Hello and welcome to the Grand Line Review, your source for everything One Piece. And today we have an interesting topic to explore. I mean, what we talk about here is always interesting, of course, I hope. But today I would like to look at the true bounty of Luffy. Now, what does that mean? Well, one of the many reasons as to why bounty should never ever be used as an accurate judgment of anything, power, or general world infamy is because they take into consideration only what the world government knows about a particular individual. And in the case of Luffy, he has a whole ton of achievements that the world government either remains blissfully unaware of or they did know about and covered up, thus not contributing to his number. Either way, this results in a fairly inaccurate bounty for Luffy throughout the history of the series. And I personally suspect that if everything were taken into account, then Luffy's bounty would be much, much higher. So that's what we're here to find out. But before we get into it, it's time to play a round of bounty guesser, a very simple mini-game, the rules of which are as follows. Basically, I'm going to give you an obscure character and a bounty number. It is then your job to guess whether that character's actual bounty is higher or lower than the number I've stated. And if you are wrong, then your punishment will be subscribing to the ground line review. And if you are right, then good on you, I guess. I hope you feel good about yourself. Now our character here today is going to be Vito of the Fire Tank Pirates. And the number I am assigning him is a bounty of 80 million berries. So please do select whether you think Vito's bounty is higher or lower than 80 million berries. I'll give you some time, not much time. Three, two, one. And the answer is higher. Vito's bounty is actually a pretty hefty 95 million berries, which was revealed to us in the Vivica data book. So if you said lower, then welcome to the Grand Fleet and please do say hi in the comments below if you are indeed a new recruit. All right though, let's begin the interesting stuff. And to start off, we are going to cut all the way to the end of the Arlong Ark where Luffy receives his first bounty of 30 million berries. And nothing really changes here because at this stage, the Marines did have a fairly accurate account of Luffy's achievements, particularly noting the defeat of Buggy, Don Creek, and Arlong. The only key thing we're missing here is Captain Kuro, but it wouldn't have mattered because his bounty was only 16 million berries, so both Creek and Arlong topped that. So 30 million is a fairly accurate start, which fun fact, then Commander Brand new stated that a starting bounty of 30 million berries was unprecedented in East Blue and the average pirate holding bounty or bounty holding pirate, I should say, would be worth around three million berries. But next up, we'll skip to the tail end of our Basta for our next bounty change, which is the infamous 100 million berries. And another fun fact here, this is one of those situations I flagged earlier where the world government tried to cover up Luffy's actions because it would have and did indeed make them look bad. So if it weren't for Smoker refusing to go along with their game, Luffy would not have been awarded a bounty of 100 million berries, I guarantee that. It no doubt would have increased by some amount, but probably something more understated, so as not to draw too much unwarranted attention to the alabaster incident. The question for this video though, is was 100 million enough? And I would again say yes. The only real thing you might wanna take into consideration is all of the Baroque Works agents that Luffy beat, but they're kind of too small fry for that to really factor into things. I mean, for example, Mr. Three's bounty was 24 million berries, so those escapades are quite unlikely to impact things, but that is about to change though because we have a massive development. Skipping over Jaya after punching Bellamy in the face for the first time, we now arrive at Skypeer, where Luffy is responsible for defeating God and El, an event that the world government will likely never know of or care about because they have no concept of El's existence. It was completely isolated, so we need to ask ourselves, if a giant jack falls over in a sky forest and no government officials are around, does it make a sound? And I think you'll find that yes, yes it does. Even with the Devil Fruit counter in mind, this is still a pretty phenomenal achievement from Luffy and we do have a method of scaling this into a tangible bounty. This is due to an SBS segment in volume 43, where Oda was asked how much El would be worth if he came down to the blue sea, and he stated that his bounty would probably go over 500 million berries, considering how troublesome El is, and he went on to say that Luffy kind of lucked out due to being rubber. And as such, there are a couple of ways we can go about this. Bounty increases usually follow certain patterns and by and large, there is usually one of three options, the first of which being to raise the bounty slightly above the opponent defeated, which is what we've seen so far with Luffy. The second option would be to add a stock amount onto a bounty due to major involvement in a prominent event, which sometimes sees bounties just plain double or even triple. And then the third option would be adding a helpers fee due to being present for a major event, but not being the primary instigator. And that last one is irrelevant for Luffy in this particular case. But given the situation here, I think option two would be the best way to go. I mean, yes, we could increase Luffy's bounty to just over 500 million berries with option one. However, it would be a disingenuous representation of threat to do so. But we do still need to recognize this grand achievement and a middle ground approach of tripling Luffy's bounty to 300 million probably seems like the way to go. It's the happy medium between 100 and 500 million. So 300 it is. So we've reached that 300 million mark much quicker than Luffy did in reality. And that puts us in a unique situation for our next bounty increase, which is going to be after any slobby. There's nothing much missing to take into account here. The world government is well aware of what happened on water seven and on the judicial island. And long ring, long land is well as per usual irrelevant. But then Luffy goes ahead invading and effectively destroying any slobby, which in the actual series led to the increase to 300 million. However, because the any slobby incident is an event rather than an opponent, we can't just let it remain at 300 million. Because as a result of this Luffy's threat level to the world government has increased. And I think that slapping a clean 100 million on top of this is appropriate. Adding 200 million like in the series was a pretty drastic measure designed to indicate that Luffy was a much greater threat than previously thought. But in this world, we already know that to a degree due to registering the defeat of Enel. So at the end of the water seven saga, Luffy is carrying a 400 million very bounty. And then we step into Thriller Bark where Luffy and the straw hats go on to defeat another warlord of the sea being Gecko Moria. And here we have an example of a world government coverup because they actually anticipated Luffy defeating Moria so much so that they sent Bartholome Kuma to Thriller Bark to make sure that he eliminated any witnesses of Moria's defeat. And while Kuma certainly did fail to do that for complex moral and cyborg related reasons, Luffy's triumph over Moria was still kept from the general public. So it was not to cause outcry over the very idea that Luffy had now taken down two warlords of the sea. And well, we can't afford to think like that here. And for the purpose of this video, news of Moria's downfall will spread. Our Luffy currently has a bounty of 400 million. And whilst that is much higher than Gecko Moria's frozen bounty of 320 million, Luffy is still going to get a change because his threat to the world government has very publicly once again increased. And I'm going to slap another 100 million on top of it, not just because of Moria, but because Moria is the latest in a long string of collapsing dominoes for the world government. At this point, Luffy has now beaten two warlords and completely destroyed their judicial island. In terms of threat accuracy, he is now a 500 million very man quite easily. I mean, physically, no, he may not stand up to the power of other people within that bounty range, like say, podcasties, but bounties do not measure power. As he is right here right now, Luffy is just as much for threat to the world government without being some sort of commander level being. In fact, given that commanders are directly under the control of emperors, and Luffy is more or less an unhinged force of anarchy, it's arguable that he is even more dangerous. But now we get to some really crazy cool stuff, the paramount war saga. So in the series, this whole thing leads to a rather underwhelming bounty increase, which sees Luffy move from 300 million to 400 million. However, we are starting this saga at 500 million, and there is a lot here that Luffy either does not get enough credit for, or he gets too much credit for. As an example of the former Luffy's involvement during Impel Down is completely overshadowed, both by Blackbeard simultaneous invasion of the prison, as well as prominent figures alongside Luffy, like Jinbei, even Kov, Crocodile, and even Buggy. Yes, that's right, I said Buggy. And Luffy amongst all of this was never quite properly acknowledged for just how instrumental he was in Impel Down. But then when it comes to Marineford, he probably gets a bit too much credit for pulling off feats like say Punching Garp, the hero in the face, to the viewing public that is a huge, huge shock to the system, but it would not be accurate to use that sort of stuff here. What is relevant is that the paramount war saw the revelation of Luffy being the son of Dragon, as well as demonstrating that he has conqueror's hockey, very, very potent conqueror's hockey. Along with his association with Silver's Rayleigh, which was revealed to the public after the war, this is what gave Luffy a 100 million increase canonically. Purely an event and infamy related increase, which is something that we can't ignore here. And furthermore, we also can't ignore Luffy's main role in Impel Down like the world government more or less did. So we are going to take that canonical 100 million and add another 100 million on top of that, landing Luffy at 700 million berries. And my logic is that the destruction of any slobby brought him 100 million. So the first mass breakout of Impel Down should do exactly the same as a threat indicator. And the other 100 million, well, the reasoning for that is established in the series. And yeah, look, 700 million might seem way, way, way too massive for a pre-timeskip Luffy. However, pre-timeskip Luffy's threat level was severely underestimated. Once again, no, he doesn't have the raw power yet. But by this point, he's done more to directly harm the world government than any emperor of the sea has. So that takes us to the post-timeskip era. And starting with Fishman Island, you might think that we can overlook this arc, but we absolutely cannot. I mean, we can and should overlook Hody Jones in general, not just for this video. But the important part of this arc is that Luffy became friends with a literal ancient weapon. And for all intents and purposes, Luffy has command over the ancient weapon Poseidon after this arc due to developing a bond with Princess Shirahoshi. And if the world government knew that Luffy had access to an ancient weapon, then they're probably not just going to sit around and do nothing about that. And as such, it is here that I issued Luffy with a flat bounty of one billion berries. Although I think a solid argument could be made for much more than that, because ancient weapons are not to be underestimated and Poseidon has been said to be able to sink entire islands with ease. And that seems pretty damn threatening, but I think one billion is the solid conservative number. This does make things a bit underwhelming during Dressrosa though, because we now have a fairly tanky Luffy coming into play, causing Dolflamingo to promptly crap himself and ultimately lose as per the series. Now Dolflamingo's bounty was 340 million berries prior to being frozen, but that doesn't matter. Luffy is still getting hit with a 100 million increase because it's not about the opponent. It's not about the power. It's about the event and the threat. The fall of Dolflamingo represents the third warlord that Luffy has taken out, meaning that he has defeated, you know, roughly half of the world government's most powerful pirate squad. And we can't just leave Luffy be if he continues to become more and more of a threat because that's what bounties are meant to indicate. And so we're going to race into Holcake Island with Luffy being worth 1.1 billion berries, which is only slightly more than Kartakuri. And I find that pretty amazing actually, because when you consider their fight, even if you do like using bounties as power system, this is pretty damn accurate. This logic places Luffy and Kartakuri on an even-ish playing field with an expected outcome of Luffy's victory, which went on to happen. So I'm actually pretty amazed that we landed so close to the sweet commander's numbers, but Holcake Island is about much, much more than that. Yes, Kartakuri's defeat played a part in it, but the bigger issue is Luffy's invasion of an emperor's territory. That and a wide array of other factors, such as the emergence of the Straw Hat Grand Fleet. This is what led to the world government just up and tripling Luffy's bounty to 1.5 billion berries in the series. However, we have got to go further than that, much further. Because at this stage, we have a man worth 1.1 billion, proving to the world government that he can handle a direct conflict with the Emperor of the Sea, which no does not mean a one-on-one fight, don't get those ideas conflated. But what Holcake Island achieved was Luffy standing up and saying, I am equal or greater than you. And if that had happened whilst he'd had a bounty of 1.1 billion berries, I highly doubt that the world government simply would have gone, I don't know, should we add a couple of hundred million more? No, they would need to signify that Luffy has become a much larger threat, which he has. So I'm actually going to do exactly what they did and triple his bounty to 3.3 billion berries, which in the end, I think makes perfect sense because he was later announced as the Fifth Emperor, but it still places him at a significantly lower value than his other Emperor contemporaries, thus distinguishing him as a lesser but still very prominent threat. And that's where we find ourselves today. I can't go any further with this idea until the end of Wano, but the concept of 3.3 billion doesn't seem insane at all to me. For the vast majority of Luffy's pirating career, his threat level has been wildly underestimated. In fact, to think that he walked into the Holcake Island arc with a mere 500 million on his head is absurd in retrospect.  And a number like 3.3 billion would be the result of that. And really, when you look back on One Piece, the world government always seems to be a step or two behind when it comes to Luffy. His bounty numbers suffer a huge degree of lag, which is why if you think that Luffy is only worth 1.5 billion berries as of right now, then I think that you are absolutely mad. His true worth is much, much more. And laying out all of his achievements on the table, very much goes to show that. So all hail the 3.3 billion berryman. But what do you guys think? Please do leave your thoughts in the comments below or even join my Discord server. And if you'd like to see more videos like this, then please do go and check out some of my other content or even subscribe to the channel for more glorious One Piece business uploaded straight into your YouTube feeds. But for now, this has been the Grand Line Review and I'll see you next time.


Hello friends and welcome back to another episode of our VGC 2020 battle series My name is Lee also known as Osiris and in today's episode We're gonna be featuring a team based all around G max Pikachu with the recent event that we had in the wild area where we could catch G max Pikachu Finally in sword and shield it now has access to that hidden ability lightning rod Which makes it a lot more viable within this VGC 2020 format So what better time than to put a team together based around this Pokemon and have a bit of fun with it So as always there will be a rental team for this specific squad at the end of the episode So if you want to try that out stick around to the end and you can grab that one and try it out And if you do try it out do let me know what your thoughts are around this team It would be great to hear and if you've been playing around with G max Pikachu of your own I'd love to hear some of your builds that you've been playing around with so we've got today the the Pikachu With the the lightball which was a pain to get honestly It took me ages to get the lightball, but we've got that it doubles Pikachu's special attack and attack So we'll probably more gong special attack with this Pikachu here We've got the extra speed control support from the whimsy cot there then Draco vish the diggers be Garados and the toga case making up the rest of this squad So I'm gonna be a lot of fun We'll click into it now and hopefully it doesn't take us too long to find our first opponent of the episode I've been playing a few games off-camera with this team just to try it out and it is a lot of fun It's difficult to get to work and you can't always go the Pikachu mode But I do try and force it a little bit and I probably will for this episode because it's all about the Pikachu, isn't it? We've got a first opponent of the episode anyway, and they are playing a team of toxicity the whimsy cot drag-a-pop toga kiss Papa surf etched for fetched surf etched and the dust globes. So What are we looking at speed control from the whimsy cot and toxicity going to be a bit of a pain something that we can't Paralyze because of its electric typing But everything else kind of doesn't want to be paralyzed in this team and even if they do they've got the dust globes So I guess that makes it a little bit more tricky to get around diggers be looking really really good here and Honestly if we can get like some speed control support with it. It's gonna do a lot of work I think the issue is the the opposing speed control from That the wimmy on the opposite side of the field Hmm, okay Now do we go It's be control with wimmy or do we go with toga kiss redirection diggers be just clear the way a little bit And I think wimmy's nice because that if they do lead dust globes We have got taunt there. So that could be a nice option there. I Don't know if I want to lead pika Do I want to lead pika? Pika could be good leads. It could be a good lead, but I feel like Yeah, we'll go pika but diggers be and We will bring toga kiss this one and we'll not time out. We'll try not time out. Okay So we'll see how we get on in this one Hopefully you can get the big W. That's what we want. That's what we want with the pika And I want to get like I don't want to go through this whole episode where I wasn't bringing the Pikachu But at the same time I do want to show some value to this content where we can actually play the team where we're not just forcing Pikachu and Wimmy every game is a lead here But turns out it's not too bad an option here. Um, so You can probably get the paralysis on both Pokemon right now we can tailwind match their tailwind go Do we just do it? Do we just do it feels like it's too easy here, and we shouldn't be doing this But like let's just let's just go for it. Yeah GMX vol Christ watch it be weakness policy to this now I've jinxed myself and like it's not gonna be You've got the RNG gods on our side as we always do so let's let's hope the RNG god shine down Kindly on us in this battle, especially with a shiny GMX Pikachu In this Pikachu, I actually ran into a random raid Online like last week, and it was just a shiny I was like, oh my god because I had such a difficult time getting my own with lightning rod, so oh No way That is so risky. They just taunted our whimsicott. I can't believe they just actually did that like really because like if they lose The speed tie there. It's so risky and Like they blocked that tailwind great, but like like 50% of the time that isn't gonna work and I mean It's worked out here great, but we did it down to a kiss. So we've got a new toga kiss counter right here Which is great. So we actually have the dragon pull coming in now. Um, hmm And we've got no option to protect on Pikachu, which is a little bit of a shame We could potentially just double in to dragon pulls here to be honest get some nice damage onto it There's always a chance that it is weakness policy dragon pull as well Like we should probably take a look at my opponent's team a minute to see if there's any I mean the dust clubs could proc it there I don't know if they're gonna though are they? Let's go moon blast and I mean, yeah, if we can get the paralysis onto the dragon pull that's huge for us I don't know if we're gonna be able to we're gonna see a max. I think Could see max. So there we go Feel like dragon pull probably is able to knock out Pikachu Like that's a one big drawback with G max Pikachu is and you can bulk it out defensively But you're gonna lose speed or power really. Oh That's not what we want to be seeing not the fake tears is special special dragon pull is it max worm wind coming out Okay, this is what I mean like it's just not the bulkiest of Pokemon at all Just a little bit of a shame and but it's not the end of the world right now Hope he could you do get the moon blast into the dragon pull So probably take it down the next turn, which is which is ideal and we can bring in toga kiss now And we do have the the thing is with toga case. We're probably going to I'm probably gonna lose it to big tears and Max phantasm I would imagine although Toga kisses like we might be able to take that combination, you know, maybe maybe We're just gonna double up into the dragon pull again I'm not really too worried about the whimsicott like the thing is that the paralysis This would be a great time for it to be fully paralyzed like RNG gods if you're there listen Okay, well the tailwind I don't mind like honestly, I prefer that to Wow max lightning coming out I prefer that to losing toga kiss like at least this way we get to kind of hang on our Taunt should wear off this turn so it means we get the tail wind up the next turn then we got diggers be in the back and Oh, yeah, we do have them access to our weakness policy now as well, which is pretty nice There's moon blasters should take down the dragon pull and the the dozen gleam gonna do a nice chunk of damage to this whimsicott as well And then my opponents and got one Pokemon left and because they are taunted and they are paralyzed now Their taunt is gonna go After our tailwind which is ideal So it will be interesting to see what the last Pokemon is. Okay, it's toxicity, which is perfect for Good old digs to come in and do some work. So we'll tailwind and Yeah, we'll go for a dazzling game tokicus might add speed toxicity. It depends how it's been trained I'm not gonna count like count on us at speeding it though, but you never know Fully parallelized whimsicott would be great. No, no, no fully parallel like paralysis like where's Pikachu's power There's overdrive. Yeah, we'll take down our tokicus Do a nice bit of chip damage to our Whimmy but it's so fine now it is super fine because we get diggers be in I Don't feel like the toxicity's really got a way to do anything to us and Digs with these huge powers just gonna be able to earthquake and take Take everything down. So we will moonblast the opposing whimsicott and we'll go for that earthquake And I'm kind of opted for earthquake on diggers be here because we do have the Gyarados and we do have the Toxicus in the team. So we've got really quite nice synergy between at least two Pokemon that we can pair Next to diggers be that we can earthquake with quite freely and you know whimsicott It's kind of another Pokemon. It doesn't care too much about earthquakes It's still gonna take you know decent damage from it, but it's not getting killed from it It's not putting us in super detrimental positions. So I Felt like the earthquake was quite a nice option on diggers be and it gets around like redirection Follow me support from tokicus ex-cadrill. We get the fully paralysis. Yes. Come on There we go. That's what we wanted to see Okay, so no fake tears coming out this turn from the wimmy and Diggers be out speeding and gonna get this earthquake off Pick up the knockout and pick up the W for us. Yes. Come on So Pikachu did do some work didn't do all of the work I feel like wimmy probably did more of the work than anything else But Pika Pika definitely picked up the knockout on that tokicus at the start of the game So that was that was pretty big for us and So we can't take all the credit away from Pika and we got it gone So that was good. And you know what right thinking about it Tock is probably one of the most annoying Pokemon to face in this format, right? And we just care of it Super easy with Pikachu Pikachu just like why is everyone so worried about tokicus? I'll just get rid of it easy watch watch and then boom gone gone So Pika is good for that If nothing else It probably be good to have something like screen support. I think like screen support Pikachu would probably be quite good. Ah Okay, we got our next opponent playing team of Hatterine and their talk all giggle it orangaroo and The octopus which I can't remember for the life of me right now and the lie pod What is it called? Octillery? Octillery there we go. And super hard trick room team We got trick room users there with the orangaroo and the Hatterine and You've got the copycat as well with the lie pod that can be a thing so I mean we could paralyze stuff for sure, but is it really gonna be that helpful right now? Not so much Without like a steel type in the team it makes it pretty difficult to To deal with something like Hatterine and I mean, yeah, we've got I we do have taunt with whimsy cop, but I mean it's not really doing anything Magic bounce makes that very difficult. Let's go Pika up top We want to go maybe diggers be diggers be could be quite good here. Um, although not ideal We could go talk a kiss though Probably not the the greatest idea either. Um, let's go diggers be Toga kiss And the fit we're not gonna get it. We're not gonna get it. Oh come on We're going whimsy cop Pikachu. I'm just just dawdling far too much Yeah Hatterine is pretty difficult for this team to deal with because we're kind of relying on The taunt mainly to shut down trick room options where we can't really go for that here and And Yeah, we can't taunt the live on we can't taunt the Hatterine we can fake things out, but It's not really helping us too much Could you max ball crash for sure? I mean we could double into live hard. We could totally double into live hard Get rid of it Probably gonna fake out though. That's the that's the issue Like does it fake out or does it just go copycat? I think it just goes copycat and the hat goes G max max guard. I'm hoping that's what happens. That's what happens Then it makes it a bit easier for us if we just say fake out trick room. That's when it gets a little bit more difficult for us I mean we do Paralyze both targets so there is every chance that we could get Both Pokemon fully paralyze this turn. So, you know Okay, we're gonna see the max Great if it was live pod. Never gonna be those it Never All right. Well hat. Let's see that max max guard copycat combo. Let's see it. Come on Take out.  Wow That's quite a lot like I'm always quite surprised by how strong Pikachu is especially with the light ball Okay Come on fully paralyzed Never come on just this one time Man, we don't even take that Pikachu so weak So weak No trick room. All right. Well, it's not the end of the world just yet It's like okay. Well The fish is not a bad Pokemon to bring in right now Fish is not a bad Pokemon to bring in Just to get damage onto the hat. Honestly, um Let's go for it. And let's just try and get rid of the live pod Come on Come on. We'll probably see talk or maybe hit the field now and place a live pod Definitely could be an option if you go in the back There's something I would be thinking about Then just G max smite again into the fish and that gets rid of it first. This could be quite a short one Yes We get the funny power lies twice Come on break through that confusion with me. Oh With me the dream was nearly real. We get the fishes rend off Okay, well, it's not as much as I kind of expected it to do But We could maybe do we protect here? I don't know if we do is a moonblast going to be enough with a fishes rend to get it potentially potentially I don't think it is actually I don't really don't think it is like we could protect but the problem is protecting here Hey, we're gonna get we're gonna get confused Anyway, which is not ideal um Probably better off just doubling up into hat Honestly, I think that's the best way for us to do it You get it twice. Come on We snap out of confusion Oh, I think we might get it, you know with a high roll. I think we get the hat Yes, we get it. Come on Yes, this is awesome Okay, pika putting in all of the work here pika's like I'll show you some work. We'll get this done right well We still got diggers being the back We're not confused anymore. We've got rid of the biggest threat hat. So that's perfect Octillery we're gonna hit the field now What's octillery we're gonna do like what are you gonna do octillery? It's just a pure water type, isn't it? um, it's probably good to get rid of Lipod while we can so we'll get rid of that I have no idea what octillery does. I know it has moody But it's about it. We'll keep we'll keep the fish around for a little bit longer Big tears. Uh, okay.  We are like the jam. I feel bad for my opponent. Do I do I though? Not really no No, I'm just taking it the RNG gods have said we we've got this. So this is fine Um gigalith coming in onto the field now. Now. What do I worry about more? The octillery for sure. Um, I think we go double into that thing Um, yeah, just because diggers we can then come in diggers be able to deal with gigalith Yeah, okay. We see the protector Fish's rent still should do some good damage to this thing and if they go fire blast again, then that is super fine Come on fish's rent pick up the knockout from there The fish is broken. The fish is just broken. Okay. Well, we get it And then yeah, we don't even need the diggers be so this is this has been this has been good This is like a super turn around because I didn't expect us to win this one I thought this one's gonna be super tough, but I mean really when we look at it if I look like All the luck in the world. Haven't we we've had all the luck. So I feel bad my opponent But we've had two good games today with the pica squad and we've got two wins. So that's all that matters my friends Um, and pica's put in a lot of work. It's got knocked out Very quickly in both games, but I think that's how pica works. You kind of Maybe reallyistically without the sash Like sash is probably a really good item on it to be honest because it then gives you the ability to probably get to G max vol crashes off, which you kind of want to paralyze the whole team if you can But you can see we're not taking any big attacks at all, which is the one drawback of it I still think it's a really cool pokemon. It's it's a nice pokemon to have a bit of fun with So like I said at the start of the episode We're going to do the rental team here for this this team if you do try it out Definitely, let me know and I hope you have a bit of fun. It's not a super serious team It's not going to be winning you any any tournaments or doing like you're going to have good streaks on the ladder with it But it's just a team. I have a bit of fun with And I think that's the big thing with pica chew And manage teams to share Um, okay, what are we going to get rid of? No, I don't like getting rid of teams. I really don't um Um, what about order options? I'm not good. Let's get rid of this one because this one Yeah, we'll create some space there because the the snorlax and stuff like that the hat It's it's recent, but it's it's kind of it's I think one of the teams you could get rid of here And then we can pull in this one Take this public And what I'm gonna have to do is get myself out of the picture here for a minute because I do notice in my last episode video was that I I kind of blocked the view of the whole um the rental code I might not now because um I have oh no, so that's all right If I just move my arm a slightly a bit over here and then there you go my friends There is the team so if you do try it out have a lot of fun with it It is a really fun team to play around with hope you've enjoyed today's episode as always if you have Drop a like on the video do subscribe to the channel if you're new here for more of this content more pokemon news Guides updates all that sort of thing cover everything pokemon and we just have a lot of fun while doing it So it'll be great to have you join in and we'll wrap things up there We'll be back with another episode very soon my friends take care of yourselves If you're playing in the players cup this weekend as well the I see it is the um Qualifiers this weekend. Good luck. Let me know how you get on and we'll uh I look forward to hearing about all of your battles and success stories next week So until then my friends take care of yourselves and bye. Bye


It's key, you know, the future of AI is open. So I'm Marcus, I'm from Germany, part of Red Hat doing a lot of technical marketing stuff. And I do talk a lot on stage and I do introduce a lot of people in various contexts but today is kind of a big fan boy moment because I have the pleasure to welcome Sherar and Sid from Red Hat and Intel and they both are gonna walk us through the latest and greatest of what Red Hat and Intel do together with this exciting topic around artificial intelligence. Sherar, take it away. Thank you, Marcus. I will say, even before we get started, I had fun putting together this slide deck with Sid. I think the topic is very interesting and very relevant to where we are today. Lots of things going on in the AI industry and the work that we'll talk about really highlights how this isn't new for Red Hat and Intel. We've been here before and so it's a great opportunity for us to collaborate in which we have been doing. So I'm very excited to talk about what we've been doing together but then also some work that we've been doing independently that support each other. Really quickly, the agenda. I'll first talk through, you know, the open source. Well, I'll give you the introductions myself and Sid and then we'll talk through open source revolution, how open source is critically important to LLMs and the future of LLMs, what we're doing together with LLMs, both Red Hat and Intel and what Red Hat's doing with large language models and generative AI. And of course we'll round this out with what Intel is doing in this space. Just a little bit of background. I'm Sherard Griffin. I've been at Red Hat for about six and a half years now focused primarily on data and our AI and the ability for us to build products that help customers infuse their applications with AI. This goes for OpenShift AI, I head up the engineering for that as well as our open source community open data hub. And then more importantly, I work with great partners like Intel to round out our ecosystem to ensure that our customers have the best choice of both hardware and software for their AI initiatives. I have with me Sid Kolkani from Intel. He has a phenomenal background. He's been delivering AI and data solutions in three different verticals over the past two decades. Anything from retail, finance, as well as ad tech. And Sid's currently the VP and GM of the data and ad platform at Intel responsible for delivering the AI vertical apps and several verticals as well as optimizing Spark installations. So I'm very glad to have him here and I'm actually gonna go ahead and kick it over to Sid to get us started on open source. So good morning and good evening, everyone. And thank you, Sharad, for that great interaction. Today I'm gonna talk to you about open source revolution. So I think you are already familiar that open source in itself is a revolution which started somewhere around in 90s and just it took by the storm. The reason it is a revolution is because it promotes collaboration. Obviously it does provide transparency and security. The shared problems are solved much faster and teams collaborating, communities collaborating and working together creates standardization. There are over a million open source projects that are now created. However, with the advent of LLMs, open source is even more relevant because of transparency of both the model as well as the transparency of data and the whole sensitivity around transparency issues and security issues. Next slide, please. So who else is actually saying this, right? So if you look at this interesting memo from Luke which recently leaked out of Google, he says that we have no mode, neither does open AI. Open source is going to outpace any closed AI systems and is already doing so in several waves, this memo argues. So as you're fully familiar, LLMs actually took the whole world by storm around November of 2022. Since then you have seen significant innovations and multiple companies making multiple announcements every month or every two months. This week itself, Meta and IBM are starting an AI alliance for leading technology developers, researchers and adopters collaborating together to advance open, safe, responsible AI. This includes about 50 members along with heavy hitters such as AMD, Dell, Hugging Face, IBM, and of course in Dell followed by Oracle, FastAI, Stability AI and NASA to mention the few. More recently, you have also seen the European legislation that ultimately included restrictions for foundation models but gave broad exemptions to open source models which are developed using core that's freely available for developers to alter for their own products and tools. This move could benefit open source AI companies particularly in Europe. Let's go to the next slide. So let me walk you through a little bit of my experience as an engineering manager in building products and building product strategies. So previously, before the advent of the LLMs, typically you start a project, you start it small, you start putting it together with available open source components to keep the cost down, to run certain POCs and then you kind of make a buy versus build decision. If you are creating an application that captures the nuances of your business and want to give you a comparative edge to you, you probably decide to build it in-house but if there is a solution that is available on the market, especially as a service model, I think you would normally choose that as a solution. So that is kind of the process I would go through while deciding buy versus build. Whereas today in the advent of LLMs, it feels like it is turned by 180 degrees. So typically first now people start experimenting with Blackbox LLM services to keep the cost very low but then after doing some experimentation and using the data in the open source as well as using some of the fabricated data, then you start thinking about your own data and making your application more and more relevant and leveraging your enterprise data. Then the real data questions come in. Can I actually upload my data to a service? Am I risking my data? Is the service transparent? Can I customize the model or service? That's when you start actually bringing a model in and possibly fine tuning or doing a rag on the model so that it becomes specific to your business and delivers you the comparative edge. Next slide please. So with that I hand it over to Sharad. What Sid mentioned is something that we hear many customers facing that challenge of I wanna get started quickly with my AI initiative. Why do I do so? And a lot of times that barrier to entry is oh, well, let me just call this service and send the data even today in a meeting, the conversation was brought up, well, can I just use this service and infuse AI into our processes and develop our code faster? Well, what we're trying to highlight is the challenge and the difference in the Delta which you see now with LLMs and how companies can pivot to having a more sustainable infrastructure to not risk the data and not risk you have security issues that you may have when dealing with a lot of these pop-up AI services where you don't quite know what they're doing with your data. Are they using your data to retrain? Are they using your data for other purposes? So if you need to have that infrastructure in-house in a way that is approved by InfoSec, improved by IT I wanna talk you through some of those requirements and what that looks like. For most companies that they first start playing around with LLMs and chat GPT and now they're getting to the stage to where they're done they're kind of done playing around with those concepts and they want to build products and services for themselves that take it to the next level. This is where they have to transition to really seriously investing in the AI projects. You may have had something run on your laptop but now how do you roll that out into production at scale where the infrastructure is critical to the success of that especially if you wanna protect your data and run it in-house. When you're doing that there are a lot of stakeholders that have to partake in this exercises. Now you may have one or two people having a myriad of these personas but regardless, we typically see this as the cycle that has to happen in order to get an idea from that early stage into it's been invested and infused and now it's ready to roll out. This goes anywhere from your business leadership to provide the right criteria and requirements for you to design your systems around. The data engineer, the data scientist, and the ML engineer is working really closely together where the data scientist may develop the model but the ML engineer is responsible, think of it almost like an SRE type of role. They are there to usher that model into production and provide capabilities like roll forward, roll back and ensure that metrics are being collected. But even more critical to that is the application developer. If you roll a model out into production but you don't have an application that rounds out that experience then for all intents and purposes it may not be all that useful. Imagine chat GPT without the application that went with it and so that's the criticality of the application developer's role. They need to understand and have the right ability to work with the models and infuse their applications with AI. And across this, every single team member plays a critical role in this complex problem of getting things to production. And when you start to look at this holistic view this can be very expensive if you were to try to replicate some of the cloud services and Sid will talk about the cost of a lot of this down the road. But what I wanna highlight here is you have to start thinking about if I wanna run these projects and go from ideation into a seriously invested AI project you have to think about the cost of CPUs, GPUs, memory and even what it could represent if you're using cloud, cloud services, EC2 instances, Google Cloud, that has an inherent cost. So these are all the things you have to start thinking about when you're delivering the solutions for this. Now, this is where I'm excited because the work that Intel and Red Hat have done together helps you optimize on the execution of the models as well as the cost in order to ensure that you're building on a system that is scalable and you can support long-term. One of the key parts about this is the industry has moved away from, at least when you talk about how you can apply this in your own local environments they've moved away from a lot of these large language models that are billions and billions of parameters some of them 100 billion parameters very large sizes and the gigabytes of sizes they've gone away from that and they've started adopting more industry-specific smaller foundation models that can be tuned and you can do inferencing on it with a lot less hardware. Now you combine that with some of the innovation coming out of Intel and Red Hat then you have a really powerful ecosystem. So if you look at the left side of this I wanna talk a little bit about what we've done on the training and validation side. We have great technology that allows and all of this is OpenShift AI and what I'm gonna talk through and it all runs on OpenShift. On the left side of this what you see is a lot of technologies that are geared towards being able to do distributed processing of the data whether that's where you need to fine tune it and you need to use all of your infrastructure to do that or optimize the usage of that or even down to I just need to train a model how can you do that in a way that allows you that scalability, that elasticity even in your own data centers. Then we have technologies like MCAT which allows you to optimize the queuing as well as the priority order of your training jobs that are running. If you have 10, 15, 20 data scientists all needing to train the data you need ways to optimize that. We also have InstaScale that allows for dynamic scaling up and down of the clusters.  Now on the right side which is more on the tuning and the inferencing side we've done some great work with Intel they have the AI analytics toolkit as well as OpenVINO. Those are fantastic frameworks to be able to do more with limited hardware. That goes for anything from Edge to just you don't have GPUs when you need to do CPU and still get really good performance those are great frameworks for that. For example, let's say you don't want to necessarily purchase GPUs for some of your inferencing and you wanna do things like quantization or sparsity those are great frameworks that allow you to do that and get GPU like performance but on a much more commoditized hardware. And you couple this with the smaller more specific foundation models that it can be very powerful. Some of the other things in this space that's fantastic we've also been working on the hardware side with Intel and as well as the kernel level to optimize performance. If you do need GPUs you can have that with what they're doing there. Also fantastic work with the Xeon they have upcoming capabilities there. Each release is adding more cores to the CPU which is phenomenal if you want that more CPU workload. And then of course we have Habana Gaudi 2 which we work fantastic with Intel and all this is done open source. All of these technologies are coming together to help application developers to help MLops engineers really optimize on simple hardware how they can adopt LLMs and foundation models into their ecosystem. Now I talked briefly about OpenShift AI I wanna make sure I hone in on this point. We are focused on the AI industry along with Intel but this is all being done out in the open. And we have a lot of upstream projects CodeFlare, Ray, Qflow, K-Serve they're all feeding into our open data hub upstream community project. And then that is what we have is our downstream product with OpenShift AI. And when you think about it there are very few companies like Red Hat who have not only learned about open source but they've survived all the myriad of changes that have happened and we've thrived in open source. So with open source Intel and Red Hat it's in our DNA it's what we've done. Intel has decades of experience in this space going all the way back to being a key contributor to Linux. And so we've both grown from open source we've lived in open source communities and we're pushing the envelope even more in terms of how we can bring more innovation to the AI space using true open source patterns. And when we talk about OpenShift AI what we're really trying to do is provide that platform that unifies the data scientists and the application developers.  And then we're using OpenShift as that environment because OpenShift's already the platform that application developers know they love it. They work in Kubernetes it allows them to scale up their applications. And so giving them, giving application developers that AI platform that's in the same ecosystem the same kind of patterns, the same ideas of providing the ability to manage your applications we're applying that to machine learning and what we're doing with OpenShift AI is bridging that gap between ML ops and operationalizing your models and the challenges there we're bridging that with what we're doing on the DevOps side of things and providing that consistent platform to be able to do that. When you start to look at how you piece this together I mentioned the application developer I mentioned the applications that they build and then I mentioned the data scientists and the ML ops and the models that they build. The importance of all of this is because we're moving towards a world where every application needs to be intelligent. It's now the new differentiator for apps coming out. If you are building an application and you're not thinking about how AI can be infused into it and it can augment the benefits to a customer then that's something that your competitors probably are thinking about. We already know almost every C-level executive they're asking these very same questions. We know we need to do something with AI but we don't quite know what and a lot of them are questioning well what is the right avenue to go down? Can we even do these things in a way that protects our data provides the capabilities that our customers are looking for? And so when you look at OpenShift AI and the work that we've done with Intel it allows customers, it allows developers, partners to be able to do the entire data science process and accelerate their time to market. And so on the top part of this you have the cycle of building out your models. You gather the data, you do some fine you grab a model from the open source it could be hugging face could be somewhere else and you wanna do something with that. You wanna be able to fine tune that model do some rag augmentation on that and then deploy that model into an environment that's sustainable. And the data scientists in the MLOps engineer they're gonna cyclically go through and ensure that that model is retrained, retuned for the new data that comes in. Now on the bottom side of this you have the application developer which has a very similar cycle. They develop the code, they go through QA they deploy the code into production and then they monitor it as well. Having that same consistent platform to be able to do all of this work that accelerates the time to market. So now you have OpenShift and OpenShift AI with all of its capabilities providing the right automation for you to be able to do this at scale and do it quickly and really break down the barriers for the application developers to interact with the data scientists. It's one cohesive platform. And so we have all the tools here to be able to help you get your models into production faster, help you reduce the costs with the work that we've done with Intel to optimize the infrastructure that you have and then make it to where the data is the differentiator for you. Having a platform where you can add your own data to the open source models be able to fine tune on your data be able to do rag on your data. That now makes that model that you've done this with the differentiator for you. It's different than if you just take a model from upstream and you immediately add that into your application and there's no differentiator. With these tools, with this framework and this platform, you have all the ability to be able to really create that differentiator experience. And so now I wanna pass it back over to Sid. He's gonna tell you about all the fantastic things that have been going on with Intel in this space and all the innovation that they've been a part of. Thank you, Sharad. Let's go to the next slide. So I like to actually start by saying that, Intel as a company, we are committed to a vibrant open ecosystem for developers, which includes compute, pervasive connectivity, cloud to edge infrastructure, artificial intelligence, as well as sensor. Now, when we talk about open ecosystem, what we actually mean is it includes open source software, open source hardware, open standards, open specifications, open APIs and open data models as well. So like I said, we promote openness, we promote choice and we promote the trust. Intel has done significant contribution over the decades to the open source and the open source community. So Intel has over 20 years of investment across hundreds of independent projects, over 19,000 software engineers. It's number one Linux kernel corporate contributor since 2007. It's top 10 contributor to Kubernetes and over 300 community managed projects are run by Intel, 700 member foundations and standard bodies, six architectures supported in one API, 700 GitHub projects. Intel is also the Chrome OS leading contributor. So significant contribution more than 20 years over more than 20 years with that, I would like to go to the next slide. So this is the Intel vision actually to bring AI everywhere. So what we mean by this is to empower every user at every level by providing multiple technologies at all the layers where they touch. So let's start reviewing this from the top. So right from the large to small, the goal is to unlock the AI continuum with novel applications. The next layer from training and fine tuning all the way to inference and deployment, streamline the AI workflow with AI software that will be made available in the open source. All the way from cloud to client, including the data center, simplify the AI infrastructure with scalable systems and solutions and then create silicon offerings from AI specific to general purpose, all the way from Gaudi data center GPUs, that is Xeons, Arc and Intel Core. That is the Intel vision of bringing AI everywhere. Let's go to the next slide. So when we double click on that high level vision conforming to the similar layers that I just reviewed, this is the whole offering that Intel actually provides and most of these libraries and frameworks are actually in the open source that are available for use. But please don't get overwhelmed with it because they are meant for certain specific purpose. I will go over at the high level but not get into each framework and each library. So these frameworks actually span over client, edge, cloud, as well as data center. Let's start from the bottom layer, which is the foundational software. The foundational software includes firmware, bios and simulation, operating environment in kernel, virtualization, orchestration of edge, as well as cloud native. I'll point out a couple of important ones in each of these layers. So in the operating environment in kernel, you obviously see our partner here, which is Red Hat, prominently featured there. In terms of virtualization, we support multiple different softwares, including Kubernetes and VMware. When it comes to languages, frameworks, tools and libraries, we support multitude of tools, languages and frameworks in libraries such as SQL and one API by Intel as well as one CCL. And then in the AI realm, we support large number of libraries which are actually optimized by Intel, including NumPy, Python, PyTorch, TensorFlow, Cycadline, BigDL and so on. The topmost layer is the solution services and platforms. So Intel runs several developer programs and resources. I encourage you to go to the Intel website and take a look at them. In terms of platform services and solutions, there are multiple platforms and services as well as solutions that are supported. In terms of AI, Intel has created multiple things such as OpenVINO as well as Sigopt along with ConvergeIO. Let's go to the next slide. So there is always a debate between the specialized AI models versus the large foundational models. There is of course a cost associated with it which I will cover in the next slide. So the advantages of going with a large foundational model are they are incredible all in one out of the box versatility for text, programming and textual language learning as well as plain text summarization. Surprisingly compelling outcomes it provides you but there are certain challenges. The challenge is that they are very big over 100 billion parameters to provide that versatility and therefore they're very expensive. The next slide talks a little bit more about the cost and we'll go into detail but it's about 4 million to train 3 million per month for inference. The large model also hallucinates and has lack of explainability as well as intellectual property issues and they're also frozen in time because they're trained on a particular sample that was taken in a particular time. Whereas if you come at the domain specific models the advantages are they're 10 to 100 X smaller models while maintaining and improving accuracy. They're economical on general purpose compute like Sharad mentioned. Many of them you can actually leverage the CPUs and significantly reduce your cost instead of using the GPUs. In terms of correctness the source attribution as well as explainability is available and it utilizing private it can also utilize private or enterprise data. You can actually find you and all do rag on them to be more specific to your business. They could be continuously updated with new information. There are several challenges as well. Some of the challenges are reduced range of tasks because they are not so big and they are not so versatile and it requires a few short fine tuning as well as indexing. So let's go to the next slide with this. I talked about the cost. Although this whole area of LLMs has seen significant revolution there are advantages but there are limitations as well and one of the biggest limitation is actually the cost. If you look at the training cost, GPT-3 cost about $1.65 million. That's 3,640 petaflop days costs if trained on Google TPU version three where a GPT-4 training cost was about $40 million. That's 450,000 petaflop days over 7,600 GPUs running for a year. Now if you come to the inferencing cost, the chat GPT inference cost is about $40 million to process prompts per month with 100 million active users and as you know, they reached that number pretty quickly. If you look at the Bing AI chatbot cost, it's about $4 billion. Bing AI chatbot to serve responses to all of the Bing users. So the cost is humongous for these large language models. And with that, let's go to the next slide please. So as I've discussed before, the specialized models actually enable scale and they're extremely useful in multiple verticals. I will cover some of the verticals and their applications here. For example, in the education vertical there could be use cases such as teacher assistant, student study buddy, or parent chat portal. In the health vertical, it could be drug discovery, doctor assistant, and patient family chatbot. In the finance vertical, it could be algorithmic trading which is becoming very popular, customer portfolio assistant, and the risk or credit assessment. In the retail vertical, it could be product promotion, customer interface and sentiment tool, and image shopping aid. In the government sector, it could be government services assistant, document search and summarization, and live language translation. In the energy sector, it is energy consumption forecasting which is extremely important, and then operational performance as well as energy trading assistant. In the automotive sector, it could be autonomous car development which has caught the imagination of the public, multi-language in car aid, and supply chain optimization. The supply chain optimization is applicable to multiple industries, including retail. In terms of manufacturing, it could be factory automation, predictive maintenance. Predictive maintenance is a very important field in which it could be predictive maintenance of the shop floor equipment. It could be trucks, or it could be airplanes as well as other vehicles. It could be precision agriculture, and in terms of telco, it could be personalized customer service, network automation, and operational performance. With this, let's go to the next slide, and I would like to hand over to Marcus to discuss the hackathon and the results of the hackathon. Thank you, everybody, for your attention today. Have a good rest of the day. Thank you so much, Sharad Sidd. That was a pretty impressive presentation, and I enjoyed every second of it. Thank you again for joining us today. I will let you go, literally kick you out of the stream. I always wanted to do that with general managers. So thank you again. Goodbye. And while I remove everybody else, I do add my old friend Jeff back. Jeff and I, we basically do the same things. We do technical marketing. We talk to people. We look brilliant on screen, and we have the honor to not only talk about our technologies, but also talk a little bit about the past and the future. The past, because we're talking about the code shift hackathon, about all the amazing submissions that we got and the future, we are gonna talk about the winners of the code shift hackathon. So with that, I'll add your amazing presentation to the stage, Jeff and you. All right. Looks like it's up. Okay. Thank you very much, Marcus. You know, I heard Sid and Sharad talk about the future of AI. And I think what you heard a lot is you heard the word developer a lot in creating applications. And I think it was Sharad who said that app intelligence is the new differentiator. I thought this was really interesting. So this was, well, I had the luxury and pleasure of planning and executing the code shift hackathon. So the goal of this hackathon was to bring together developers from across the world to demonstrate their skills and also their creativity. Using this Red Hat cloud native platform as well as with Intel technologies. So the hackathon explored, how do you build cloud native apps using Red Hat's OpenShift platform that includes things like Java runtime support with Quarkus and Spring Boot, event-driven architectures with Apache Kafka, integration with Camel, SSO, all these things. And most importantly, developers were given access to be able to build, deploy, and scale intelligent applications too using some Intel OpenVINO and analytics tools. So it was really, really cool to hear Sharad and Sid talk about kind of, I wouldn't say in theory, but all the things that we provide together both Red Hat and Intel from a platform perspective to give to developers. Now, what do they do with that? How do they integrate those models and into these applications for the cloud? So it was really exciting to see, first off, to organize and to see what developers created. So first I wanna talk a little bit more about the hackathon first. So a couple of things by the numbers. The dates where the developers had from September 26th through November 3rd to create an application, you'll notice that's not a lot of time and you'll see what they did was quite impressive with the short period they had. We had almost 500 registrants across 57 countries. In the end, there were 30 submissions that met the criteria, which far exceeded our expectations. We had developers who were full stack, they were back end, they were front end. We had AIML developers. People came from having skills around OpenShift and Kubernetes. We had Java developers. We had all across the board and we had some people from university who actually learned all this, which is flat out astounding. So we are giving away $70,000 in prize money, $35,000 to the overall winner, 15 for second place. Third place will get 10 and we had two runner ups who will get $5,000 a piece. Our criteria was pretty straightforward. We wanted the apps to be innovative, like how innovative were they in terms of creating solutions and products? And actually someone created a developer tool, which was really neat. So what did the UI UX look like? Not every app has to look beautiful. Some do, some don't, but the UI UX was a part of it. Potential impact, like I mentioned impact to end users or it could be impact to developers. And then lastly, how well did they integrate the Red Hat and Intel products like the OpenShift and Open Vino from Intel? The judges were from both Red Hat and Intel and the submission criteria was pretty straightforward. Had to be an OpenShift, or excuse me, an open source application. We asked for a five minute demo, kind of in their own words, supporting documentation, architectural diagrams and such. So that being said, we took those 30 submissions, got it down to, I'd say about, we down selected to about 10 finalists. Really tough to choose. There were some great, great applications. But ultimately we took those 10 and got down to our five finalists, which one of the applications you see here will win money. Everything from, we had a great AutoDocs AI which took, used AI to transform source code into documentation. We had someone do a open, like a marketplace for buyers and sellers. We had someone like Pertas, they created a performance testing SaaS solution for using virtual threads with Java 21. Vino Pharmacy, which utilized AIML to create a whole online pharmacy. And then Open City Hub, which is a smart city management. So these are in no order. So these were our finalists. So let me move a little quicker here. So our runner up was Open City Hub and Pertas, that's the performance testing. Open City Hub, they both will win $5,000. Open City Hub was developed by Kunan Singh. Once again, this is a smart city application that had things like traffic management, pollution management, health management, really neat microservices based cloud native architecture. It utilized, deployed an open shift, used Kafka, Redis, RabbitMQ. And there's a link at the bottom here. You guys don't have this presentation. So please go to codeshift.devpost.com, codeshift.devpost.com. That has, thank you so much, Marcus, that has all of the, all the submissions, not even just the winners, it has every single one of them. So great site there. The Pertas developed by Himanshu Gupta is an opinioned way of doing the performance testing using virtual threads. Really neat, like I said, we thought this was so innovative and useful, not necessarily from an end consumer perspective, but from a developer perspective to do automated testing. So really neat, deployed an open shift, used Kafka, Redis, RabbitMQ as well. Next up is the third place finalist that will win $10,000. And I talked a little bit about this, Autodox AI, this was developed by Atabayo Omolumo. Hopefully I got that right, Atabayo. Once again, this used AI to transform code in GitHub into readable human documentation. So it integrates not only with GitHub code, but then also integrates long language APIs to create documentation. They used OpenShift Knative. So it's a serverless application on top of that and obviously integrated GitHub. So really cool technology. Almost want to take a step back and talk a little bit about how the, you'll see this commonality with these finalists that they kind of use all the tools in the Red Hat OpenShift application platform at their disposal, everything from these Java frameworks to serverless and all these great things. So that was really cool to see. Second place in the first two, the first three, the first two and three were quite amazing. Second was OpenShift Hub. This is a really cool application. So they will win $15,000. It was developed by Vishal Vats. I'm not gonna do it justice. I really implore you to go to codeshift.devpost.com to look at this. So Vishal created a marketplace that essentially connects buyers and sellers, but it's much more than that. He included services for not only creating an e-commerce site, but there's a browser extension that does things like, uses AI to do visualizations of classification for images you may see in a web browser. And he integrated a WhatsApp bot as well. So really cool stuff. I kind of try to put a scattering of all the screenshots where you can kind of see what this application does. One cool thing that I liked about this application or we liked about this application was how it also had analytics for the seller to look at their distribution for what users were buying things and what categories were they looking at. And it actually integrated some ideas around strategizing what to do and sell next. So excellent job, Vishal. It was almost a shame this one came in second because the last one was so good, to be honest. So with that being said, the winner of the codeshift hackathon is Vino Pharmacy. Thank you so much for that. Ooh, and a little drum in the end. This application, like I said, they all were really good. This particular one was developed by Motha Kumar. Only V, I don't know Motha Kumar's last name yet. So I'm gonna have to talk to him and dig more into this. Really, really cool application. I tried to do a GIF on this one so you can kind of see what's going on here. It's an online pharmacy for doctors, patients, and administrators. It saves the doctor's information in the blockchain. It offers a lot of functionality again. So it puts basically the patient, the doctor together. They can do things like video chat, text chatting. They can upload visualizations of their issues or on the camera and uses AI and intelligence to try to classify that. It allows doctors to create prescriptions and share those out via a barcode. Lots of great stuff here. Once again, I encourage you to please go look at it. With these top two and probably three winners, we're gonna do a series of blog posts to really dig in to their experience using the platform and using how do they integrate these models. So this one in particular used OpenVino extensively. So you just heard Sid talk about it. This is a great example of what developers can do. How do you integrate those models in? So he used models around medicine names and wound recognition, which was really cool. Deployed an OpenShift. It utilized Quarkus, which is a Java framework. Utilized the blockchain. So we thought it was just a really, really great application. So thank you to all the, a lot of people. Thank you first to the participants. DevPost is a great hackathon hosting site. Please look at that if you're interested in doing hackathons are great. Red Hat developer team offered us a free sandbox that anyone can get to. So sandbox is a hosted OpenShift platform. Easy to get things like Kafka, open data science and all the Intel products were there. Easy to use, Quarkus and those kinds of things. So please, if you're interested in playing around with this platform, don't try to install it locally. Go to developers.redhat.com and sign up for a sandbox environment. It's a great way to get started. And lastly, thank you for all the judges who spent time going through these 30 great applications. So congratulations to the winners. You will see more hackathons from Red Hat and hopefully Intel in the future. And thank you very much. Thank you, Jeff. And again, big thank you to Sit and Cherar for spending the time and working us through the combined offering. With that said, I think we have 14 minutes left before we gonna continue in the various different, what is it called, stages? Yeah, I think so. So we have three stages of content waiting for you on top of the hour. So go grab a coffee, but be back on time. We'll be waiting for you. I'll be waiting for you, especially in stage two. And as you can see, I've switched my head over to this beautiful Quarkus hat. We're gonna talk about LLMs and Quarkus, but there's much more to come. So thank you so much for now and see you in like 14 minutes, 14. Bye.


esn't make the section any less effective Since the whole of article 6 of the of the bylaw is intended to be used in conjunction with the dimensional table This amendment moves the text of the of the footnote to section 6.6 And that's two more down planning board unanimously recommends this article Thank you. It's my understanding that the select board recommends but does not wish to make a statement It's my understanding that the finance committee has no position and doesn't need to make a statement This also requires a two-thirds vote. Is there a discussion before we come to a vote? I see no hands Well, is there a hand? Yes Okay, identify yourself with the microphone Helen Berg precinct one. Um, can you show us a map of the property? So, um, I believe there is no map here This is zoning bylaws that may be covered in many different places And it's just rearranging the wording of the bylaws to make them more readable basically So there's no specific map. It could be anywhere in town It okay. Um, would it be correct to characterize this as a pro-growth? No, I don't think that really makes sense. No, okay There's further discussion before we come to a vote I see no hands this requires two-thirds all those in favor of the motion under article 33. Please say aye Opposed please say no The eyes have it it is unanimous And article 34 I call on mr. Burt whistle to make a motion I move in terms of the article Motion is made and seconded you may speak to your motion Actually, I'm sorry. Ms. Chow is going to speak to this one. I'm Maria Chow Um Article 34 zoning non substantive corrections To see if the town will amend article 11 administrative and enforcement of the zoning bylaw This article is being brought forward to reduce the time and effort of the town meeting and other boards and committees spent on reviewing changes to the text of the zoning bylaw That amount to proofreading and copy editing while not changing the substance of the bylaw Several towns have adopted this type of language in their zoning bylaws some of which are concord mass Dalton mass reading mass Well fleet and Hillsborough, New Hampshire An example where this article would prove useful is looking at section three two of the zoning bylaw where use regulations Specific to so-called special districts are listed is poorly organized such that the numbering protocol has been exhausted And no new substance subsections can be added to describe regulations for any potential new special districts Uh, let's see the article Would add a new subsection to section 11 of the zoning bylaw which provides for amendment of that document The procedure being proposed would constitute amendment only in a technical sense practically It is about correcting errors or changing numbering It would it would require a discussion of the proposed change at a public hearing of the planning board and be Sign off by the town clerk who is the keeper of the town's official records The proposed amendment would allow strictly technical adjustments to the zoning bylaw and would streamline suggested as Editions to sections rather than be hindered by the current organizational limitations of the zoning bylaw numbering system It would relieve the town meeting of the time effort and paperwork That would otherwise be needed to make minor adjustments to the zoning bylaw that do not change How the bylaw works or the substance of any provision or zoning The plan where may request a change that inadvertently or otherwise Does affect the substance of zoning and should be subject to the approval of town meeting However, it is hoped that this risk would be mitigated by the requirement that the proposed change be implemented by the town clerk Who may decline to do so at his or her discretion um The zoning subcommittee reviewed this non substantive corrections this past fall and it was decided to bring this article and it was a Six zero vote with three absent to recommend this article. Thank you Miss Kruger for the select board. Did you want to say something here? Nope select board recommends it, but doesn't need to make a statement finance committee has no position Doesn't need to make a statement. It requires a two-thirds vote. Is there discussion before we come to a vote? um Yes, about boroughs back there white card Jim Oldham piecing five. This sounds like a reasonable proposal. Uh, just a question with the Two votes that we just had the two previous articles have qualified as uh Under this could could change. They were Not changing the content of the law. They were changing Footnotes into the body of the zoning and i'm curious if if that would go If this would be broad enough to allow something like that. Thank you. Mr. Crowner um I would say that these the previous two articles would not qualify. Um, they would these those uh articles actually Deleted some language and moved and added language the same language, but but it it was actual Moving stuff around it would allow us to not have to come back to you When we're done deleting all the footnotes, we're going to come back with another article that that um Who's all moves that right now with the footnotes are a b reserve reserve reserve? We're going to we're going to um be able to move all those up Delete all the reserves without having to go to town meeting. Thank you. Um, yes front row Rob caster precinct three. This is a more serious Comment and I don't know whether This it's probably impossible to to amend this at this point, right? It's a friendly amendment, but The town clerk I would hope would report to town meeting the results of such De minimis changes That would be the one request and perhaps this could be amended at a future town meeting or your practice could be And the other thing that's maybe more substantive. I would I would hope that Such changes would would be effective although they shouldn't have any effect But if there is some effect after such a report to town meeting It doesn't require the approval of town meeting But it's sort of a notice to town meeting in addition to the public hearings that you're proposing So I'd just like to put that on the table for a future town meeting To read legislative necessary or make it your practice Yeah, and just you know if you you didn't make a motion to amend if you had I wouldn't have accepted it because I would have felt It wasn't in scope, but it's a great comment Um, yes, I see a hand there Carol gray precinct seven so Let me just read the by lock as I think it's important to know the exact wording After public hearing in accordance with article three public hearing of the planning board rules and regulations The planning board may request and the town clerk may make now. Here's the key language Non-substantive corrections including doesn't say just these things it says including so that means it can include other things too Including the following reordering and renumbering and correcting cross reference numbering and typographical errors So those are things are harmless renumbering things like that, but Who defines what non substantive? Changes are there have been countless bylaws that have come to this body with the former planning director in particular saying this is a technical fix Well, in fact, they were substantive changes that Helped or hindered projects down the road and basically this is shifting the power to decide changes in the by-law away from town meeting to A planning board that's all appointed by one person and instead of having the elected body Which handles zoning make the decision. I think the last two votes showed that when there's a really technical fix They go through in two minutes So I don't see why we should shift the power to determine what's substantive and what's non substantive from this body Quick fixes go through quickly I think we should vote no and keep the power for zoning in town meeting Further discussion before I come to a vote Yes, I see a hand there Janet McGowan precinct eight I I have a kind of a comment or a question or a request which is for those of us who look at the zoning by-law and Try to keep up with it It's actually really hard to to keep up with it online or get a hard copy from the clerk's office And so I don't know if you can figure out some way when there's been a non substantive change to sort of put it on the List serve saying hey, we just changed this so The six of you who are really into this and want to follow it Can make the change at home, you know or something like that just to kind of Communicate with town meeting a little bit Because I have been occasionally working on a By-law version that is out of date or is missing a page or a correction and things like that for people who Are sort of grooving on these things We're ready to come to a vote I see another hand there second row Maria Kepicki Precinct date so my question is if somebody felt after a change if this goes through and somebody felt afterwards that it was not non substantive What would be the recourse? What could one do? About that But if the front table have a comment on that Yes Yes, I'm sorry There there would be a notice in the paper and A notice on the town website about a public hearing that the planning board would hold so people would be Able to come to the planning board meeting and discuss this or send A letter or a memo or an email to the planning board In advance of the public hearing or at the public hearing if they had concerns about a change So, you know, there would be a public process Thank you Yes, right here second row William DeVore precinct five. I support this to me. This reads more I have a background as a graphic designer to me. This reads more like an issue of formatting the actual article rather than any sort of substantive Content changes, so I'm wondering if maybe That language would need to be inserted instead of saying non-sensitive corrections Maybe just adding formatting in there because you know the numbering and reordering that's not really Changing any of the content of the law itself Well, we can't add any language to it at this point in time. Um, are we ready to come to a vote? Yes in the back corner there Felicity Callahan precinct nine in the presentation It was said that the town clerk had the discretion to agree to make these corrections or not Is that that's not that's not really Put into the wording there. Is it all in the word may where it says to the town clerk may Do it is that the may that gives her the discretion to say I don't think that these are non substantive Yes, that's the word that gives her that authority or that discretion Yes, I actually call any of my names here good. Um, yes third row in the aisle right there Jerry wise precinct eight. I call the question Most of the previous question has been made and seconded before we come to a vote I am going to give a couple quick reminders Um, clean up after yourselves return your electronic devices and a thank you to everybody who stayed tonight Till the end of the meeting and now we're voting on the motion of the previous question If two-thirds of you vote. Yes, we will then come to an immediate vote on article 34 All those in favor of the motion of the previous question. Please say I Opposed please say no Moderator here's two-thirds. We now come to a vote on the motion before you It's just in terms of the article for article 34. This requires a two-thirds vote all those in favor. Please say I Opposed please say no Moderator here's two-thirds We have an electronic vote You may enter your vote anytime This is on article 34, which requires two-thirds Anybody having any problems with their voting devices? We have 109. Yes 33. No, we have achieved two-thirds. I call on miss. So the article has passed Calling this brewer to make a motion I move to adjourn to thursday tomorrow May 11th Motion made in second into you wish to speak to this motion We said we would give people the option I am well aware that there's a charter commission meeting tomorrow night and lots of other things in people's lives But we said we would give people the option of meeting tomorrow night And this is not debatable. So we now come to immediate vote requires maturity majority vote Yes, we will meet tomorrow if the majority votes no, we'll have another motion from this brewer All those in favor of adjourning till tomorrow night. Please say I I Opposed no Ms. Brewer. Let's uh Nose have it. Ms. Brewer make another motion, please Hey, hey folks. Hey be polite. Let the motion happen here I move to adjourn to monday may 15th Motion is made in seconded. Do you wish to speak to that motion? No. No all those in favor of adjourning to monday night Please say I Opposed no We are adjourned. See you all next monday Thank you