 square foot conference room, this is a training program that I put on that I would have given my legs for. Literally, cut my legs off when I was a salesperson when I was younger. But it didn't exist and it wasn't available until now and I've created it. And just like how I made 700 grand a year selling cars, I'm gonna teach you how to do the same thing. What I want you to watch is this video that's coming after me and put your eyes visually and vision yourself being here alive with me during this training program. Check it out. And there has never been ever in the history of time a program that's been like this. This right here is going to be the one program that would change your life forever. Did you see yourself here training live with me?  The difference between where you are and where you wanna go is the gap in your skill set. Come train live with me. Every month, I put on a live master closer seminar right here in Scottsdale, Arizona in our 10,000 square foot conference room. I wanna train with you. Shoot me a text message. Let me give you some information on it. If you can't make it this month, come next month. Look, it's a planned event. We make payment plans. We don't care how you get here. We just want you here. Shoot me a text message right now. Let me get you some information.<PHONE_NUMBER>.  Test me. I'll create the world's most elite salespeople. I'm doing it now. Let me do it for you.<PHONE_NUMBER>. Shoot me a text message. Time to level up, baby.


In this module, we'll talk about protein structure. We'll talk about the primary structure of proteins, secondary structure, tertiary structure, and quaternary structure of protein. The primary structure of protein is basically the sequence in which amino acids are linked together. We have already mentioned that amino acids are basic unit of proteins. They are the monomers. These monomers are linked together with one another through a peptide bond. Peptide bond forms when a water molecule is removed from the two adjacent amino acids, a condensation reaction. The carboxyl group of one amino acid forms a bond. The carbon present in the carboxyl group forms a bond with the nitrogen atom of an adjacent amino acid. This bond, when it forms, a water molecule is lost. So carbon-nitrogen bond is basically the bond that is holding two monomers of a protein together. This bond is called peptide bond. The primary structure, as I mentioned, is basically the result of these peptide bonds, the sequence of different amino acids in a protein molecule. The diversity of different combinations we can form by linking two amino acids is very great. We can take a simple example. If we want to link two amino acids together, we can have 400 different combinations in which we can link two amino acids with each other. As we have mentioned, there are 20 amino acids. So the formula for calculating diversity is 20 to the raised power N. N is the number of amino acids present in a protein. So if you are talking about just two amino acids, it will be 20 to the raised power 2, which is 400. If we are talking about a medium-sized protein, say 100 amino acids long, with this formula, it would be 20 to the raised power 100. This is a very large number. In fact, this number is so large that we don't have enough electrons in this universe. The number of electrons in this universe is less than this number, 20 to the raised power 100. The one thing I also want you to pay attention to is the red oxygen atoms and the blue hydrogen atoms. The hydrogen atom attached to this blue nitrogen atom can form a hydrogen bond with an oxygen atom, which is denoted by a red ball in this diagram. This type of bonding is important in formation of secondary structures. I will highlight these atoms that I'm talking about. Here is the oxygen atom that I mentioned, and here is the nitrogen atom, and this nitrogen atom is attached to the hydrogen atom. This hydrogen atom, the white ball, can form a hydrogen bond with the oxygen atom, and this is important in the secondary structure. There are two types of secondary structures, alpha helices and beta sheets. Alpha helices are present in fibrous proteins. The result from formation of hydrogen bond between the hydrogen atom attached to the nitrogen atom is a peptide bond and also the oxygen atom, which is attached to the carbon atom. The two types, as I mentioned, alpha helices and beta sheets, these are the two types of secondary structures. Tertiary structures basically is the manner in which the whole protein folds upon itself. This tertiary structure can have smaller domains which are composed of secondary structures, alpha helices or beta sheets. This folding of a protein upon itself can be stabilized by all types of different bonds that we have talked about, hydrogen bonds, covalent bonds, the sulfur, we talked about cysteine, sulfur atoms forming the sulfide bond, and also ionic interactions. The quadrary structure basically is more than one protein molecules coming together and forming a larger structure, multi-subunit entity with more than one protein components. Next, we will talk about certain features of the proteins in the next module.


You know, as we get older, we might feel like it's more difficult to do the things we used to do easily. But one of my main goals for my patients and all of you is to feel like the youngest version of yourselves. And a big part of that is supporting your muscles. So first of all, let's talk about muscles and muscle mass. One of the important things we're now learning about muscles, besides the fact that we need them to move around, muscles are metabolic organs. They are actually the main customers for the sugars and fats and protein that we eat. And they're ready to work for your energy production system if you use them. Now, what's interesting about muscles is that muscles are actually the main storage system for the sugar that you eat. The sugar you eat is used as a fuel in the form of glucose, but any excess glucose can go into deposits within your muscles called glycogen. So muscles have a large storage depot of glycogen. So that glycogen can be depleted when you exercise. Now, that's actually, believe it or not, a very good thing. So when your muscles are exercising, the first thing they eat is the stored glycogen that is in them. Once they use up that glycogen, then they have to find another alternative energy source and that energy source normally should be free fatty acids and or ketones. Those free fatty acids and ketones are actually one of the keys for long-term health in that during the time mitochondria in muscles making ATP from free fatty acids and particularly ketones uncouples mitochondria and makes them more efficient and makes them more plentiful. Additionally, we now know that muscles make communication systems hormones called myokines. Myokines actually are important for brain health for making neurons and the finding that exercising that people who use their muscles well into late life have better brains has now been discovered that these myokines, these hormones are really part and parcel with keeping a healthy brain. So the more you use your muscles, the more muscle mass you have, the better your brain is from this hormonal signal from muscles. Happy muscles, happy brain. Unhappy muscles, unhappy brain. We're beginning to realize why all of these super old people are actually active exercises. Now the good news is you don't have to become a gym rat late into life to do this. In fact, none of these super healthy super old people are gym rats. They get their exercise from walking, from herding sheep, from gardening, from going up and down hills, from not taking the elevator, from not driving their car one block. They walk, they move. My mother loved housework. She thought scrubbing floors was one of the most fun events of her day. Seriously. And my great-grandmother, who died two weeks shy of 100, lived in a three-story house. And her bedroom was on the third floor. And my sister and I just couldn't fathom why grandma wouldn't move her bedroom down to the first floor as she got older. Little did we realize she was the smart one of the family. And walking those three flights of stairs several times a day was one of the best things she could possibly have done. Now, so exercise is really, really important. And exercising as you get older is really critical. Please, please, please ignore all those commercials that tell you how this particular gadget is going to help you not to bend over to pick something up, is going to help you make it easy to turn on your TV or open a garage door. Don't be dissuaded into moving and keep moving. That's one of the secrets. Now, one of the big worries that you hear mentioned all the time is as we get older, our protein requirements go up that once you hit the age of 65, you dramatically need to increase the amount of protein you eat because studies of humans have shown that protein requirements appear to actually go up once we hit that magical age. But what's really going on here, I think, is deceptive. As most of you who follow me know, our gut surface area where we absorb nutrients is the same surface area as a tennis court. That's that big. But what you did know is through the years, the damage to that surface area from the lectins we eat, from the other disruptors that we eat or use, the damage is cumulative. So that by the time you reach age 65, instead of a surface area of a tennis court, you've got the surface area of a ping pong table to absorb the protein that you eat. Now, how do I know this? Because as a heart surgeon, I would see many people who had low total proteins and low albumins that we would operate on. And that low total protein and low albumin is a sign of risk from heart surgery. And it was typically associated with being old. So you would assume that these people need more protein. But hold on. When I started teaching people how to eat with these low total proteins and low albumins, one of the first things I did was actually lower the amount of protein that they were eating. And lo and behold, despite the lack of protein in their diet, their total protein went up to normal. Their albumin went up to normal. Why? Because I was taking lectin containing foods away from their diet. And I could measure that their gut wall was being repaired. So the advice that we need more protein as we get older is actually the wrong advice. What we need as we get older is to repair the wall of our gut. Remember, you are only as healthy as the wall of your gut is intact. And so getting the wall of your gut intact by removing gut damaging foods and other compounds is the key to having plenty of protein in your body. Not eating more protein is getting the wall of your gut repaired. So you want more muscle, repair the wall of your gut, move, and it will take care of itself. Now what about building muscle with intermittent fasting? Interestingly enough, there is a famous Italian cyclist study that looked at the effect of a standard meal at training table, and for three months, and looking at the effect of eating in different time windows. One group of athletes ate all of their meals within a 12-hour period of time. The other group ate in a 7-hour window of time. And despite having the exact same calories and the exact same training regimen, the athletes who ate in a 7-hour window maintained muscle mass, maintained performance, but lost weight, and lowered their insulin-like growth factor, which is a good sign of improved longevity. So you actually increase protein synthesis by limiting your eating window. Now one of the effects that's important to realize is that normally digestion takes huge amounts of energy, and it burns a lot of protein. So limiting the time of day, the amount of time you spend digestion, is actually one way to save protein. The other thing that's important to realize is that if you need blood flow to build muscle and digestion costs blood flow, 30% of all of our cardiac output after we eat goes down to our intestines. And so if you're trying to build muscle, the most important thing is to exercise on an empty stomach. And then, and only then, add your meal, add your protein, and your muscles will be far more receptive to the availability of amino acids and sugar if you exercise on an empty stomach and then eat. It's one of the easiest things you can do to improve muscle mass during training. Now many of you have probably heard about the idea of training with blood flow restriction. Well, believe it or not, that's the same principle. It's to make your muscles work when they're not getting blood flow, when they're literally starving for food, and then release the blood flow, and that's when they'll gobble up and grow. You can accomplish the exact same thing by exercising on an empty stomach and then eating. Believe me, they'll be starving, they'll be ready to accept what you have to offer, and you'll actually improve delivery of nutrients to muscles and make them grow. It's just one of the easiest tricks you can do. Now, does that mean you got to go to the gym? Does that mean you got to go for a run? No. You can do even little bouts of exercise and it will have the same effect as a long period of exercise. And I've written about this extensively in the energy paradox. You can jog in place for a minute. You can do crunches, you can do planks, you can do squats. I do deep knee bends twice a day while I'm brushing my teeth for two minutes each time. You can do jumping jacks. Put one song on your iTunes and dance around the room for two to three minutes. Just yesterday while we were outside with our dogs, my wife Penny decided to dance to one of the tunes that we were doing while I was watching the dogs. It's easy to do. She looked great doing it. She had a good time doing it. It's fun. Finally, the protein myth. You see all the time why, oh, I was a vegetarian and I had really bad health and I decided to add meat to my diet and this is what happened. Well, quite frankly, you don't need animal protein to get protein for your muscles. Tell that to a gorilla or a horse. They have more muscle than you'll ever have and all they eat is leaves. You don't need animal sources for protein. On the other hand, it is difficult for many vegetarians and vegans to get adequate protein into their diet because they're damaging the wall of their gut with lectin containing foods. You can get two grams of protein from any one ounce serving of vegetables that you can name. Try using low lectin healthy plant protein choices like pressure cook lentils. Several companies make pressure cook lentils and beans. Lentils are mostly protein and they've got 15 grams of gut friendly fiber in every cup. Hemp tofu is available. Hemp protein hearts are easy to obtain. Sprinkle them on your salad. Sprinkle them on your vegetables. Plant proteins in the form of nuts are great. They have anywhere from four to nine grams of protein, including all of the essential amino acids. Baru nuts and sacha inchi seeds have the highest proteins of any nut or seed and they're lectin free. They're now some great spirulina algae flax seed protein and hemp protein powder is available out there. So there's always an easy plant source to get protein into your diet. The other benefit of plant protein in terms of aging is there are certain amino acids in animal proteins that sadly age us faster than plant proteins. And if having a great health span is a good idea, then opt for plant proteins whenever you can. And just one final word of warning as I talk about in the upcoming book, gut check. As you know that I've been warning people about the dangers of a sugar molecule in beef, lamb and pork called new five GC. And the new book will give you a lot more to think about on why it might be a really good idea to avoid new five GC in your diet or to have new five GC containing foods where the new five GC has been eaten by the process of fermentation. And it's exciting part of the new book. Stay tuned. But in general, beef, lamb and pork are not your friends as a source of animal protein. On the other hand, wild fish and pasture raised chicken are better choices, but plant proteins win in most cases. More amazing episodes just like this one. Watch now. If you look at people who use a liter of olive oil per week in their diet and there are multiple long web people that use a liter of olive oil in their diet, they're skinny.


What's up, everyone? It's your girl Kenzie here from Layton Sports Guards, and I'm ripping a personal here for Karsten G We've got a 2021 Panini contenders optic football hobby box. All right, Karsten Best of luck. Thanks so much for your order Yeah, Volk, Volk destroyed my Holloway Isaac, I re-changed my mind. He will be going for that Alright, here we go. Yeah, I don't know who's gonna fight Volk next Wow, this destroys that way, class Makes me regret selling my 101 Volk David Montgomery, we've got rookie ticket of Jacob Harris Rams And we've got Hollow rookie ticket Shreya Lance Here you go Karsten, nice Lance On the rookie base and pink to 75 winning ticket of Devonte Adams Packers hit there. That is 11 of 75. If you offer the jersey number there For the Saints, we've got Super Bowl insert here of Marcus Colston So happy to see him have autographed Nice There is Let me look real quick. Sorry about that It looks like a scratch. It does not look like something that we can dust off right here where he signed You can see it pretty clearly. So I will send a box with this one for you Karsten If you would like to take that up with Panini, it does look like a little scratch out of the card itself Yeah, it looks like dust for a second, but then it just looks like it was just scratched And then we've got rookie ticket on card auto of DeWayne Eskridge for the Seahawks Two on card autos coming out of that box Nice Alrighty Karsten, that'll do for your box. Congrats on your hits again We will be sending an empty box with you in case you want to get that Colston replaced Thanks again. We'll get these right out to you