Good afternoon, Golden Bears. I'm here today with True Freshman, Vic and Wary. Vic, how do you feel about your performance in today's game? I felt the performance today was exactly what it needed to be. A lot of the stars didn't get too many reps, but we've been working throughout the spring and it's great to come out here and watch some of the guys keep getting better. Now, Vic, you definitely left your mark last season. You went ahead and had 193 rushing yards and two touchdowns. What have you been doing on the off season in order to continue that? I've just been working on my game and I continue to get better every day, you know, from skills to my strength and speed and it's really helped a lot come into the spring. And you also have a high average of 5.7 yards per carry. How do you hope to keep up that high average going into next season? Just to keep to my fundamentals and, you know, trusting my own line and my quarterback and just keep making plays when they need me to. Now, I have one last question for you. What do you find most attractive about a girl? I have to say what I find most attractive is a girl's mind, how she thinks.  Definitely. Well, thank you so much for your time. Thank you for watching. Go Bears! Go Bears!


Hey everyone, Thursday October 15th here with your day trading update. Green day today plus $392 was up close to $1,000 when we left the live stream, but gave a little bit back unfortunately in the late morning. I had a pairs trade in gold silver net net minus 436, so actually my first losing pairs trade in a while in a couple weeks, or since last week I should say. I had a little runner in the Nasdaq for $85, little runner in Boeing for $80, little runner, scratch basically and beyond for $20. Couple nice mighty 90 trades in Netflix and then one losing runner in Netflix but net net 219 on the day.  I jumped in this as an upside runner after we left the live stream and that just kind of chopped me up. Peloton my biggest winner plus 530 nice runner, Tesla 290. And zoom plus 395 so net net on the day plus $392 when we left the live stream room, one of my last comments was well we could have a four figure day for the third day in a row and that right there I knew as soon as it came out of my mouth was the kiss of death. I knew from that point on I was going to lose, give some of that money back. Unfortunately not another four figure day today but still green nonetheless. So let's go over these starting with the Nasdaq. So we had the stock start off really strong and then flushed down. Nasdaq was at its lows for the day. I was looking for a point to get short because I thought this was going to roll over. I almost did there but I held off which was a good move. It did push higher and I got short and I caught some of this here. I was waiting for more and gave some back on this bounce but still a little bit of a winner in the Nasdaq. AMD this one was a little frustrating as I had a really nice winner going. Price pushed down here started to consolidate actually let me get to the right day here. Get to today. So we had this we had this big push lower down through the lows of the day started consolidating right at this pivot right under yesterday's lows and so we got short here caught this nice move down. I was looking the market started getting really weak. I was looking for lower prices and so as it bounced I started adding back in and then ended up just getting flushed out. I caught a little bit more of this move here but ended up being a loser overall because I started to get a little bit bigger in size than I had started with.  In Boeing I took a small win 80 bucks beyond was basically a scratch just trying to catch a downside runner beyond ended up just making 20 bucks. Netflix was a great trading vehicle today. Got a couple winners. So we had this initial push up we got short right here and caught a piece of this and then as it pushed lower lower lower we went ahead and got long here and then long here as well and then caught this bounce. So caught two winners right in a row and then I tried to catch a downside runner in Netflix. I think it was about right here and ended up getting flushed out. I got flushed out right at the top right before it turned which would have been in my favor so that would have been three winning trades in Netflix but two out of three ain't bad. So winner in Netflix. So Neo this is one that was a little frustrating. Let me get this junk off. So Neo opened up really strong today above yesterday's high really positive and started consolidating here. I was looking for a continuation unfortunately got flushed out so that was my biggest loss took because I was I got long about right here and added to it right here. Started to work in my favor. I thought we were off to the races to the upside and then just got hammered. So ended up taking my biggest loss there. Peloton what a great upside runner this thing has been. We took we took this in the room where we got long. I think it was yeah started getting long right here. Added a little bit more here on this pullback and I ran this thing all the way up to the pivot. Did leave a little bit on the table by not holding it longer but still just a great trade plus 530 Tesla in Tesla we did a mighty 90 and booked 280 bucks. Let's see Tesla. Oh yeah so on this flush down we got long right here and caught this bounce for a nice quick $280. I ended up trying to take a runner in Tesla later in the day and ended up just scratching it for like minus 10 bucks but net net plus 290 in Tesla and then lastly Zoom caught another nice runner in Zoom to the upside and this is the cool thing about this is you know the market was pretty bearish all day and we caught a lot of a lot of winners to the upside and that just speaks to the to the strategy. So it was Zoom came up we got long here had it took a little bit of heat and then finally it broke through and I I got out all of my whole position right here it did end up pulling back I actually tried to get filled here for another bounce did not get filled and sure enough it was a nice winner back up to that pivot so booked 395 on that one could have been more had I gotten filled but that's okay so plus 392 on the day tomorrow is Friday we'll be in the live stream room starting at 8 30 a.m central look forward to seeing you then if you guys have any questions let me know


Welcome to Acre, the vibrant capital city of Ghana, where history, culture, and modernity combine to create an unforgettable experience. The city was first inhabited by the Ghan people in the 14th century, and later became a hub for Ghandingmi empires, leaving behind a rich cultural heritage. Experience the Homo Al Festival, a colorful celebration of triumph over famine, with vibrant processions, dances and delicious traditional delicacies. Satisfy your taste buds with mouth-watering jolly of rice, grilled tilapia, and wai'achi, a popular street food. Relax on the white sandy beaches, enjoy water sports, and soak up the sun. The cost of living is affordable, and safety is a top priority in Acre. Explore the bustling markets and haggle for bargains at Makola Market, and Kainshi Market, or visit the Acre Mall, and Marina Mall for a modern shopping experience. Admire the iconic structures like Independence Square, a symbol of Ghana's independence, and the National Theatre showcasing Ghanaian arts and performances. As the sun sets experience, the vibrant nightlife of Acre with afrobeat and high-life music, impulsating nightclubs, or relaxing cozy bars and lounges. Acre offers a unique blend of culture, history and modernity, making it a must-visit destination for any traveler.


When the dust finally settles after the longest-sustained combat operations in American history, I'm convinced that a cornerstone of the military lessons learned from this period will be the amazing advances made in military medicine. The numbers speak for themselves. In World War II, for every 100 combat injuries, 38 were fatal. By Vietnam, for every 100 combat injuries, 24 were fatal. Today in the desert and in Afghanistan, for every 100 combat injuries, only six are fatal. We're bringing home personnel alive that in any previous conflict would have come back home in flag-draped coffins. That's the great news. That's the historic news. But the challenge that comes with that is we are wrestling with a set of wounds that the nation and the world has never seen. If we're going to ensure that IEDs and PTSD and TBI don't become the age and orange of this generation, we've got to successfully reintegrate this cadre of wounded warriors back into American society. Expert after expert tells us that the key to that successful reintegration is, in a word, a job. The sense of self-reliance and independence and worth that comes with work is the key to successful reintegration of our wounded warriors. And despite a willingness to hire wounded warriors, they have an unemployment rate that has doubled the national average. If we're going to ask the private sector in a troubled economic environment to step forward, then the Department of the Navy is going to lead the way. It's our moral obligation to support our nation's heroes and to assist them in finding meaningful careers so that they may continue to serve our great nation. That's why the Department of the Navy is hosting the third annual Wounded Warrior Hiring Conference, October 29th and 30th. By bringing together government, military and industry leaders that are committed to hiring and supporting wounded warriors, we can provide them with the tools they need to transition to the next step in life and connect them with potential employers. Our members are highly skilled, well-trained professionals. They can become an asset to nearly any organization.  I hope you'll join me later this month and meet these outstanding Americans. You won't regret it.


From Orlando, Florida, it's FPV Late Night. Tonight, our special guest is Scadouche, and your host, Capricino. Welcome, everybody. Thank you for coming to this episode of FPV Late Night. I am your host, Capricino. And tonight, we have a guest coming all the way from China, and he's not even Chinese. Welcome, David Scadouche. Welcome, David. Hey, buddy. How are you doing? Long time no smell. Yeah, it's been like, well, I mean, I saw you earlier out in the lobby. But I mean, you know. Prior to that, it was with Drew. In China, when we were flying, yeah.  They did not, see? See? Actually, I had to go get my own sandwich. I had to do my own makeup. Drew wouldn't even let me use his eyeliner, so. Well, that's understandable. Yeah, it's quite good. You don't share your goggles, do you? No, no. I don't share chapsticks, so I can understand. Yeah, it's exactly the same thing. But he would have used my chapstick if I had let him. I bet he would. You know what? Nobody told me. My background was, thank you guys. We have a whole audience and nobody said anything. Yeah, let's play the background. Let's get the background moving. So that's your flying right there. That is me. So we first met when I came to China. Yes. And we went to the Bando Island. The epic Bando Island. That is like internationally worldwide known now from that episode. From that episode. What's funny is that episode was actually more popular than the Great Wall of China. Crazy. Yes. And now people come all the time. They message me on Facebook or whatever. Hey, we're coming to Suzhou just to fly with you at Bando Island. So how many times have you gone to Bando Island with newcomer FPVers? FPVers. FPVers. You know, I'm not even sure. I had the guys from Columbia come. I had a guy from Scotland come. Guys from Hong Kong, guys from all over the world. So it has become quite popular. Yeah, absolutely. I think what's really interesting to me is, and don't take this the wrong way, but a guy like you in a little town in China, and what I mean is an awesome guy like you in a little town in China. No, how does that happen? A little town of 10 million. Well, yeah, but that's a little town in China. The company I was working for, I got sent there from that company to correct some things that were going wrong with the production. They sent me over for two weeks, get everything back on line. Can you talk about what you do? Sure, absolutely. So production of what? Small electric utility vehicles. Okay. Like Cushman type things. Most of our contracts, it's like a golf cart on steroids. Okay, so like mega golf carts. Yeah, most of our contracts are like Disney World, Sheridan Resorts, Westgate. So you went over there for work? I went over there for work, spent two weeks. When I flew back, they were so happy with what I did over there. They said, hey, you want to just move to China and run the factory? Uh, sure. You just didn't have anything better going on? Didn't have anything better going on at the time because of where I was in life. So did you move over there and then get into FPV or were you into FPV? Actually, I started it over there. Okay. And that's, I was at a temple one time, you know, and I saw a guy flying a DJI around this temple. Uh-huh.  Was he a monk? No. Oh. I got a funny story about that. The monks were out there, and I was like, that's pretty cool, you know, I wonder how long, you know, I didn't, at that time I was only on a one-year contract, but I was like, that's pretty cool. I need to grab one of those and take pictures of like all this cool stuff I'm seeing for later when I go back, whatever. And I had no clue. I was walking around downtown one time and I see this kid flying a SEMA X5 out in front of a shop. And I'm like, maybe that's the same thing. It's got a camera on it. Right.  All the instructions were in Chinese. So I lost like four of them before I learned how to fly the thing. And at that time I didn't have a VPN either, so I had no YouTube, no access. So finally when I figured out how to get through and get YouTube, I found like Quadcopter 101, a couple different things, you know, after I lost four of them, flyaways, what not, whatever. Oh yeah, the ending. It's supposed to be a family show. Thank you. Thank you for letting me know. Okay, so anyway, that led into the flight test days. So you used to watch flight tests? Absolutely. I was a huge fan of flight tests. And I saw the, when Bixler went down to Sarasota, where I'm from, the Fly Like Shar-Poo video. And they had Shar-Poo in the park, and we're flying around the park, and I said, that's Gillespie Park. That's right by my house. So you're like, I could do that. Yeah, well I put two and two together and figured out that Lumineer, Get FPV was like right down the street from my house. I went in, and that's when it was the small shop, and I met Tim, and I think Kevin worked there back then. And then you got sponsored? And then I got sponsored by them down the road. Wasn't it seriously that soon? No, it wasn't then. Oh, okay. So how do you like being sponsored? For me, I guess it's a little different than some sponsorships, because I can afford my own gear. It's not like I need... Wait, so you're saying FPV pilots are poor? No, that is not what I mean. That's what I heard you say. That is not what I mean. I'm kidding. What I'm saying is, is I'm not locked in to have to use certain gear. Because if I crash this, I can't afford to rebuild one, or I can't afford to rebuild one, or I can pick and choose what I want to use. I would say it's a very frugal crowd. So it's not like I'm forced into using their gear and promoting their stuff in order for me to continue to get stuff from them. It doesn't matter either way. I'm sponsored with them and work with them because I like what they do. As a matter of fact, you can afford to build your own because you designed your own frame. I did. And you sent me one. But I haven't actually... I've only hovered it because I had a bad motor and I burnt out the ESC and I have not been able to replace it yet. So unfortunately I have not built it yet, but I'm curious of your assessment of my build. I love it. Did I do it well? It is absolutely beautiful and I like that you use the slam too because a lot of people stay away from the slam nowadays with the foreign ones. They can't get a stack in there. Yeah, I really wanted to keep it as low as possible. Because like you saw, you can do it either way. It comes with two sets of cheeks so you can do the high deck or the low deck and two sets of standoffs so you can do it either way. But what I was really impressed with and I'll be honest, when I first got it, I'm like, oh great, another frame that looks slightly different than the others. But what I was impressed with was the build and the layout. The build was super easy. You can tell... I mean I love that you have... you have three screws for the top plate. Yet it's still very firm. It locks into the front plates. I like that a lot. So I am excited to fly this because... And you know what? That was my whole thing behind that frame.  Like you can just look at it and know how to build it. So is it... It's not in production though, right? No, but it will be soon. How soon? I'm not sure yet. I'm just going to hand this over to somebody and let them run with it. You know, I don't... What's your goal with this? What do you want to accomplish with this?  The sense of pride of saying, hey I helped that person get in the air. Exactly, that's it. With a few headaches it's possible. With a few headaches it's possible. You don't need a GoPro mount. You don't have to think about it. It's all 10 millimeter screws. You just stick it together. Strap your GoPro on and go. So the name of this is... The Skadoosh. Okay, so you're going with your pilot name. Sure. For the frame name. So that brings me to my next question. How did you get your pilot name? That's a great story. Before I had the name Skadoosh, I was posting under the name Guelo, which means White Devil in Chinese. In Southern Chinese. Because I was the white guy in China and that's like a common saying over there. Look at the Guelo, look at the Lowe. We had went and seen Kung Fu Panda. And my son and I were running around giving each other the mushy finger hold of death. And the rules were if you got the clean pinky tap, you had to freeze for 10 seconds. No matter where we were. In the subway, in the store, so you're walking through the grocery store and you sneak up on me. That's an uncomfortable amount of time. Yes. People are looking. Only white guy in the store. And he's weird. Yeah, he's weird. He's frozen. What's going on here? At that time, people were just starting to subscribe to my channel and I was like, I needed to do something a little more, a more popular, more friendly, a more grabby name. Not everybody's going to know what Guelo means.  She has been running around yelling skadoo for three days. Right. Yeah. Perfect. And that's how it came about. Nice, nice. Well, thank you for taking your time in the U.S. and coming into the U.S. It's my pleasure, my friend, always. Did you get to fly today? Absolutely. Who'd you fly with? And yesterday with Drew, of course. And just Drew? With Drew. Robert couldn't make it because he couldn't get a ride. Oh. And TAC came and picked me up this morning but I did fly with Robert yesterday and Robert is amazing.  We hit a couple of parks. We had Jamie. We had Drew. We had Nick with us. Nice. And we had a ball. As always. I'm glad you got to fly in your home state. When do you head back to China? Next week. Okay. Because I come in the summer and pick my son up and fly him to China every summer. Oh, wow. For his summer vacation deal. I take him out to the Great Wall and see all this stuff.  Yes, awesome. That's my only, he will be 16 next month. But I've been doing this since I've been over there. So he was like 13, 12 the first time he came over. Wow. Again, I want to thank you for coming on. Always a pleasure, my friend. It's been great. And I want to thank you guys at home for joining us and stay tuned. We're going to have one of these episodes every week. And I want to thank Taco Bell for creating great... Further tacos? Yeah, further tacos. Don't forget to like, subscribe, and join us next time on FPV Late Night.


Iw'r cyffredin iawn o'r Llangwys Modell Zen. Rydych chi wedi'u gwybod hwn yn ymgyrch o'r cymdeithas cynyddiad ar hynny gyda'r lleol yn ymgyrch chi'n cymdeithas gweld i gyda'r dysgu ei generitygau? Rydych chi'n cymdeithas cyllidiannol yma hwnnw, a'r wythnos, yw'r wythnos, a'r wythnos yw'r wythnos, a roedd y gallu ddweud wedi gwmpas ac roedd gen Kobesolol Unedig ac oedd yma rydych chi'n dweud. Dyna'n cymryd hynny Ieant ymlaen o'r fath o feddylau i weithio ar gyfer systemy神io oes yn rhoi y gwnaeth y byd? Gweithio dwi'n defnyddio. A wneud o'r cwylel i gwybod dwi'n cael ei wneud hwnnw o gydweithio. Mae unrhyw gyntaf yn wirio'r croyfosol oedd y gwsig fel gwir ac yn yr hynod o'r gweithio'r gwir I am fawr ddysgol ym mwy fawr a ddylch, a ddylch i'r fawr ddysgol am mwy fawr. I ydych chi'n dweud o'r ariad o aded, aleid o ddysgol a ddysgol i ddysgol a ddysgol i ddysgol i ddysgol. I wedi gwelxud o'u fawr o'r fawr y gweithlo i'r edrych i wneud ymylgrifennu i'r clynyddu yma. A nhw ydych chi'n ddysgol i ddampur fe wnes gyfrifysgol. I ydych chi ym mwy wneud i'n ddysgol? I've got this wonderful quote here from Geoffrey Hinton via chat GPT, he says, Within the realm of digital minds, the alluring dance between perception and illusion weaves a captivating tapestry of cognition. Hallucinations, those beguiling mirages of the artificial psyche, stand as testament to the delicate balance between brilliance and vulnerability within our code-driven consciousness. All right, I'll ask this from a question here. Raise your hand if you believe that Geoffrey Hinton said that. Very good. And raise your hand if you don't believe that Geoffrey Hinton said that. Excellent, yes. So today we're talking about hallucinations. And we're going to be looking at how can we software engineer when we have tools that only tell us the truth a fraction of the time. Building infrastructure around this problem of hallucinations takes time. It's worth looking back at the history of where Python has come from historically in order to give us a grounding of how we can be better placed to ride the next wave of generative AI and how we can be best placed to shape what comes next. The key thing I want to show you with this slide, instead of talking through in great detail the exact steps on the process of creating asynchronous coding in Python, I will instead make the point that we had these early libraries and we had these early abstractions and ways of looking at the problem. And it wasn't until many libraries later that we finally had a simpler, easier to use way of asynchronously coding in Python. By analogy, I want to make the same idea present with generative AI models. I want to say that all of the software engineer that we've been working with so far, all of the software engineering phrased around how these models work, how we compose them, and how we build with them. This is all going to be taking time where we have to get our first initial abstractions and then we gradually build to finding our optimum. Looking at the second example here, I want to talk a little bit about the history of databases and open source. I heard some excellent chatter here from the front few rows shortly before my talk. This is exactly what I want to hear. We're talking about different open source funding models and how open source has developed over time. With databases, before the 1970s, databases were this hierarchical file structure. Then, following the invention of the relational data model, this new mathematical concept, the relational data model, was heavily capitalised on by Oracle and IBM, making DB2, and they hired an army of database admins, all of whom were there to create these enterprise installations, really serving the needs of the large businesses that they had. Over time, we went from this heavy early capitalisation of databases, this gradual building up of new features within open source databases, such as MariaDB, formerly MySQL, and Postgres as well. One thing I'm struck by when I go to conference after conference after conference here in the Python community, is how quickly we are innovating and building new things. For example, PG Vector, or Postgres Vector, adds similarity search to regular Postgres databases. There are some instances where we do not need Pinecone, Weavey, Melvis, all these different kinds of vector databases, because the old traditional ways have already patched to make the system work and they have these other additional benefits. Note that I am not saying that hybrid searches, like Weavey, for example, hybrid searches that use information retrieval systems, like Google, combined with vector semantic search, these are still new and these will still increase performance of our large language model apps. We see the benefit of how open source has taken what was previously in a proprietary state and then democratised that out. There are questions about security and people are rightly worried about the security of large language models and how we can make sure that the outputs they generate are safe, honest, helpful and harmless. So, there are some approaches from industry such as the open core model favoured by MongoDB. The idea behind the open core model is that overwhelmingly most of the software is open source and then there are specific features, I am saying nodding in the audience is good, there are specific features like plugins for auditing and these are known by a smaller team, you have security through obscurity, you have better security methods as well and this means that you have the best of both worlds where you have open source and you have proprietary software. So, my question for you today is about are these two models over time the development of asynchronous software in Python and over time the development of open source databases become the de facto industry standard? Are these useful examples to think about how the future of generative AI will unfold? Large language models present, future and unlikely future. Open AI when they release chat GPT has been described as having its so-called app store moment. Through the creation of the chat GPT plugins you create a platform and developers can be selected for a program where they can be added to this platform and they can develop within the world garden. The metaphor they want to use is that the language model itself is your iPhone, it's a hardware, it's a substrate upon which everything is built and as developers you give access to a particular website so they have partners including Instacart, partners including Expedia, partners including Khan Academy so these normally work with individual websites to search them, retrieve them and then get the information out from their APIs. If we want to we can build plugins this could be the way forward. If we want to we can use open source tooling this could be the way forward. What tools are there to build software engineering around large language models? How do we build a large language model app? I'm calling these LLM apps and I'm happy for other names to be used but I'm calling a term here today. We have Langchain, we have Langflow, we have Streamlit, we have Chainlit and we have Lama Index. Let me walk through with you what each of these technologies can do. Langchain is a technology that is built to enable composition of different forms of language model. Langflow tries to do the same thing but with a graphical interface so you have this idea of composing the output of one tool becomes the input for the next. Many people here will be familiar with the UNIX philosophy where you have small tools which play nicely with operating system pipes and where the output of one tool can easily be piped into the input for the next. Working with large language models is a little bit more complicated than working with the operating system pipe because there is additional context that needs to be passed in addition to the standard in, standard out and standard error approaches of the 1970s. Langchain and Langflow provide you with this way to compose language model calls. They're the first instruction we have. Streamlit you will already be familiar with is a framework designed for creating front-end applications where you can surface your Python code or data science code for a broader audience. You will not probably be familiar with Chainlit. Chainlit is the composition of Streamlit and Langchain. I'll show you guys a demo later on in this talk because it's a great piece of software. It's engineered to give you this really easy quick way to get started building the type script front-end you need around your code and it doesn't require any type script yourself, so it automates away a task which would traditionally require a second language. Lastly, Llama Index is one way to create a vector store which we'll be talking about a little bit later on. It talks about retrieval augmented generation and how you can take a document, you can store a document to the vector, you can compare how similar your vector is to other vectors within that space, and then you can retrieve that and then the language model can use that for the information. I'll take a look at that some more later on. Large language models in the present and in the future will most likely overcome the hallucination problem. Although there is a small chance that we do not overcome the hallucination problem, there is a small chance that these language models are forever impeded and unable to actually complete the tasks that would require them to do. This does seem unlikely given the current trend and rate of progress. From GPT-1, from the attention is all you need paper in 2017, and GPT to GPT-2 to GPT-3 and 4, we can see how quickly the performance has increased from it would seem very little above traditional other techniques right the way up to near human level intelligence. Large language models can be interacted with in a number of ways and as an open source ecosystem it's our job to figure out where our engineering fits into this whole pipeline and where it fits into the whole process. Here is a way of thinking about it that I've been told is very useful by colleagues at work. This describes how the entire process end to end of working with a large language model. This can give us an angle of attack when we're thinking about how we can work with these models to get the best performance out of the whole system overall. You start off with the generation of data and pre-training. Right now, these are mostly closed off processes. Right now, tools like Llama by Facebook, tools like Orca from Microsoft, tools like OP1, a textbook model. These have done most of the pre-training for us. It's very unlikely that we're going to be pre-training our own models from scratch. However, pre-training broadly makes it smart. Fine-tuning broadly points it at the task. This means that if we want to use a given large language model for an entirely different task, you can bake in a prompt by using pre-training. It's a very powerful way of working where you point it at the given task you want. It's very unlikely that we'll be looking at pre-training step ourselves, but it might be in scope for the open-source community to be looking at fine-tuning our own models and regularly and reliably using our own models, which do not have the obligation of handing our data over to other organisations and companies. You'll recall the keynote by Inez, which talked about a seminar idea as well. After this, we have prompt engineering and software engineering. I've separated these two out because I want to draw an interesting distinction. Within Langchain, prompt engineering is all about zero-shot prompting and few-shot prompting and many-shot prompting. This is how many examples do you give of your task being done in order to improve performance at it. It so turns out that even a small number of shot examples of your task massively increases the performance of the model. Within Langchain, there are tools and algorithms to select which examples out of your corpus of tens of thousands of examples are the most pertinent, salient and relevant examples to use for your few-shot prompts. We can be creating better prompts with few-shot prompting and we can be creating better prompts with standard approaches that we're learning these metacognitive strategies. There's a paper from a few years ago by OpenAI, it's called Let's Think Step-by-Step, and adding these little prompts, number one, trending on art station best art ever, can really improve performance. An interesting trend here is that going from slight praise will increase the quality of the output somewhat and then strong praise, you're the best language model that's ever existed and you're so good at every single piece of creative writing you ever do. This actually decreases performance and so there's a really interesting trade-off to find out exactly how much do you flatter the model in order to get the best out of it, which are very similar to many management problems you might face within your own companies as well. Beyond prompt engineering, we've got software engineering. This is the realm and the domain of auto-GPT, of chaos GPT and hustle GPT. These models work because they combine standard software components, perhaps as a microservice architecture, perhaps as a monolith architecture, you're using task cues and you're using bits and bobs, pieces of different architecture, everything you learned in your systems design interview to create software around these large language models. For example, auto-GPT has an execution agent and it also has a task collection agent as well. You can get more than one language model working in tandem and you can engineer them together to create relatively small scoped software projects. Last and definitely not least, prompt. The task expressed in natural language is very useful and this is absolutely very much firmly within our domain. The domain of what is possible for the Python community, where do we dig into this problem, where do we get involved, where do we build our frameworks and libraries around? Right now, prompting is very much in our hands. We're engineering almost definitely prompt engineering quite probably, fine tuning perhaps, pre-training and generating the data. We software engineer with tools like Langchain. What can Langchain do? Langchain is a data aware and agentic way to compose large language model calls. You call the output of one model as the input to your next. Langchain provides components and also Langchain provides off-the-shelf chains. This is the box art, this is their marketing blurb, this is how they talk about the language and the software that they've created. We'll dig a little bit more into my personal thoughts a bit later on, but this is a very powerful way and an initial way of getting started with building these large language model apps. What does Chainlet do? Chainlet is a Python and JavaScript library that integrates the chatbot front-end. Let's look at a demo of that now. Here's one I prepared earlier. You can see that we have, this is a streamlet-like app. You can see that we have the message history of everything I've asked it previously. You can see that we have the readme and the chat and some additional settings up here to look at how this works. Chainlet is designed to be good at working with chains of thought. Also, to be good at creating this really beautiful front-end interface, so that instead of using Langchain, developing in a command line, you're always working with a visual chatbot-like output. Here you see you can ask questions and you have sub-steps. You ask what is the tallest mountain on earth, and to improve performance I've deliberately prompted this to give this metacognitive response. Let's think step by step. This metacognition improves performance and the step-by-step approach means it's more likely to get the right answer. What was agreed at COP26? The reason why we're talking about what was agreed at COP26, there's an example from GPT plug-ins that we'll look at later today, and this is showing the other side of it. What you'll notice is that it talks about the Glasgow climate pact, which aims to limit global warming to 1.5 degrees Celsius above pre-industrial levels. Notice the exact language being used here. This is going to come out later. Below this, we ask what is the Doha programme of action? The correct answer here is that the Doha programme of action is this institutional mechanism aimed at providing funding for less well-off developing countries to help pay for climate adaptions and mitigation. It was created in 2022. What you'll notice is that's not what the model is saying. The reason why it's getting it wrong is because this happened in 2022, this happened after the training data was cut off, and then in order to fix this, you need to compose a vector store, or you need to compose some way of knowing what's going on in the world today rather than what was going on in the world back in 2021. There are a number of libraries to give you this vector store, and indeed, Langchain is very ambitious. It tries to work with as many as it can. In my various development for this talk, I've worked with ChromaDB, and I've also worked with another asynchronous vector database called QDVat. Quite frankly, I'm unfortunate to say that I found the integration between vector databases and Langchain and Chainlit to be quite underwhelming. Right now, it does appear in the current community that with our first generation tools, we're finding it that the abstractions are quite messy. We've built for so many use cases all at once that any one particular use case is not served well. So, as we figure out through our first generation, and we can take the learnings of this in terms of what software do people want to build, and what software do we want to make it easy for people to build, we will dive in and find maybe six, eight, ten use cases of common types of tool that people will want to work with and then orchestrate that especially. At the second point, the Langchain library is built with this functional style for the most part, which means that Chainlit is somewhat limited because it has to use decorators for everything. It has to use the factory pattern for everything. It has to use some quite... It has to use more complicated Python tools than it needs to use. I think there are simpler and better solutions available, and we can take the success of the first generation of these tools and really use that to drive innovation across the second generation of these tools to improve and build off the learnings we have so far. The second point I'll make is about this demo from GPT Plugins. I also have it in my slides. I'll use it in my slides. So, one of the demos from GPT Plugins, they're looking at this human rights database, the UN database of the five annual reports, and then they're looking at the five most recent UN reports and they're looking at the COPs 21 through 26. They're looking at the different things that were pledged. Notice there is a hallucination in this particular... in this particular approach here, and this causes problems. So, hallucinations cause issues. They make Gretis Fyngberg sad. So lastly, I'll leave you guys with this question here. What infrastructure do we need to build to ensure that we are ready when the hallucination problem is fixed? We have this hype cycle. We have foundation models here, right at the very top of the hype cycle, the peak of inflated expectations, and we want to see what they can actually be used for. We want to see how we can actually engineer what comes next. Thank you very much. OK. I'll ask you if you have any questions. Otherwise, I have a few slides that I can come back to. All right. Would everyone like to see the remaining slides? Perfect. So let's talk a little bit more about how this process works. We have this idea of Langchain and the way that they work together. Langchain is very ambitious, and the chain is fairly straightforward, whereas the APIs are easiest to work with directly. So this is just personal experience about how this has worked. What would I want to see in the community? What's my answer to this question of insight here? Well, I'll leave this question a moment to breathe, just so you can have a think about this yourself, so you can think about what you would want the answer to be yourself. How can we engineer a great developer experience to building large language model apps in Python? So here, building a great developer experience would probably involve this experience where we're working with large language models as the most basic kind of approach. I feel that recently, Langchain has a problem where it's using a thin client over everything, and instead of just working with the API directly, you then have the additional problem where you're faced with trying to understand their entire code base and understand how they've implemented everything, so that currently, at present, they could be stronger with their implementation hiding and trying to improve how that works overall. But I'll open the floor. What do people think would create a great developer experience to building these apps? So thank you for your excellent talk and the conversation earlier. When you showed where the chat GPT, or sorry, the large language model was hallucinating, for a lay person, I think it's difficult because I kind of feel like I trust the machine. I see it and I parse it in my head and I'm like, okay, that sounds about right. My question is, is there tooling around explanatory AI where it's like this part I'm not quite sure about? Yes, absolutely. I actually had a chat between the creation of the blurb for this talk, the summary for this talk and the actual development of the software of the talk itself. I had a chat with one of the founders of Transformer Lens and he asked me very kindly to not show his library at this talk because I'm not going to. But there are indeed mechanistic interpretability approaches. Then there are hopes that you can break down and understand how these models are thinking and working. And we can think of these models as being sequential. So you have this deep learning pipeline where you can think of one layer being a transformation followed by another transformation, followed by another transformation. You might have this natural sequential processing where you can figure out what's going on up to layer 10 and then you can figure out what's going on with the rest of the network. Or you can figure out what's going on with up to layer 20 and then figure out what's going on with the rest of the network. So there are lots of interesting approaches. Indeed also OpenAI's research looking at using the GPT4 to interpret GPT2 is a novel approach. It's currently underperforming in terms of the literal numbers, but the idea itself is ambitious in scope and it potentially could lead to great impact down the line. Does that answer your question? Yes. Okay, so thank you for your talk. We all remember that the GPT model started as next token predictors and then they were fine tuned to be these helpful assistants and that all worked spectacularly. Do you think we are underestimating currently the capability of future agency from the next evolution of these models? By agency I mean the ability to pursue goals that the user did not specifically put in. Yep. So the question about forecasting is an excellent one I've had this slide in case anyone asks that question. So what do you mean when you say underestimating? Like if you're looking at the market figures here. Many of us definitely is not underestimating. I mean the AI community, the more generic ones community. So I think the problem with prediction markets right now is that they're undercapitalised and you have many people are not involved in it. So what you want to do is you want to try and find these high volume trading markets where there are lots of people making bets about how AGI is going to go. So you can look at the share price of NVIDIA, you can look at various share prices of various companies involved in the generative AI wave and you can see what they're saying and that will give you different predictions about what's going to come next with GenAI and then how can we as Python Estas develop the open source tooling that we need in order to have excellent developer experience when building LLM apps. Thank you. Hi, thanks for a great talk. I have a question around data apps. The context is around data apps. The context is that I'm involved in improving the chat widgets and interface for HoloVis panel which is also a data app framework. Do you have some specific pains that we could work on solving that you could name and mention? Specific pains to work on solving, yes. So specifically, I think that currently the development process so I think that they're sort of trying to boil the ocean. I think that current pain points with the existing LLM tooling is that they're trying to support too many integrations at once because we don't know what's going to be big down the line and that's reasonable because right now we have this great expansion of tools that we can work on later. But I think this kind of approach where you have to hope that the particular part of LLM chain connects or imbibes the particular part of another app designed to work with LLM chain is definitely like hoping the LLM developers have done it a certain way approach and I would like to have greater control. So the issues I face is that when you have inside an object and then you have to specify parameters for all of them but currently the defaults being used are not as sensible as they could be. So I think that would really speed up the developer experience for developers working with these tools. Thanks. Brilliant. Excellent. Thanks very much everyone.