What's up everybody I mean we're Evan we are here on torque drift so this video is about that mx5 we are going to do our crate build today we have some crates stacked up but it is also about torque drift is listening and this should prove it so I know a lot of people have been complaining about the update where they couldn't control the car and now we have the ability to change our driving style from other versions so if this doesn't prove to you guys a torque drift is listening to what I'm saying listen to what you guys are saying listen to what everybody's saying this should prove it right here we got our stickers we got our decals and now we have different handling characteristics and driving styles so I know a lot of people are complaining when the update came out the last one about the driving well now they've listened they've given us ability to go back and use whatever driving style we want so like I said today is going to be that crate build on this mx5 and it's very very anticipated the red mx5 chilling right there and 490 horsepower to start off we're gonna do 23 crates see what kind of build parts we can get for this car so we're gonna just jump right into it and get this then we're gonna get into those those new those steering differences in the driving response aftermarket suspension forged motor sports coils those are definitely a good thing to have good thing to have some nice coils on the car getting some more aftermarket tires which you know I have plenty of tires but you know I have burned through a lot of tires recently but we don't need that many aftermarket clutch that's good but apparently the car already had an aftermarket clutch so we got money for it so we're gonna aftermarket angle kit it already has an aftermarket angle kit on it apparently so probably should check what parts car already came with from the when you bought it it knew it came with some parts but I didn't know was all that stuff already and apparently we're just getting all tires with this build so if we need tires we have plenty of tires aftermarket turbo that's a good thing to get we did not already have that on the car luckily so we got an aftermarket turbo a little more a little more power in there some more tires I swear all I'm getting is tires and it's actually very sad yeah I import turbo all right so now we're up in the game in the turbo gang so more flutter more more the flutter gang 40 40 torque box I call it torque box but you know and more tires so very sad build so far with just tires and turbos but I mean the MX-5 is pretty good out of the box so aftermarket ECU which it already has in there so I think this car is pretty much already aftermarket from out of the box some more money so this build is not going the greatest import suspension shock works drift so now that's good to have and a little bit more upgraded suspension sad that we got two suspension so far in this crate buying it incident but we're not we're not getting us some very good very good pulls in these sponsor crates right now and I'm kind of sad but I guess we're gonna be rocking a almost stock MX-5 import exhaust I say almost stock we dig it aftermarket turbo and stuff like that too some more tires so we have tires for days I could you know run a tire warehouse if I needed to we got more cash and our final crate pole is gonna be a aftermarket turbo which we already got so we did not get much good things out of this so we do have the aftermarket turbo we're gonna bump that up to 11 psi we did lose the ability to sell our parts which I believe they're gonna put back into this hopefully and then we're gonna leave that tune there and then we got an aftermarket exhaust excuse me and then we are gonna be rocking our aftermarket suspension or import suspension I think we're gonna leave it how it sits and just see how how she handles normally on the just oh look we've already maxed out our engine so we got to put our shock works suspension in the rear get some new likes going on up in the and some new reply so let's go check out the new likes and replies up in here exactly like full-lock car oh nice full lock 240 a lot of people are liking the s15 build so big thank you to all you guys and like in that s15 livery the new s15 livery if you guys haven't seen it this was the new s15 livery decided to drop in the game so we got a lot of a lot of big name players in here liking this and we're just gonna drop some likes and throw down some throw down some reply comments real quick to these guys big thank you to all you guys that are following me on the hub and you know definitely follow me on the hub if you are not already a follow me on the hub but I feel like you follow me on the channel you probably follow me on the hub so we are gonna get into all the changes that the steering driving like feel is oh we got a message where we gonna message from I gotta get to this what did he say what did he say so one of the big players of torque drift one of the highest influences in the hub right here big shout out to you Straya you know can unlock it let the daves know so they can fix it so I can start representing evil rabbit empire big big shout out to you Straya for being a fan of the channel big thank you for following me and all this in this torque drift endeavor I have contacted devs about my sponsor and they are looking into it why it cannot be unlocked because I know there's a lot of people including me that want to get it unlocked alright so let's get into the nitty gritties of these basically the steering changes so we're gonna go to Cali banks to test this car out and I believe right now we are on the smooth so we're gonna look how the biggest thing is is how the wheels react when you go into drift so this MX-5 build is not a very powerful build but we are gonna use it so if you look when I go in it pretty much it does a very smooth counter steer and there's not much steering input needed and this car actually handles pretty well out of the box except for angle kit lack thereof so if you notice the wheels are a very smooth transition and you can still counter out of it and still change the angle of the car via the brakes the handbrake and still have some steering left in the car so we're gonna switch it to the other one and show you what the other ones look like because if you guys haven't really messed with these it's definitely a good thing to mess with because some tracks you should probably change the way your car handles and controls so now we are on the aggressive style which I found is a little uh a little twitchier where it doesn't counter as much and you it's meant for throwing more angle and it's easier to flick the car a lot harder because it doesn't counter as quick so if you notice that the wheel changes are not as dramatic or they are dramatic in a sense of they're meant to flick the car harder and be more of an aggressive style so it gives you that ability to flick the car a lot faster so that's the aggressive style so I know a lot of people are going to be running the smooth because I feel like the smooth was like almost the original version in a sense and the aggressive style was more of the medium version I hit the wrong button so the final version is a version that a lot of people didn't like the unassisted version and that is because if you watch the wheels when we do this we're going to take the start stop light with us if you watch the wheels once I start drifting they're going to go straight to zero steer every time you get off the gas or every time you get off the steering so it's constantly going to zero steer because it's unassisted so that's why there was a lot of twitchiness because the car doesn't counter steer itself and uh that's why a lot of people were having problems with the twitchiness of it so I found that smooth is a very good basic for nice smooth flowy lines obviously hence the name smooth and the aggressive one is used for on some other courses where you need some uh aggressive steering or quick uh quick angle changes so that's what I found and that's what I find that works for me some people may like other things but I feel like we got this MX-5 built we should probably throw it into tandem and uh see how it does because uh the purpose of this build was to see how it does in a tandem situation and uh we're going to give it a go and see how it goes once we find somebody to go with us we are in grassroots one which is very weird for me to be grassroots but uh you got to start somewhere on these new cars so this MX-5 build we didn't get much we just got some uh suspension bigger turbo and ECU and stuff like that so we are kind of running bare bones very low horsepower MX-5 but if you guys watch the live stream with uh Panda and I we had a lot of fun in these lower horsepower cars and Fernan also took a stock MX-5 out and uh was just showing that you don't need a lot of power to make good runs and you know that's the biggest thing everybody thinks you need so much high power cars to make you know these amazing runs you don't really need them it helps but you know like right here Cali's beginning pretty much of bones not bone stock but very mildly tuned MX-5 67 000 points it's a very decent run I could have been a lot higher on the bank but I'm still getting used to the MX-5 so of course we're going to be doing more building on this MX-5 I do have a new project car that I am running right now that is kind of a secret car in the works um you guys follow the stream you know I was building a fernan killer because uh fernan that ice cold drifter that he is I need to build a car that I feel could uh go toe to toe against his uh car so we've been in the process of building a project secret project car that I have not revealed yet and we are going to be uh bringing that out probably in a live stream so many guys may catch it in practice when I'm tandeming with it um so if you guys do catch it you'll catch a glimpse of it so kind of a little slow little tandems right here but uh tandem's on the last here in the sem-x-5 so a lot of big things in the update I know there's a new sponsor sticky motorsports came into the game um I don't think the logo is fully loaded yet um and for the people that are messaging me about my sponsor I will get with the I have messaged the devs they are looking into why I've tried it many times to get my own sponsor no penalties at the jump drift it's it's been done but there's something in probably going wrong in the the data transfer or the coding or something in those situations that are causing it to not actually accept the sponsor along with a few other ones that I've noticed as well I have mentioned that to them the devs are listening like I said this episode does not prove that or the update doesn't prove that then you're hiding under a rock and you're not paying attention so we're gonna try one more tandem battle here in this mx-5 and then we'll do a full another build episode on this mx-5 when we just start throwing money and parts at it and try and get it up there maybe to a mad mic uh mad mic with that status for this car so a little quick update video for you guys on what I found with the uh steering issues this is a better run this is a nice run up on the wall a little left for break there oh this angle is gonna kill me please don't kill me oh we stopped drift because of lack of angle and also it went into the it went into a higher gear so I accidentally uh went up in a gear when I didn't want to otherwise that was a very nice uh very nice bank run I was very happy with that bank run actually but unfortunately lack there of power or being in the wrong gear kind of uh kicked me in the butt there so we're gonna try and chase this down see if we can't get a good chase even though it looks like he may have tanked the wall and then make sure you guys follow me on instagram make sure you guys follow me on the hub you guys have questions you guys are more than welcome to message me on the hub like I will respond to you when I uh log into the game and check the messages on the hub and stuff like that so it's very it's a very good thing to have the hub out here where we can see other people you guys are following me I've uh posted a few funny funny clips with you know the barrel roll incident and my new paint on the s15 when I reveal new paints and stuff like that it will probably reveal them either on episode here or on the hub or both and uh as well as videos and stuff like that torque drift is going to uh be getting better and better every single day that they uh that we play it so the more we play it the more we figure out the more they're gonna add the more things that will come into the game so we're probably gonna take this win if we didn't then he had a baller chase run and we will watch it um but that being in back and grassroots is very uh very very interesting to say the least so like I said some new updates steering differences and changes some uh new sponsors came into the mix uh top one oil I think is another one too um mx5 is starting to build here if we could stay tuned for another app you know build episode on this mx5 to continue the build on the mx5 so before we go out of here we're going to uh check the hub 71 people following us and uh I gotta drop or 69 people I'm following 69 people we have 71 followers so I clicked on the wrong so we're going to uh drop a follower of these guys thank you for following me on the hub influence is going up you guys are liking the content that I'm putting up on the hub so that's gonna do it here for this episode here on torque drift just a quick update video for you guys here on this weekend hope you guys enjoyed it more videos coming out next week live stream as well so if you guys want to come in live to end a battle with myself make sure you guys stop back to the channel and check instagram and the hub for all the information on what's gonna happen so until next time guys like thank you guys for watching I'm evil rabbit I'm out


Now, in this video, I'm going to share how much money I made selling my photos on stock sites last month. I'm going to go into the individual details of how much I made from each site, and then I'll go and I'm going to talk about which additional sites I plan to post to next month to try to increase my sales. Hi, my name is James Wheeler. I'm an amateur travel and landscape photographer, and over the past five years I've been uploading my travel and landscape photos to a number of different stock sites. So in the past two months, as I've been uploading different types of videos to this channel, it's become really clear that you like videos that talk about how much I've made from the different stock sites. So what I've decided to do is to start doing a video each month with a monthly earnings report of how much I make from the different stock sites. My hope from this is that you'll get a better idea of what it takes to start making money on the stock sites, as well as getting an idea of which of the sites are hot right now and which ones you might want to avoid. Now, if you do like this type of video, make sure you click the like button, and also go down and subscribe and hit the notification so that next month when I do my earnings report, you'll get notified and you won't miss it. I think it is really important to keep up on which sites are hot because over the years I've found that some stock sites are hot and they start, you know, revenue starts going down while other stock sites, the revenue will start going up. So by doing these videos, I'm hoping I'm going to be able to help you to see which sites you should be investing your time into uploading photos to. Now, without further ado, here are my stock sales from last month. So if you saw my last video, it'll probably be no surprise that Avento Elements was my top site last month. And if you haven't seen that video, just stick around until the end. I'll put a link so you can go and watch that one afterwards. Now my second biggest revenue is my own website, souvenirpixels.com. Now I got one sale, which was a commercial license for a full-size image, which probably means they're going to use that image for print. Now I run my website on SmugMug, and SmugMug isn't the cheapest way to run your website, but I do find it's got the best e-commerce platform for photographers. And this one sale that I made this month will pay for my SmugMug subscription for pretty much a whole year, so I think it's worth it. Now next is Shutterstock. Now I had a really good month on Shutterstock. I used to make $100 plus a month every month a couple of years ago, but I've seen a pretty steady decline in Shutterstock over about the last year and a half. And this year to date, I've been lucky to make more than $50 a month, so I'm pretty happy with making more than $100 in a month. Now the main reason for this though was I had two pretty big photo sales. I had one single photo sale that was $32, and another one that was $10. So $40 of that $100 came from two photo sales, but I'm really hoping that keeps up over the coming months so I kind of see a bit more growth at a Shutterstock, more like what it used to be. The rest of the sites were in line with what I typically make, so if you add them all up, I made about $630 from my photos last month. Now I'm pretty happy with that because this is a hobby for me, so $630 in passive income for a month is pretty good. Now if you're just starting out $630 from 12 different sites in passive income, probably looks really good. But this type of video didn't exist when I first started uploading to sites. So I had to upload to a lot of different sites to find the 12 that I would actually make money from. Here's a list of 16 sites that I spent time uploading my photos to and didn't make a cent last month. Now some of these sites like 500PX and Find Art America have made me money in the past, but a lot of these sites, I ended up uploading my photos and never really got much of return from them. Now if you've gotten better results from any of these sites, I'd love to hear about in the comments below. I've had a lot of people tell me that they do really well in Alamy and I've started to upload my photos there more, but I'm still getting no lock. So I'm not sure why that is. Maybe buyers in Alamy don't like my particular types of photos. But I'd love to hear if you have success on Alamy, what you think I could be doing differently to make more sales on that site. Now over the next month, I'm planning on trying a couple of different new sites. The first one is one called Canva. Now I heard about this site because they purchase Pixabay and Pexels, two other sites that I do upload my photos to. And so I went and signed up for a contributor account and I uploaded a number of photos. So we'll see if I get any sales from them next month. Now I used to be pretty active on a photography contest site called Viewbug a couple of years ago. So I've got over 200 images already uploaded there. I noticed that they recently allowed you to start selling your photos as stock from that site. So one of the other things I want to do this month is just go in and add all of those photos to the marketplace. Now, unfortunately, it looks like you need to go through each individual photo and add it to the marketplace. So it's going to be a couple hours of, you know, mindless pointing and clicking. But I think it'll be worth it to see whether I can make an additional revenue from Viewbug. The third site that I want to focus on is Avento Elements. Now I've only got 14 photos up there right now. So what I want to do over the next month is go and take some of my more recent photos and put them up on that site. Now I've got a full video on how Avento Elements became my number one site for revenue, and I'll put a link to that site over here. But before you go take a look at that, make sure you subscribe to the channel and turn on the notifications so you won't miss my next earnings report. Best of luck selling your photos online.


Hi everyone, in this short tutorial, we're gonna talk about that how to swap two values in Python programming language. For example, suppose that we have variable X, which has value 4 and that we have variable Y, which has value 1. Now we want to swap these values so that variable X is going to have value 1 and variable Y is going to have value 4. We can do that very simply in Python language. First we're going to assign the value of variable Y to the variable X. So in this case now X is going to have value 1 and at the same time we want to say that now variable Y is going to have the same value as variable X, which is in this case is number 4. Now I'm going to check if these values have been successfully swapped. X now has value 1 instead of number 4 and Y now has value 4 instead of value number 1. So in Python we can easily swap the values of two variables. That's all in this tutorial. Thanks for watching. Don't forget to subscribe to my channel. See you in the next video.


Freedom! Today I wanted to talk to you about something very important. Right now, I am receiving a lot of feedback from the community, especially since announcing the new PR role that I've taken. And I've gotten one very important tweet that I want to share with you and hopefully explain and maybe even hopefully change a couple of hearts and minds. This tweet was so long that it actually had to be delivered in a picture. It is from LinkedChaos. LinkedChaos writes, Freedom! I'm not sure if you're going to be able to see this because you're going to get loads of tweets like usual. But I'm not happy as a partner channel. But I do think that you guys as a team will be able to improve the performance of your channel network. How? Let me get you some feedback on your ideas. First off, thank you very much. We're always very happy to have feedback of any kind. Positive, negative. Even some comments that we initially think are just troll comments turn out to be very valuable ideas. And we thank you and everybody else who sends them in. Thing number one, lack of ideas.  Are these really going to help our channels develop even further? Your promises are always broken and are never fulfilled, like every other network out there. If you want to be unique and help us grow even further in our YouTube careers, start making freedom-worthy ideas that will get us interested, not in views or subscribers. That'll make us seem very salty for views. This isn't even funny at all. And also mentioned in one of your announcement videos, not impressed at all with the lack of thought in your speeches. Couple of things I'd like to point out here. Everything that we announced on this channel is designed to meet one of multiple goals. Sometimes that goal is to help you. Other times that goal is to help other people. Sometimes that goal is to take on a market that we're not currently in or one that we are developing. So for instance, when you see something on the channel about cosplay, that might not be relevant to you, but it is relevant to a brand that we are hoping to launch sometime in the future. Also, it's something that people can identify with. If they're a gamer or if they enjoy doing cosplay or maybe even just once a year if you want to keep all these ideas in hand for Halloween. Also with, say, my tech series on impulse. Again, that might not help you, but it is accomplishing one of many goals. It allows us to help our gaming crowd by giving valuable feedback on these products. And it also helps us get information out there for these developing markets that we may look into in the future, like tech reviews. It's accomplishing one or more of these many, many goals that we have set. Sometimes we're not that great at communicating what goals it is we're looking to accomplish in each video. And it does cause a bit of confusion and for that, I and freedom are truly sorry. It is something that we are looking at doing a little bit better in the future, communicating this in a way that will help you understand the reasoning behind making the video. As far as can something help you grow specifically? Many of the advices that we give on this channel can help pretty much everyone. Because some of this advice, some people can say that they've heard it a million times, I can tell you from experience that sometimes all it takes is for someone to hear the same advice they've heard a million times in a way that hits home for them, for them to understand. So yes, some advice is going to be restated multiple times, but the advice that's restated multiple times is restated because there's a large portion of the community who is not yet understanding and taking that advice and running with it. If you are the person who's already taken this advice, then that video is not for you. It's important that you understand that we have several hundreds of thousands of partners and sometimes there's going to be a video that covers something that maybe you've already mastered. That might be a video you want to skip. However, you might also want to think about not skipping it because we might mention something in a way that you understand better than you've ever understood that piece of advice before. She goes on to say, the development of the ideas. Let's quickly think about the improvement and the development of your current ideas. Do you really think that it will be a lifelong thing?  Don't listen to us at all. You need to start thinking about developing projects without our insight to make sure that it's a surprise and of course to make sure that we know that you're working on it. You will... You need to think if... This is where I'm a little bit confused.  It's your choice at the end of the day. Just think about the idea. Leave it or develop it. I think I'm following you here but I'm not sure if the image I'm looking at is corrupted. The sentence structure is a little bit weird but we're going to try to move forward because I think I understand. On one hand, you tell us that we shouldn't come and we shouldn't present an idea until it's in its fully finished state and I understand that. I get that. Fully polished ideas are great and I would love to only present those. However, this is freedom. We believe in the free flow of information. As soon as we start work on something, we believe that our partners who are for all intents and purposes, our financial investors believe... They should know about this. See here at Freedom, we don't do things like other networks. We don't believe in hiding things behind closed doors until such time as we deem it necessary to share. That would lead to a lot of very sluggish development. It would lead to a lot of very nasty things that I don't even want to list in this video. I do understand that freedom has some projects that either haven't come to light or maybe we haven't done the best job of communicating that we are still working on and why these projects have been delayed or whatever the case is. And that's one thing that I'm looking to hopefully help fix in the coming months. And yes, it will take months to get this right because if it's worth doing, it's worth doing right. So in short, honestly, I don't think that we're going to stop presenting ideas before they are 110% right because as our financial investors, you have a right to know and you also have a right to voice your opinions, voice your concerns. That's the truth of the matter. Linked Chaos goes on to say, this is not a rant. This is a very important matter. I hope you guys understand what I'm coming after. You need to think about things more carefully. Anyways, peace. Linked Chaos, partner with freedom. Linked Chaos, thank you so much for reaching out on Twitter and voicing your concerns. These are things that we are looking into and they're not things that we take lightly. Hopefully after seeing this video, you kind of understand a lot of this and you kind of understand where we're coming from. You hopefully understand how we feel about this and why we feel the way that we do. This isn't just random chaos. This is the best way to do what we're looking to do, which is serve you and people like you even better. And when I say people like you, I'm talking about YouTubers, content creators. And if that means that every so often I have to make a video just like this to help people understand where we're coming from then so be it because we are here for everybody because this is freedom and your look matters most. Anyways, till next time, like, subscribe, comments, please.


Great. So I'd like to welcome everyone to the privacy and tech and AI model building. So a little bit of additional information. So before we get started, so I work over at doc.ai on the head of edge infrastructure. doc.ai is a Silicon Valley based digital health company. We build mobile health products that have a, that work with AI technologies and algorithms, privacy preserving infrastructure. We work with a variety of organizations across the board and including very large healthcare organizations. For me, part of what I've done over at doc.ai is I started the federated learning and AI privacy teams, built up the initial teams around them. I also focus on infrastructure related things to help healthcare companies adopt zero trust and cloud native environments. I also do a lot of work in the open source space. So I do a lot of work with network service mesh, which is a CNCF sandbox project and is helping with lower level networking and zero trust networking and policy. And we're seeing early adoption in both telecom and enterprise. I've also worked in a variety of different areas across the board throughout infrastructure. So let's get started. So the agenda is we're going to run through a survey and the survey is going to start off with some AI model building technologies and then we're going to look at survey infrastructure technologies after that. And then we'll finish it up with a short example that uses some of these technologies. So the problem statement, so when you start looking at what artificial intelligence is, specifically how neural networks work, they learn to predict their outputs based on their input features. And so you have a set, if you look at the graph on the right, you have a set of input features that come in. They run through some set of network that network will learn things about the about the input dimensions and then we'll try to make predictions in the output. So this is generally a good property in that as you learn information about the input, then you can make better decisions about what the output should be. But this also has an impact on privacy. And the reason why is because you're literally learning to identify features about your inputs to predict your output. So if you're training a model that has access to sensitive data, or you're training a model which what you're effectively doing is you're training the model to recognize the training set, to recognize properties about the training set. So the reason that this happens is there's an effect that is called overfitting that happens within neural networks. So when you have a given input, the system is not immediately capable of generalizing right up front. And there are techniques you can use to help with that generalization. But what ends up happening is whatever input you send is going to be what identifies.  And this particular technique is a form of bias, it's a form of overfitting. And to make matters even worse, overfitted models aren't too much information about the individual inputs themselves. What this means is that if a model were to escape your environment and end up in the black market, then you don't have a way to remove that model off of there. And people can analyze the model you have and discover information about the inputs, which could be very sensitive data. So to give you a visual example, we have two lines in the center. And each of these represent two very simple models. So we have the straight line, which is a more generalized predictor. And then we have the dotted line. And the dotted line is something that has been overtrained. And so the question then is predict where the blue and red, sorry, where the blue and where are the squares and where are the circles? So if you look, you can see has had the line squiggles around. You can see where it reaches out to encompass a circle. You can see where it reaches out to encompass a square. And if we were to populate this with the initial training set, you can actually see how this particular model has overtrained. And in this scenario, you can see the linear, the line version, the solid version is still predicting, is still giving you information about the overall data set, and but is also giving you less information about any individual. So this means that the very first thing you should do in terms of protecting your, your information is you should reduce your over fitting. And so there's several techniques we could which you can use. This is not a full survey of how to reduce that that form of bias. But there are tools that are very well used throughout throughout the industry that can help you get started. So the very first thing to realize is that smaller networks learn less information. A network that has 10 nodes or 100 nodes is going to learn a lot less information than a network that has a million nodes or 10 million nodes. And so the first thing you should do is try to work out what's the smallest network that I can provide that still gets me a good prediction, but, but isn't too, isn't too large so that you're not learning too much information about the, about the specific entries within your, your data set.  So that that node will, will learn less information and will generalize a little bit better. You can also train on more data. So if you train on a small data set, you'll learn details about that small detail, that small data set, but you train on a large data set, you, depending on the bias within the data, of course, you learn, generally learn less information about any single, about any single entry. There's regular, your regularization techniques that you can add in that help suppress some of some of these overtraining. And of course, you also have things like data augmentation, the ability to add additional noise into the system in order to help reduce some of the bias. Now these are not the only things you should do and we'll get into more rigorous techniques shortly. So another problem that, that we have as well is sensitive access to data. So most models are developed on centralized data sets. So imagine you're creating a model to try to detect some form of cancer. So the way that it typically works is people will gain access through, could be through hospitals, could be through research organizations that have collected this information. And they'll centralize all that information into a central repository. And that central repository, which this is, which could have sensitive information is, is then provided to a small group of researchers who then can run their AI model training loop. And so you can see in the environments on the right that the centralized data and the AI model training loop are both coupled closely together within the training environment. So one of the questions that comes up is what are the parties who own the data do not want to share the information to the central group or to the central authority? So one technique that we have to, to work with this is to use something called federated learning. And federated learning allows you to develop a model without having access to the model directly, but instead you work in coordinate with lots of other agents so that they each train a small part of the network and then they send you back updates. So if you look in the graph on the right, we have three stages. We have the first one is the model. We have a central model that has some initial predictor that gets pushed out to a remote set of agents. Those agents have some data that they can train on. Each of them does their local training loop and produces a, produces a new model. Those, the updates to those new models get sent back to the original, to your repository. And that those models then become aggregated. They become, they can be ensemble together. They could also be averaged out. There's various techniques you can use to work out how do you want to join them. But what ends up happening is that there's a way that these models can get joined together to produce a single model or a single set of models. So this helps, this helps with the issue where you might have sensitive data that multiple organizations don't want to share directly with another organization for a variety of reasons. And so it still allows you to gain access to train on these models across multiple organizations without owning the data itself, without ever having access to that original data. But even then that's still not enough. So can we, one of the things in this problem is that can we model a population without modeling the individuals in the population? Because if you recall, you still have this problem that exists. You still have this line that can get created that still exposes information. And federated learning by itself doesn't solve that, although it is a piece of the step towards removing access and preserving privacy. So again, phrasing the question again, can we model a population without modeling the individuals of the population? So if you have a sensitive question, a sensitive question to be something like, do you have any history of mental illness in your family? You get a complex answer because there may be legal and social implications. The same may may be about drug use in the past or criminal history or so on. So these types of pieces of sensitive information, if you ask this question to a person, they may choose not to answer it and you get bias from people who are more comfortable answering it or they may lie about it because they want to participate and being seen not to participate, they feel can give away information about themselves. So they would talk to lie instead and add additional bias into your system. So one of the techniques we can use to help with this is a technique called differential privacy. So differential privacy can be used without machine learning. And what we do is we add noise to both the inputs and the outputs. And so I have a very simple example. Let's take that same question that we had before and about whether you have a history of mental illness in your family. And you were to ask this as part of a survey, just like a written survey. And if you just ask the question, you'll get bias. So what we can do is we can put a person into a private room and there is a non-biased coin that is there. Maybe the user even provides the coin themselves so they know that it has not been tampered with. And so what they do is they start, the instructions say, you toss the coin. If it's heads, you toss the coin again, which erases the initial answer, and then you answer the question. If the first coin toss tails, you toss the coin again. If it's heads, you answer yes. If it's tails, you answer no. What this allows us to do is it adds plausible deniability into the answers. By plausible deniability, what this means is if you ask the person who filled out the survey, hey, you answered yes. That means you have this particular condition or you have this history. The person can say, oh, no, I answered the coin toss. So it gives them the ability to not provide information about themselves directly, but it still gives you the ability to find something about the overall population because you know the probability of the coin toss. And you are able to then model that into your system. So it turns out that this technique, you can also apply into machine learning. You can apply noise to the input. You can apply noise to the output. You can cap the learning rates so that you don't learn too much information on a single pass. And you're able to then quantify how much information has been encoded into the system. And this actually turns out to also help generalize your model because you're not learning about information about an individual user in the same level. So another technique that we use as well that is related is secure multi-party compute. So secure multi-party compute is where you have two parties who want to collaborate but do not want to share information. So what happens is the data is converted into multiple components basis. So in other words, A gets turned into A1, A2, B gets turned into B1, B2, and B3. And in this graph on the right, we have three separate organizations. We have one organization that owns all the circles. One organization that owns all the squares. And a third company, which is a trusted third party, is the diamond. So what happens is that the components, these component spaces, these A1, A2, and A3 are produced from A and they get sent to, each piece gets sent to each party respectively. The same occurs with B. And they all perform the same computation on the data. And what happens is that the center graphs, the center parts, like the one that's performing that function F, is not able to reason about what is A from A1, what is B from B1. The same is true with the square. And the same is true for the diamond. But then once you combine the information back together, then you can get the result of of the process without having shared the original information itself. Now, there are some limitations to this technique. In short, if you have any form, and this is more for some of the mathematicians in the room, if you have anything that is nonlinear, then you need to be very careful with this technique. It tends to break. What this means is that, from an AI perspective, you're able to perform the initial work on the graph. You're able to do the multiplication and the addition. But if you tie in something like a tangent, then your output is likely to break in that scenario. But branch predictions still work. So this means that you're able to perform your predictions as long as you stick to things that are linear. So this does limit some techniques that you can use, but it's still a powerful technique that when your problem fits this space, then it is a tool in your box that you can reach for and use. So going on to some of the infrastructure work, there is an environment called a trusted execution environment. And so what this is is, if you look at traditional containerization techniques, you have virtual machines, you have containers, Linux containers that have come up, and all of these are centered around protecting the host, the host operating system from the guest. And so that guest one would, in most scenarios, would have limited to no capability to influence guest two or the host. In a trusted execution environment, it's about protecting in the opposite path. It's about protecting the guest from a host. So effectively, trusted execution environments are containers which protect the guest from snooping from the host. The way that this works is that there is a hardware, there's some new hardware that's gone into the latest set of chips, so you have Intel and AMD and ARM and so on, have a way to encrypt the memory of the process. So you're able to create what's called a secure enclave. And the secure enclave you can then deploy software into and keep it separate from the host because the host and each of the guests are all encrypted with a different key. And it minimizes the performance impact because you're not doing the computation on an expensive key and it's hardware accelerated in its encryption and decryption path. And the keys are typically stored in the processor itself or in a trusted chip that's alongside the processor. So what this allows us to do is that assuming you can attest the guest that you're deploying to and guarantee that it is in fact running in a trusted execution environment, it means that you can ship a sensitive workload to a cloud environment and have some protection from the host being able to inspect what's inside of them. There is also another technique that's coming around. And if you look at how systems are defended against today in your security model, you tend to have something called perimeter defense. The idea behind a perimeter defense is if you look at the top graphic, you have a trusted network with a workload inside of it. This trusted workload is needs to communicate with a second workload that's in a different network. So what ends up happening is typically these, there'll be a secure connection between the two networks that's established and that's that connection will be defended by putting a firewall between the two networks or they may have a VPN that allows one network to communicate with another network. So one of the problems is if the attacker enters the trusted network, then they're typically able to access systems within that trusted network with very few limitations. And so we see these types of attacks go on quite often and there's some very famous attacks that have occurred where the operator has had some web service that's exposed to the internet and the attacker will end up breaking into that web service. And once they have access to that web service, then that web service straddles both the internal and the external network. So they can then scan the internal network and find databases that exist within that and start asking, start to extract information. And there's some very high profile and we'll give names in this scenario with some very high profile attacks that had to this particular profile that have led to very significant breaches throughout the industry, both within healthcare and out of healthcare. So what Zero Trust does is it's a different way of thinking of how to perform security where the idea is to minimize your overall perimeter to the smallest thing possible for a given set of workloads. So we're not saying there's two trusted networks, instead we're saying I have one workload that needs to talk to another workload, let's limit the communication to those workloads that are involved regardless as to what network they're in. And so the first company to implement this at scale was Google. So after they were, if I recall properly, after they were attacked by a advanced persistent threat, they decided to move over to this approach. And the way that this particular approach tends to work is every workload receives some form of cryptographic identity. It's, you can think of it like a certificate in a web server, when you visit your bank, it has a cryptographic identity. So it's the same type of cryptographic identity, but assigned to an internal workload. And when two workloads communicate with each other, they have to prove to each other by showing their certificates who they are before that secure connection can occur. Once that secure connection is established, we can then control the communication between them using declarative policy. And declarative policy is basically describing what workloads can talk with other workloads, what messages can you send across the, across the wire, as opposed to a more imperative approach, which say these IP addresses can talk to these IP addresses over this port, which, which becomes difficult to manage at scale. And it is how people do it today, but it's a, it's a very hard problem to, to scale up in, when you're dealing only with IPs, because there's not an implicit relationship between an IP, or rather there is an implicit relationship between an IP and an identity, rather than an explicit relationship. So this allows us to decouple and it allows us to work in more edge environments, cloud native environments, groups, groups like, like Kubernetes, where your workloads can spin up and spin down quite quickly. The IPs that receive change on a, on a regular basis. And they get, they get rotated amongst different workloads. And so this allows you to, to focus on creating your identity based upon what the workload actually is, rather than on an underlying detail. So preserving privacy itself, what part of the reason why this is important is because we have a whole chain of things that need to, to occur. We have, when you're, when you're building out a AI, we, or you're building out a model, you need to know, is where I'm grabbing the data from a trusted environment? Am I applying the right type of, of, of privacy into it? Am I adding things like differential privacy? What type of communication can I have if it's like a federated learning example? How do I, how do I know that I'm talking to an entity that I, that I trust who's, who's remote? So these type of, these type of models, you can't just focus on, on one layer. You have to focus on all of the layers down from the hardware and, and what's running all the way up to the actual processes themselves and what's, and what's running, and what's running on top of them. So this means that you have to have cooperation from your, from your data modelers. You have to have cooperation from your, from your infrastructure team, the people who are building out the pipelines and the vendors who are selling you the, the hardware that this stuff, that this stuff runs on. So in terms of, in terms of privacy, we've applied these particular systems to several of our, of our products that we have, but we want to focus specifically in this example on bottom left with, with Passport. So Passport itself is a secure application that is designed to allow them to have an employee dashboard that is designed to help teams get back to a shared physical space while simultaneously preserving their privacy. So the way that it ends up working is that there's a series of questions that are, that are asked, that, that are sensitive, but are necessary in order to protect the population. And what we do is, is these questions get pushed to the phone in the same way, use the same heuristics that a set of heuristics. The questions get pushed to the, to the phone and we, we then work on that on those set of questions. We never send the the information to the, the result of any given question to the, back to the central location. So the, the sensitive information stays on, on the phone. And if they've, if they've answered all the questions in a particular way that demonstrates that they are a safe candidate to come back in, then they report back saying that, that they've replied successfully with to each of the question in a way that is, that determines that they are safe and or low risk, I should say, rather than safe, that they're low risk. And so we have, this gives us the, the, just enough information to report back to the employer and to approve the, the person while simultaneously not giving the employer any detailed information about what goes in. So it's the sharing a minimum quantity of data back, sensitive, sensitive information itself is not transmitted. And the end result is a cryptographically signed QR code that exists on the phone that they can then show and scan. It says basically, the effectively says, yes, I answered this, I answered these set of questions and have, and I've attested that, that the person is low risk. So on the backend, we also make use of, of a zero trust environment. And so each, each of these connections in the internal system are based upon, are based upon Spiffy and Spire. So Spiffy is a CNCF project that describes a set of, like, how do you give out these identities? How do you rotate these identities? And Spire is the reference implementation of the Spiffy protocol. And so it's, these, both of these are CNCF projects, which is the, which is part, the same organization, which also manages Kubernetes. So they're, they're both, they're all sibling projects to each other. And so Spire does the actual work of handing out the identities to the workloads, verifying information about the workloads, what images they're running, what systems are they running on. And then open policy agent is a system which you can write declarative policy. And that declarative policy is human readable and machine readable. So you can say this workload is a lot, this application is allowed to talk to this database. And I want them to prove who they are using the identities I received from Spire. And they're allowed to send these messages. And so you can specify the interactions using open policy agent and open policy agent will then read the, the input from the requests. And then we'll give you a decision like, yes, this, I want to allow this or no, I'm not going to allow this. And here's why. And it gives you that explainability. And finally, these, the information that is necessary to report back to the employer is then sent back to a, to a metrics and logging infrastructure where the employer can then look in a, in a dashboard. So as an example, it's trying to reduce the, the total amount of information it's sent. It tries to separate out components. The, the policy is separate for, from the application itself and is applied and is, and is, and is applied as a, as a, as a uniform thing that you can control with that with Spire giving, giving out the, the identities themselves. Like the application cannot ask for a specific identity. It gets assigned an identity based upon, based upon its properties. With that, as a recap, we want to make sure that we collect less data overall. So when you're building out an AI system or any other system that is collecting sensitive data, it doesn't have to be ML AI. These, these type of techniques also work in other environments or in other, with other, with other approaches. So you want to collect less data overall, try to collect less sensitive data if you can. Exercise, good processes for data that was collected. In fact, part of this is not just about the technology. It's also about your internal processes you have internally. If you have, if you have good discipline within your processes, then this discipline will reflect in your technology and in your, in in your choices and will help you identify clean lines of ownership and clean contracts. And all of this ends up applying good software engineering techniques will, will help you in this path and make sure that you, the same way that you have your features, consider privacy as a feature. Don't consider it has a secondary bolt on thing that you've added. It is a feature in and itself. And it's something that is cross-cutting other features design that design and prioritize that privacy in your architecture. And importantly, do not assume that the AI models or the data that you, that you trained on sensitive data is inherently privacy preserving. So one really famous example of this is if you look at the, the Netflix study that was done in, there was a, there was a Netflix prize that was released and people came up with some pretty, the idea was to provide enough information about what movie a user has watched and in order to provide better predictions. It turns out that this dataset, when you pair by itself, it was de-identified and by itself it was, it was pretty innocuous, but when you paired it with information from Twitter, from Facebook, from other social media platforms, then it turns out that there was enough information in there that you could then start to personally identify people and what movies they watched. Like a person might say, I watched a movie tonight and I watched this or this is my favorite movie and how they rated it. All of this information can be paired with other, with other things. And so if a model is, that is supposedly private, is extracted and is, and is leaked, make the assumption that people are going to try to pair this up with other datasets to try to reduce the, to try to reduce the overall privacy. The differential privacy that we discussed earlier helps a lot in this particular space because it turns out the, because you, because you, you have that plausible deniability that's built into it, it makes it very difficult to work out whether some, whether some spike in your, in your data is real or not. It reduces, it reduces that, that privacy leak through, through that property. And think about privacy across your whole chain. You have the models, you have your pipelines you build, you have in the infrastructure, people tend to separate their infrastructure into three parts. You have compute, you have the transport, the network and the storage. All of them have to be designed to work with each other. They're not isolated. They're all part of a, of a, of a solution or all part of a chain. Make sure that things are designed so that they have clean interactions with each other and can help each other. It can help, they can help defend against each other. Like on the compute side, you, you may have a secure storage and a secure transport. But if you have a, if you have a bug in your compute and the actual hardware, maybe there's some information that can get trans, that can be transported to the side, things like heart bleed or similar techniques that, that may come out in the future. And so how do you, how do you defend against compute? Well, maybe what you do is you only run processes from a specific user on a specific processor that's been dedicated to them for a specific period of time. And, and you don't co-mingle certain types of processes. And so like think of these type of problems. If you're working on data that's not sensitive, it doesn't matter as much. But if you're working on very sensitive data, start asking these type of questions as to like, what can go wrong in my computer? What can go wrong in my model? How can they, how can they defend each other and cover each other's weaknesses? And finally, strongly recommend that you engage with open source communities who are focusing on privacy in this area. So there are certainly multiple organizations within the machine learning and AI space that are starting to have a focus on this. And this is the tip of the spear. I'm also hoping that Linux Foundation, IEEE and other similar groups start to also invest heavily on not just the security side, like there's a new security organization within Linux Foundation, but also focus on privacy because security and privacy are, even though they're related, are not the same thing. Private security is very often focused around who's doing what, how do I make sure that only I'm unauthorized, that I can authenticate and authorize a user and defend, and defend things. Privacy is about how do I not learn or how do I not share sensitive information. They're both tightly coupled with each other. But they're, they're two separate orthogonal things that do have some implications with each other. And with that, I want to thank everyone for joining and listening in. And if you have any questions, feel free to ask here in the webinar while it's on. You can also reach out to me. I have my name here on the CNCF Slack. If you're not on the CNCF Slack, there's a link there on how to gain access for information about doc.ai and its products and what we're doing, or if you want to join us in, in producing or building some of this stuff, you can send us an email at info at doc.ai as well, and we'll be happy to answer any questions. Thank you very much. Okay, thanks everyone for joining us. We're going to go ahead and have Frederick answer a few questions here and share my screen. So in terms of questions, so me go ahead and read some of them. So one of the questions is what do you do to recommend to assure compliance of AI models with privacy regulations like the GDPR in Europe? So GDPR is, is a very interesting scenario. So I'm not an expert in GDPR, so I want to be a little bit careful. Please take, take in mind with, take my answer a little bit of grain of salt and get some expert advice in this area. My understanding with this from, from the privacy side is that if you have data that is, that originates from a user, you have to be able to track and be able to delete information from that user in specific ways. And there's rules on, on who can perform actions as a, as a processor of that, of that data. And so in some ways, there are some, some similar, there are, there are some similarities with how HIPAA tends to work within the United States. So like with HIPAA, if you want to work, if you want to share some sensitive information to have some processing done or you, you want someone to work in, in a specific way on some sensitive data, you generally have to, you have to get them under a, what's called a BAA, I believe it stands for Business Associate Agreement. And this means that you can audit, you may have to audit the, the organization in some scenarios, you have certain requirements in terms of how the data is passed. And there's a, a legal infrastructure that gets established between, between the companies. And I think that if, that with GDPR, if you want to play it safely, it's not just about the technology, it's also about the people. So we want to make sure that, that you establish those same type of things. They like, Hey, I have some sensitive data, and I'm going to provide you with that sensitive data as you can provide a service, set up that legal infrastructure so that you can ensure that they protect the data as well. And also that if they breach the, the agreement that they take responsibility for, for it as well, that they, they don't just palm it off back on you and say, Oh, we were just the, we were just the processor. And remind, you know, that the techniques that they used were, were not particularly, particularly good in that scenario. So I hope that answers the question properly. In terms of another question, have we experimented with homomorphic encryption? So that was, that's a fantastic question. I have experimented with homomorphic encryption. One of the things that I'm a little bothered with in terms of homomorphic encryption is when you start to do things at scale, that tends to have significant slowdowns. As far as I know, this is not really a solved problem. There are some excellent use cases for homomorphic encryption when you have smaller quantities of data. And I would recommend that you make use of those when, when you can, if the problem fits. But when you start training on very large quantities of data, then homomorphic encryption starts to, starts to have some, some problems there. And so my hope is that we, in the future, we have some advances in the industry that helps make, makes this a more, a more attractable technology. That being said, there, there are certainly areas where it can, where it can work out. And so if, if you experiment with it and you see that it, that it fits your, your problem, then by all means definitely, definitely use it. There's another question about insecure multi-party compute. Why is the function F not labeled F1, F2 and F3? Thank you for, for asking that question. It's a fantastic question. So what we want to, we want every function, we want to run the exact same function across all three datasets. So we're not deviating the function. We're, we're deviating the data that is sent to the function across the three organizations. So there's, there's actually a really important point because it turns out that you have sensitivity in the data. Most people focus on sensitivity, sensitivity of the data. But what if the algorithm itself is sensitive and can give away information about what you're trying to do? Then in that scenario, secure multi-party compute may not be the right choice for you because you're effectively giving your algorithm to two or three other organizations.  So, and that can, that can give you, that would allow you to deviate the function itself. Thank you for that question. In terms of this, in terms of the slides, yes, we can, we can post the slides. I'll make sure the Linux Foundation actually, they do have access to the slides, so we'll make sure they get posted out. Let's see, there is a, there is a question about Passport. Melissa or Marina, maybe, maybe you want to answer this particular one on Passport. Basically, the question is about what would the information the employer would be able to gather from the employee's responses? Would it be anonymous? Is it, of the help, what gets sent back? My recommendation is to ask, send an email to info at doc.ai and get you an accurate response because I'm not able to properly answer that specific question. Thank you, thank you for posting that. With that, I think that answers all of the questions. Other than, I don't know when the webinar will be, will be posted. I'll work with the Linux Foundation to, to get that out and we'll, we'll put a message on our Twitter when it gets posted as well. I can jump in here, Frederick. Yes, so the slides and the presentation will be posted to the Linux Foundation YouTube page and you can look for that in the next week. Okay, and I'm actually going to switch the host over to Marina. She's going to answer the Passport question. Oh, as I was just saying, that question probably would be best answered, answered by email. We can follow up in a bit more detail on, on Passports specifically. So the, the email to contact us at is info at doc.ai. Great. Is there any other questions? Okay. If we have no more questions, we will close out for today. Last call for questions. Okay. Thanks everybody for joining us and we look forward to seeing you next time. Thank you.


Hello I'm Ron Duke with Pyramid. Today we're going to talk about why you might want to consider going with the higher end archery optic. On the table today we have three different price point products. We have a Trophy Ridge opening price point volt. We have a middle price point black gold rush and we have the higher end black gold Pro Hunter HD. There are several reasons to consider the higher end optic. All three of these have features benefits and they all have their place in the market. What you're going to find is that there's this thing you're going to run into called axis and what the heck is axis and what are those adjustments. You're going to see anywhere from zero to one axis on lower end, two axis typically in the middle and three axis on the higher end. The higher end three axis adjustment is the easiest way to demonstrate this because you can see all three moving parts and it'll make a little bit more sense as to why things are adjusted. So inside of this black gold Hunter Extreme what you're going to see is there's actually a wheel on the side. That is a sure giveaway that you have a first axis moveable adjustment. This one is a micro adjust. What that means is first axis is up and down right. So on a lower end optic what you're going to have is you're going to have pretty much your bow sight completely fixed to your bow and then you're going to be moving your pins up and down individually. You'll see almost every archie optic has an allen key needed in order to move them up and down. You set your sights at exactly the distance that you want each one of them, practice with it and then you memorize those and you're good to go. On this one however you actually have the ability that it actually moves up and down so you can see this optic as I turn it it goes up and down. That's a first axis adjustable on the fly. This is a micro adjustment which means on the back of this what I can see from an archer perspective is I see this red arrow and that corresponds with what will come in the package is different distance tapes. So once you know the exact feet per second of your bow you will be able to tell exactly how far you're going to shoot based on each of these different tapes that you would take the sticker off and you would put it right on here. Once you have it set up you're ready to go. So the second axis if you see that's the relevancy of having this bubble level right here. Now what that does is when you take this into your archery shop or if you're mounting it at home is you want to make sure that when your bow and your arrow are level typically putting it in some type of a bow vise to be able to make sure everything is steady when you do that you want this bubble to be right in between the two lines. Now if you second axis is off you will actually see it a little bit off level the bubble will go to the right or to the left. Every bow look at the instruction manuals they all make an adjustment to the second axis differently right or left but essentially you want to make sure that that bubble is perfectly level. If you do not let's talk about the issue there if you do not have a bubble level and you have it set up you could possibly have it off a little bit different. Now what that's doing let's say you set your top pin at 20 yards your bottom pin in this scenario is actually going to go a little bit further to the right or to the left depending on how you have this off level. So the further out you shoot the further and further away from center you're going to be with your target. That's a huge issue in archery so make sure your sight from a second axis perspective is always level. You could just have a bubble buy one with a bubble level on there that's typically how they're sold. If you don't have one get one that attaches to it that you can remove so you know that it's level when you go out to the range and start sighting in your different distances. So we've covered up and down for the first axis we've covered canting it back and forth for the second axis. The third axis is actually the most unique and it's one of these things where I've heard two things in the archery industry one is people saying that you need the third axis because when it full draw you have power on your string that you don't have when it's not at full draw and so therefore it actually tweaks your bow a little bit sideways right or left. I've also heard and seen this in person on many occasions where it's perfectly level on your second axis you're shooting at the range target shooting you have it perfectly sighted in all your pins you go into a tree stand and all of a sudden you're shooting at an angle and for some reason you're thinking man I thought my pin was on that and it wasn't as close to a bull's as I thought it was. The actual answer might not be you it might actually be that's because you don't have an optic that has third axis adjustment. If you want that to all be corrected you need third axis so what third axis is is it's actually the angle of the face so I'm going to move a screw here and show you in a second but your face will move in and out and gives you the ability to then cover all range and all torque and everything for your specific bow. So I'm to put this allen key in here and hopefully you can see this it's very minor but if you watch closely you can see the angle of that face coming in and out and hopefully you can see that because that makes a huge distance difference in the distance when you shoot especially the further out you go and if you're shooting at 20 yards and under you're probably okay with first second or third I recommend folks to really consider spending the money on a third axis optic right out of the gate. I didn't do that as a hunter I recommend doing it because you buy one you buy the right one you can move it from bow to bow to bow which once you're comfortable with it and then you're not constantly buying different optics along the way and you have something that you know as you're practicing your form and going out there you know that your optic isn't the culprit you know that okay maybe my form's a little bit off or there's something else I could have windage I could have other things but I have everything with my bow set up there's no funniness when I'm shooting at shooting down versus shooting straight on and just to be clear there's nothing wrong at all these are very second axis adjustments first axis starting optics they're all very solid I've taken dozens of deer with each of these different optics so it depends on you I'm just sharing some information that I found throughout the time that I wish somebody would have told me about the third axis earlier on in my hunting days and I probably would have spent the extra 100 or 150 dollars to get myself that extra adjustment just to make sure especially starting out that everything is set up right I would encourage you to check these out on pyramidair.com I'm Ron Duker remember to like comment and subscribe I'll see you next time


Hi friends, welcome back to my channel and welcome back to another weekly meal prep on WW. I am super stoked to share with you my recipes for this week. You are going to see my breakfast, my lunch and a dessert item that I am so excited to have. So if you want to see what is on my meal plan for the week, all of the recipes, step-by-step instructions, then all you have to do, keep watching. For breakfast this week, I'm going to be making copycat Starbucks egg bites in my Instant Pot. I am so excited I've been wanting to make this recipe for a very long time. I love these because all you have to do is change the ingredients that you put into the egg bite and you can have endless amounts of flavors. So today I'm actually going to be doing sausage, basil and minced onion in my egg bite. So let me show you everything that is in this recipe.  You can go ahead again and select whatever flavor combination that you want. You're going to need some non-fat Greek yogurt, some salt, pepper. I'm going to go ahead and add some minced onion to mine. I'm going to do a mixture of both fat-free shredded cheese and Trader Joe's light mozzarella. You're going to need some fat-free cottage cheese or low-fat. Just count your points accordingly. Some eggs and then I'm going to go ahead and add in some fresh basil. So let's get started on our Starbucks egg bites. The first thing that we need to do is grab out our blender. We're going to be adding four of our ingredients to our blender and puree it down until it's more of a liquid form and that is what's going to make our eggs. So go ahead and pull out your blender and to that you are going to be adding in six eggs. I went ahead and just cracked them into a bowl because I'm notorious for getting shells. So I'm always better safe than sorry. And in this bowl here I have one cup of Trader Joe's light shredded mozzarella, one cup of fat-free mozzarella, which makes up the two cups of cheese in this recipe. So the only thing I have to count points for is the one cup of Trader Joe's mozzarella. And then in here I have three quarters of a cup of fat-free cottage cheese. So I'm going to go ahead and add that. And then lastly I have three quarters of a cup of non-fat Greek yogurt. So we're going to add that to our blender. And then we're going to turn it on, give it a good puree, get everything nice and mixed together, combined really, really well. And then we'll be ready to assemble our egg cup. So go ahead and turn your blender on and let it do its work. Once your egg mixture gets nice and combined, you're going to go ahead and add in whatever seasonings that you want to use.  So I'm just going to add that. And I'm just going to give this a stir with a spoon. I'm not going to turn back on my blender. I'm also going to be adding some minced onion. I love onion and with eggs and minced onion just makes it really easy. And then lastly I'm going to go ahead and add some pink sea salt. We'll add some of that to the mix. And again I'm just going to stir this with my little spatula instead of actually turning back on my blender because I don't want my minced onions totally crushed up in there. So whatever seasonings you're going to add just go ahead and give that a stir once everything is nice and combined. So this is looking really good. Next you're going to go ahead and take your mixture. Look at how good that looks. And to that you're going to go ahead and add whatever protein source you decided on. So I am doing Jimmy Dean turkey sausage crumbles one cup worth. So I'm going to go ahead and add that to my egg mixture. And again I'm going to give it a nice mix just to get those sausage crumbles combined. So again if you're using ham or bacon you would just want to do the same thing where you just give it a nice mix so everything is nice and combined. So these are the silicone egg molds that I purchased. I actually bought these off of Amazon. You got two egg molds and you also get just two covers. If you do want to make baby food or some of the other things that you can create in the egg molds they give you covers that you can just slip on once your food is done. And then it also comes with the wire rack for your instant pot. So pretty cool. It comes with everything. I do have these egg molds linked down in my Amazon store below. So the next thing that we want to do is we want to go ahead and take our basil or whatever herb that you're going to be using in your egg molds. And we're just going to put this into the bottom. Now I'm not sure how many egg cups this is going to make. So I'm going to just start with one mold. So I'm going to set this guy aside. I'm going to just start with one mold and fill it up with some of the basil and some of the egg mixture. And then we'll figure out if we have enough to do a second batch of egg cups. Again the recipe didn't say and I did do quite a bit of modification to the recipe. So I'm not sure exactly how many it's going to make. So go ahead add your basil to your egg mold.  Now you don't want to fill it all the way up. You are going to cover this with some foil and your eggs are going to rise a bit in the cooking process. I will be cooking these of course in my instant pot. So I'll show you how I do that. So go ahead fill up your mold. It looks like I'm probably going to have enough to do my second set of molds which is awesome. These look really good. So when you pop these out you'll have that nice pretty fresh basil on the bottom of your egg cup. So there's that mold of cups. So yep it looks like I'm going to probably have enough to go ahead and fill my second mold. So I'll get that filled and then I'll show you the completed egg cups before they go into the instant pot. Once your egg molds are nice and filled look at those yum. I did have some extra basil so I went ahead and just topped each of my little egg cups with a little bit of basil on top as well. It literally made just enough to fill both trays. There was a little bit extra just maybe a tablespoon so perfect. And then from there you're going to go ahead and you're going to wrap your mold with foil. And then we're going to get these put into our instant pot. So I will show you exactly how to get your instant pot ready. Exactly what to do to cook your egg bites. But I'm really excited. This is the first time I've made these in my instant pot. So it is a little bit of a new thing for me. So I'm hoping these turn out good. But once you get them wrapped in foil you're ready to put them into your instant pot. All right so we're ready to go. You can see here that I did put in one cup of water in the bottom of my instant pot. I also added my wire rack that came with my egg molds. And then from there you're just going to take your egg mold and you're going to set it into the bottom of your instant pot. So I have both of my silicone egg molds in my instant pot. My rack here has rubber handles so that they don't get hot for us to just pull up our eggs when they are done. From there you're going to go ahead and put your lid on your instant pot. You are going to set it to vent and we're going to set our modes here to steam. And we're going to let it steam for about 10 minutes. All right so you've got your lid on. You're going to make sure that you set your lid to seal.  You're going to go ahead and hit the steam button and it goes on 10 minutes. And then you are good to go. So you're going to let your instant pot steam your eggs for 10 minutes. And then you are going to then allow it to slow release for an additional 10 minutes. And our egg cups should come out perfect. So I will be back to show you the egg cups as soon as I pull them out of my instant pot. So here are our Starbucks egg cups. These actually turned out really good. I just pulled them out of my instant pot. They come out really easy. You don't even have to spray these silicone molds. Look at that. They just pop out. So I'm going to let them cool for just a few minutes and then I will pop them all out and show you the egg cup and then show you my entire breakfast. Here are egg cups you guys. These look amazing. The entire batch is a total of 11 smart points. So you can have two egg cups for one smart point. What a fantastic breakfast for one smart point.  They look delicious. They have a nice kind of spongy texture. Like a really good cooked egg. Soft and moist. And yeah, I am super excited. So I'm going to pop two of these in each one of my meal prep containers. That will only be one smart point.  So this is going to be four smart points for my entire breakfast. So let me get these into my meal prep container and I'll show you my breakfast. Along with my Starbucks egg cups, I'm going to be having one of my blueberry western bagels. These egg cups, you guys are so incredibly low points that I thought that it would be a perfect combination with one of my western bagel blueberry bagels. You guys, these are full size bagels. Look at that. And they are only three smart points apiece. So my plan is I'm going to go ahead and add this to my meal prep container. And then when I get to work, I will toast it and spray it with a little bit of spray butter. So I'm going to be doing a blueberry bagel alongside my egg cups. If you're interested in trying out the western bagels, I do have a discount code that will actually give you 10% off any order. And right now they have a promo where if you order six bags of the western bagel, you can choose your flavors. You can do the full size or the bagelette. Most of the bagels are three points. The exception to that rule is the perfect 10 multi-grain. Those are only two. And then the max bagels, which are there like one and a half times the size of these, I believe are five points. So this should give you the nutritional facts on the website so you can determine what bagels best suit your WW program, but take advantage of the six bags for $37. Then you can take an additional 10% off of that. And that particular promo has free shipping. So that my friends is a bargain. So I will link all of my western bagel discount information here on the screen as well as down in the description box below. So if you haven't joined the western bagel bandwagon, now is the time. Now is the time, my friends. So here's my completed breakfast for the week. Super excited. What a great low-point breakfast. You feel like you're indulging by being able to have a bagel. So my full breakfast is this. I'm going to be having two of the Starbucks copycat egg bites for one smart point. I'm also going to be having a blueberry western bagel for three smart points. I will just toast it at work and add a little bit of spray butter. And then I'm going to have two of my halo oranges. So this entire breakfast is for smart points. Crazy that you can have all of this for only four smart points for lunches this week. I am going to be making spaghetti with the vegetable and some two ingredient dough garlic knots. Alrighty guys, let me show you what is in my lunches for the week. So of course for my spaghetti, I'm going to need some spaghetti. So I'm going to be using this fiber gourmet light spaghetti. This spaghetti tastes just like regular pasta and it is only three smart points per serving, which is amazing. I'm actually only prepping four lunches because I am staying home with diesel another day tomorrow, Monday. So you're going to see me prep for four lunches and I will give you the smart points for one fourth of my spaghetti and the points for the garlic knots. So I'm also going to be doing some extra lean Trader Joe's ground beef in my jar here. I have the Skinny ish dish crock pop marinara. This marinara sauce is zero smart points. I love it. I will link the recipe down in the description box below. And in my meal prep last week, I showed you how to make the sauce. I always add a little bit of brown sugar to my spaghetti sauce just to take some of the acidity and the tang out of the sauce. And then I'm going to top it with just a sprinkle of Parmesan cheese. So that is what is in my spaghetti for a breast for a brussel for a vegetable. I'm going to have brussel sprouts and then I'm making two ingredient dough garlic knots. So I'm going to need some nonfat Greek yogurt, self rising flour, olive oil spray, and then some either minced garlic or garlic powder, some salt and or I'm sorry, some pepper and some parsley. So let's get started on garlic knots, veggies and spaghetti. The first thing that we need to do to make our spaghetti is go ahead and get your ground beef browning in a pan. You're just going to kind of break it up and let it brown and then of course drain it. And then you're also going to get some water to a boil to boil our fiber gourmet spaghetti. So I'm going to get this boiling, get this cooking down and then we'll get the noodles tossed in. Once your water comes to a boil, go ahead and add in your fiber gourmet pasta. You're going to let this cook till it is al dente. You do not want to cook it all the way through because you're going to have to heat it up to eat it and you don't want it to be too soft and mushy. And my hamburger is coming along nicely as soon as this is brown and drained. We'll go ahead and add in our marinara sauce. Once your hamburger gets cooked down, we're ready to go ahead and add in our marinara. So we're going to go ahead and add that. Just kind of do little by little just to make sure that you don't over add in the sauce versus meat. And then from there we're also going to add in just a little bit of brown sugar. This just really helps take the acidity out. It is zero extra points because we literally barely added any and you're just going to keep adding marinara. Let this cook and let those flavors all melt together. Here is our noodles all packaged up in our meal prep container. So it makes exactly four servings. That's a lot of noodles. So that's two ounces total. That package is eight ounces. You can buy the fiber gourmet noodles off of nettrition.com. You can also buy them off of amazon and they are linked down in my amazon store below. So I'm going to go ahead and add a little bit of spaghetti sauce to each of my noodles. And then we will get those garlic knots into the oven. So here is my spaghetti for my lunch. Look at how amazing this looks you guys. So excited. So four days worth of spaghetti. I did not use all of the spaghetti sauce but I am going to go ahead and just count the points the same as I would have if I would have used all the spaghetti sauce. So I'm going to get those garlic knots ready. Show you guys how to make those. Get them in the oven and then we'll be ready to finish adding everything to our meal prep containers. To make our garlic knot pretzels we're going to be needing to make two ingredient dough. So what I have here is one cup of self rising flour. If you do not have self rising flour on hand you can use regular flour. Just make sure that you add in your leveling agents and then you're going to go ahead and use some non-fat Greek yogurt. It's usually about a one to one ratio. I am I do not measure my yogurt. I just add it until I feel that my dough is the consistency that I want it. Sometimes especially depending on the brand of non-fat Greek yogurt you use your dough can become really sticky. And I don't want sticky dough. It's super hard to work with.  So I'm going to go ahead and add in my yogurt. Get this nice and mixed together and then we'll be ready to roll this out and create our garlic knots. We do want our dough to be a little bit on the chunky side. Just make sure all of your flour is incorporated. Once you get your dough all rolled out into a ball I always lay down some parchment paper. You're going to grab your rolling pin and I always always spray my rolling pin with some non-stick cooking spray so that it doesn't stick to my dough. And we want to roll this out into roughly a triangle shape. I'm going to get 12 garlic knots out of my ball of dough. So I need to roll this out thin enough and big enough for me to cut it into 12 equal sections so that I can get 12 garlic knots out of my dough. So you're just going to roll and roll until it's even, until it's the consistency, the thickness that you want it to be, and then we'll cut this into 12 strips. Once you get your dough into 12 sections, so I just cut it into as equal sections as I possibly could get, you're going to take one of your sections and you're just going to roll it out in your hand. So I like to squish it together to kind of soften it up and also to kind of get it warm and easy to move and be pliable. You're going to roll it out into a snake shape. Once you get them all nice and rolled out you can actually make them into any shape that you want. More of a knot shape, a pretzel shape, you can make breadsticks. So I went with this pretzel slash knot shape. I'm going to get these into the oven at 450 degrees for about 10 minutes. We're going to pull them out. We'll add our olive oil and our garlic. Let them cook a little bit longer and then pull them out and add our parsley to the top as well. So I'm going to get these into the oven. I'll be back in 10 minutes. It's been 10 minutes. I just pulled my little knots out of the oven. It is now time to go ahead and add some olive oil spray. So I'm just going to spray the tops of each of my little garlic knots just so that the garlic powder and things stick to them. And then we're going to go ahead and just sprinkle on a little bit of garlic powder. Sorry, my camera doesn't want to focus today. Some garlic powder on each of our knots. And then we're going to pop these back into the oven for just a few more minutes just until they become nice and brown.  The garlic knots are done. I just sprinkled them with a little bit of parsley. I'm going to pop one garlic knot in each of my meal prep containers. Look at how good these look for the week. And then the rest will just set aside. And then I'll add my veggies. I'll show you my completed lunch for the week and give you the smart points. And here are my completed lunches. You guys, I am so excited for my lunch this week. So let me show you exactly what I'm taking each day. So I have one serving of my spaghetti. The fiber gourmet pasta is three smart points. I am counting the sauce still as two. And that is for the ground beef that is in the sauce. I'm going to stay with two points even though it is less because I didn't use all the sauce. I'd rather air on the side of caution. So my spaghetti, you guys, five points. That's it for all of this spaghetti. I did just put about half a teaspoon of Parmesan cheese, which is zero. I'm going to have one of my homemade garlic knots for one smart point. And then I added a little bit of frozen Brussels sprouts. So the knot is one, the spaghetti is five. And then for dessert, I'm going to have one of my favorite lean muscle bites. These are linked down in my Amazon store below. I like the M&M the best, but the cookie dough is really good as well. And then I'm going to be having an organic unsweetened applesauce. So my entire lunch with my dessert is only seven smart points, seven smart points of pasta. Yes, please. All right, guys, it is brownies time. I'm so excited. I have been craving brownies and I held off so I could make my own that are WW friendly. So I'm excited to share these with you guys. So let me show you everything that I'm putting into my brownies. This is my own recipe that I just came up with on my own. So I'm going to be using a box of the Pillsbury sugar-free brownie mix. You're going to need some water. I'm also going to use the sugar-free Pillsbury frosting. This frosting is so good. I'm going to be using some Lily's chocolate chips and egg and some unsweetened applesauce. So let's get these brownies made. I'm dying to have one. So to get started on our brownies in my bowl here, I have my box of sugar-free brownie mix. We're going to add everything to our bowl. One thing I did not show you guys is I am going to go ahead and add a packet of this instant espresso. And the reason for that is it just enhances the flavor of the chocolate. So I have one of these individual little packets here. I'm going to go ahead and add that. I also have three tablespoons of water, one egg cracked separately to avoid the shells. I also have one little container of unsweetened applesauce. I'm going to go ahead and add the entire container. I'm going to give this a good mix before I add in my chocolate chips. So I decided to make these like a double chocolate chip brownie. And in order to do that, I had to add some chocolate chips. Hello to get that double chocolate in there. So I'm going to go ahead and stir in 146. Yes, I counted them out of the Lily's dark chocolate chips. And that is going to add a total of five smart points to my recipe. And I founded that by adding that to the entire recipe. It only increased the points just a little bit. And in my opinion, it's worth it to have that nice extra bit of chocolate goodness. So there's our mix. We're going to add it to our brownie pan and get it into the oven. And there are our completed brownies. Yummy. Look at how good that looks with those chocolate chips. So these are going to go in the oven at 350 until we can insert a toothpick and it comes out clean. So I'll be back to show you our completed brownies. We'll allow them to cool. And then I'm going to show you how to make them decadent. All right, the brownies are out of the oven. I just popped them out of the pan. I'm letting them sit here on some parchment paper to cool. And then I'm going to show you how to make them decadent, beautiful and delicious, even more than they already are. Now that my brownies are nice and cooled, it is time to add some decadent. So what I have here is I have measured out 140 grams of the sugar-free chocolate frosting. And then I have some fun Easter colored sprinkles. And I'm going to go ahead and frost my brownies.  Don't these look amazing? So I cut my brownies into 20 servings. That makes them only two smart points apiece. Two smart points, you guys, for a frosted brownie. I'm going to plate one up so that you can see the size, thickness, and exactly what you're getting for two smart points. So here is one serving of the brownies. So this is a salad plate. So I would say that your brownie is probably a one and a half to a two by two inch square. So not bad for two smart points with frosting. This is the thickness of our brownies. And look at that, you guys. Moist, delicious. There's quite a bit of frosting on top. So you're not just getting a little drizzle. You are getting some frosting. So this, my friends, is a two smart point brownie. So here are the snacks that I'm planning on bringing this week to work.  I have been loving just about a tablespoon or so on top of the light and fit Greek yogurt. The granola in that dosage is zero points. You can have up to one half of a cup of the granola for only two smart points. That's crazy, you guys. Granola is anywhere from 10 to 12 smart points per serving, but the julienne bakery is only two. So I add just about a tablespoon to the top of my light and fit Greek yogurt. That makes this a two point snack because all I have to count for is the light and fit yogurt. I do have a 10% off discount code for the julienne bakery granola. It also gives you free shipping, which is amazing. That code is here on the screen and also down in the description box below. They have several flavors, vanilla, vanilla cinnamon, the espresso cluster, which I love, peanut butter, just really, really good, high quality, great ingredient, full of protein. Look at that, you guys. 12 grams of protein per serving granola. So make sure you order the julienne bakery granola using my code down in the description box. So that is a two point snack. I also have the Scandinavian fiber crisp breads. You can have two of these breads for one smart point. So I'm going to have two of those with a wedge of the laughing cow Swiss cheese, and that would be a total of a two smart point snack.  And then as usual, I will be having a built bar as my AM snack, which is what I have every single day. Built bars are a candy bar that are only three smart points. They're so good, you guys. I love them frozen. I love them in the fridge. I love them just out of my pantry. I just love them in general. So three smart points, 15 grams of protein, six grams of fiber. They keep you full. They taste like a candy bar. My code for them is here on the screen. They did just change their code program, where now you can use my code over and over and over again and always, always, always save 10% off both the sample box and the full size box and get free shipping. So what a deal. So I'm going to go ahead and have built bars. Make sure that you stock up on built bars. They are amazing. So this is going to be what I'm planning to bring for snacks for the week. Thank you for joining me on another weekly meal prep on WW. I hope that you enjoyed the three recipes that I shared with you today. Everything is amazing. Who said you can't have spaghetti and pasta and brownies on WW and on WW freestyle. So I hope that you enjoyed the recipes. If you're new to my channel, make sure that you subscribe. Hit that little bell and it'll notify you every time that I upload a new video. I do upload almost every day of the week. Also give this video a thumbs up, comment below. Let me know if you're going to try any of the recipes that I shared with you today and I'll see all of you in my next video. Bye guys.