How I use LLMs

00:00:00.000 | Hi everyone. So in this video, I would like to continue our general audience series on large

00:00:05.840 | language models like Chatsheepd. Now, in the previous video, "Deep Dive into LLMs" that you

00:00:10.480 | can find on my YouTube, we went into a lot of the under the hood fundamentals of how these models

00:00:14.560 | are trained and how you should think about their cognition or psychology. Now, in this video,

00:00:20.080 | I want to go into more practical applications of these tools. I want to show you lots of examples,

00:00:25.600 | I want to take you through all the different settings that are available, and I want to show

00:00:29.040 | you how I use these tools and how you can also use them in your own life and work. So let's dive in.

00:00:35.360 | Okay, so first of all, the web page that I have pulled up here is chatsheepd.com. Now,

00:00:40.080 | as you might know, Chatsheepd was developed by OpenAI and deployed in 2022. So this was the

00:00:46.000 | first time that people could actually just kind of like talk to a large language model

00:00:49.760 | through a text interface, and this went viral and all over the place on the internet,

00:00:54.080 | and this was huge. Now, since then though, the ecosystem has grown a lot. So I'm going to be

00:00:59.280 | showing you a lot of examples of Chatsheepd specifically, but now in 2025, there's many

00:01:05.840 | other apps that are kind of like Chatsheepd-like, and this is now a much bigger and richer ecosystem.

00:01:10.480 | So in particular, I think Chatsheepd by OpenAI is this original gangster incumbent.

00:01:16.080 | It's most popular and most feature-rich also, because it's been around the longest.

00:01:20.800 | But there are many other kinds of clones available, I would say. I don't think it's too unfair to say,

00:01:25.600 | but in some cases, there are kind of like unique experiences that are not found in Chatsheepd,

00:01:29.840 | and we're going to see examples of those. So for example, Big Tech has followed with a lot of kind

00:01:35.920 | of Chatsheepd-like experiences. So for example, Gemini, Meta.ai, and Copilot from Google, Meta,

00:01:40.960 | and Microsoft respectively. And there's also a number of startups. So for example, Anthropic

00:01:46.000 | has Claude, which is kind of like a Chatsheepd equivalent. XAI, which is Elon's company,

00:01:50.720 | has Grok. And there's many others. So all of these here are from the United States

00:01:55.920 | companies, basically. DeepSeek is a Chinese company, and Le Chat is a French company, Mistral.

00:02:03.120 | Now, where can you find these and how can you keep track of them? Well, number one,

00:02:07.200 | on the internet somewhere. But there are some leaderboards, and in the previous video,

00:02:10.480 | I've shown you Chatbot Arena is one of them. So here you can come to some ranking of different

00:02:15.760 | models, and you can see sort of their strength or ELO score. And so this is one place where you

00:02:21.040 | can keep track of them. I would say another place maybe is this SEAL leaderboard from Scale. And so

00:02:28.080 | here you can also see different kinds of evals, and different kinds of models, and how well they

00:02:32.400 | rank. And you can also come here to see which models are currently performing the best on a

00:02:37.600 | wide variety of tasks. So understand that the ecosystem is fairly rich, but for now I'm going

00:02:43.760 | to start with OpenAI because it is the incumbent and is most feature-rich, but I'm going to show

00:02:48.640 | you others over time as well. So let's start with ChatGPT. What is this text box and what do we put

00:02:54.240 | in here? Okay, so the most basic form of interaction with a language model is that we give

00:02:58.400 | a text and then we get some text back in response. So as an example, we can ask to get a haiku about

00:03:04.480 | what it's like to be a large language model. So this is a good kind of example task for a language

00:03:09.760 | model because these models are really good at writing. So writing haikus, or poems, or cover

00:03:15.920 | letters, or resumes, or email replies, they're just good at writing. So when we ask for something like

00:03:22.240 | this, what happens looks as follows. The model basically responds, "Words flow like a stream,

00:03:28.320 | endless echoes nevermind, ghost of thought unseen." Okay, it's pretty dramatic. But what we're seeing

00:03:34.960 | here in ChatGPT is something that looks a bit like a conversation that you would have with a friend.

00:03:39.120 | These are kind of like chat bubbles. Now we saw in the previous video is that what's going on

00:03:44.240 | under the hood here is that this is what we call a user query, this piece of text. And this piece

00:03:50.480 | of text and also the response from the model, this piece of text is chopped up into little text

00:03:56.160 | chunks that we call tokens. So this sequence of text is under the hood, a token sequence,

00:04:02.960 | one-dimensional token sequence. Now the way we can see those tokens is we can use an app like,

00:04:07.040 | for example, TickTokenizer. So making sure that GPT-40 is selected, I can paste my text here.

00:04:12.080 | And this is actually what the model sees under the hood. My piece of text to the model looks

00:04:17.920 | like a sequence of exactly 15 tokens. And these are the little text chunks that the model sees.

00:04:23.360 | Now there's a vocabulary here of 200,000 roughly of possible tokens. And then these are the token

00:04:32.320 | IDs corresponding to all these little text chunks that are part of my query. And you can play

00:04:36.880 | with this and update it. And you can see that, for example, this is case sensitive. You would

00:04:40.000 | get different tokens. And you can kind of edit it and see live how the token sequence changes.

00:04:44.160 | So our query was 15 tokens. And then the model response is right here. And it responded back to

00:04:51.520 | us with a sequence of exactly 19 tokens. So that haiku is this sequence of 19 tokens.

00:04:58.000 | Now, so we said 15 tokens and it said 19 tokens back. Now, because this is a conversation and we

00:05:06.480 | want to actually maintain a lot of the metadata that actually makes up a conversation object,

00:05:10.640 | this is not all that's going on under the hood. And we saw in the previous video a little bit

00:05:14.800 | about the conversation format. So it gets a little bit more complicated in that we have to take our

00:05:20.880 | user query. And we have to actually use this chat format. So let me delete the system message. I

00:05:26.320 | don't think it's very important for the purposes of understanding what's going on. Let me paste my

00:05:30.720 | message as the user. And then let me paste the model response as an assistant. And then let me

00:05:37.200 | crop it here properly. The tool doesn't do that properly. So here we have it as it actually

00:05:44.400 | happens under the hood. There are all these special tokens that basically begin a message

00:05:50.320 | from the user. And then the user says, and this is the content of what we said. And then the user

00:05:55.840 | ends. And then the assistant begins and says this, et cetera. Now, the precise details of the

00:06:02.080 | conversation format are not important. What I want to get across here is that what looks to you and I

00:06:07.280 | as little chat bubbles going back and forth under the hood, we are collaborating with the model

00:06:12.560 | and we're both writing into a token stream. And these two bubbles back and forth were in a

00:06:20.640 | sequence of exactly 42 tokens under the hood. I contributed some of the first tokens and then the

00:06:26.000 | model continued the sequence of tokens with its response. And we could alternate and continue

00:06:32.000 | adding tokens here. And together we're building out a token window, a one-dimensional sequence

00:06:38.320 | of tokens. Okay, so let's come back to chatGPT now. What we are seeing here is kind of like

00:06:43.760 | little bubbles going back and forth between us and the model. Under the hood, we are building

00:06:47.920 | out a one-dimensional token sequence. When I click new chat here, that wipes the token window.

00:06:54.720 | That resets the tokens to basically zero again and restarts the conversation from scratch.

00:07:00.080 | Now, the cartoon diagram that I have in my mind when I'm speaking to a model looks something like

00:07:04.720 | this. When we click new chat, we begin a token sequence. So this is a one-dimensional sequence

00:07:11.920 | of tokens. The user, we can write tokens into this stream. And then when we hit enter, we transfer

00:07:19.200 | control over to the language model. And the language model responds with its own token streams.

00:07:24.880 | And then the language model has a special token that basically says something along the lines of

00:07:29.600 | "I'm done." So when it emits that token, the chatGPT application transfers control back to us

00:07:35.600 | and we can take turns. Together, we are building out the token stream, which we also call the

00:07:41.680 | context window. So the context window is kind of like this working memory of tokens and anything

00:07:48.080 | that is inside this context window is kind of like in the working memory of this conversation

00:07:52.640 | and is very directly accessible by the model. Now, what is this entity here that we are talking

00:07:59.200 | to and how should we think about it? Well, this language model here, we saw that the way it is

00:08:04.480 | trained in the previous video, we saw there are two major stages, the pre-training stage and the

00:08:09.680 | post-training stage. The pre-training stage is kind of like taking all of internet, chopping

00:08:16.160 | it up into tokens, and then compressing it into a single kind of like zip file. But the zip file is

00:08:22.640 | not exact. The zip file is lossy and probabilistic zip file because we can't possibly represent all

00:08:28.400 | of internet than just one sort of like, say, terabyte of zip file because there's just way

00:08:35.920 | too much information. So we just kind of get the gestalt or the vibes inside this zip file.

00:08:42.320 | Now, what's actually inside the zip file are the parameters of a neural network.

00:08:48.400 | And so, for example, a one terabyte zip file would correspond to roughly, say, one trillion

00:08:54.480 | parameters inside this neural network. And what this neural network is trying to do is it's trying

00:09:00.400 | to basically take tokens, and it's trying to predict the next token in a sequence. But it's

00:09:05.760 | doing that on internet documents. So it's kind of like this internet document generator, right?

00:09:10.560 | And in the process of predicting the next token in a sequence on internet,

00:09:15.600 | the neural network gains a huge amount of knowledge about the world. And this knowledge

00:09:21.600 | is all represented and stuffed and compressed inside the one trillion parameters, roughly,

00:09:26.960 | of this language model. Now, this pre-training stage also we saw is fairly costly. So this can

00:09:32.240 | be many tens of millions of dollars, say like three months of training and so on. So this is

00:09:37.840 | a costly long phase. For that reason, this phase is not done that often. So for example, GPT-4.0,

00:09:45.520 | this model was pre-trained probably many months ago, maybe like even a year ago by now. And so

00:09:52.080 | that's why these models are a little bit out of date. They have what's called a knowledge cutoff.

00:09:56.960 | Because that knowledge cutoff corresponds to when the model was pre-trained.

00:10:00.960 | And its knowledge only goes up to that point. Now, some knowledge can come into the model

00:10:09.440 | through the post-training phase, which we'll talk about in a second. But roughly speaking,

00:10:13.280 | you should think of these models as kind of like a little bit out of date, because pre-training is

00:10:17.680 | way too expensive and happens infrequently. So any kind of recent information, like if you wanted to

00:10:24.080 | talk to your model about something that happened last week or so on, we're going to need other

00:10:27.600 | ways of providing that information to the model, because it's not stored in the knowledge of the

00:10:31.840 | model. So we're going to have various tool use to give that information to the model.

00:10:36.400 | Now, after pre-training, there's the second stage called post-training. And the post-training stage

00:10:42.240 | is really attaching a smiley face to this zip file. Because we don't want to generate internet

00:10:47.360 | documents, we want this thing to take on the persona of an assistant that responds to user

00:10:53.600 | queries. And that's done in the process of post-training, where we swap out the dataset

00:10:58.640 | for a dataset of conversations that are built out by humans. So this is basically where the model

00:11:04.000 | takes on this persona, so that we can ask questions and it responds with answers.

00:11:09.040 | So it takes on the style of an assistant, that's post-training, but it has the knowledge

00:11:16.480 | of all of internet, and that's by pre-training. So these two are combined in this artifact.

00:11:23.200 | Now, the important thing to understand here, I think, for this section, is that what you are

00:11:28.800 | talking to is a fully self-contained entity by default. This language model, think of it as a

00:11:34.640 | one terabyte file on a disk. Secretly, that represents one trillion parameters and their

00:11:40.240 | precise settings inside the neural network that's trying to give you the next token in a sequence.

00:11:45.360 | But this is the fully self-contained entity. There's no calculator, there's no computer

00:11:50.080 | and Python interpreter, there's no worldwide web browsing, there's none of that. There's

00:11:54.320 | no tool use yet in what we've talked about so far. You're talking to a zip file. If you

00:11:59.120 | stream tokens to it, it will respond with tokens back. And the zip file has the knowledge from

00:12:05.040 | pre-training and it has the style and form from post-training. And so that's roughly how you can

00:12:12.640 | think about this entity. Okay, so if I had to summarize what we talked about so far, I would

00:12:17.280 | probably do it in the form of an introduction of ChatGPT in a way that I think you should think

00:12:21.120 | about it. So the introduction would be, "Hi, I'm ChatGPT. I'm a one terabyte zip file. My knowledge

00:12:27.760 | comes from the internet, which I read in its entirety about six months ago and I only remember

00:12:34.000 | vaguely. Okay? And my winning personality was programmed, by example, by human labelers at

00:12:39.920 | OpenAI." So the personality is programmed in post-training and the knowledge comes from

00:12:46.640 | compressing the internet during pre-training. And this knowledge is a little bit out of date

00:12:51.680 | and it's probabilistic and slightly vague. Some of the things that probably are mentioned very

00:12:56.880 | frequently on the internet, I will have a lot better recollection of than some of the things

00:13:01.120 | that are discussed very rarely, very similar to what you might expect with a human. So let's now

00:13:06.800 | talk about some of the repercussions of this entity and how we can talk to it and what kinds

00:13:11.040 | of things we can expect from it. Now I'd like to use real examples when we actually go through this.

00:13:15.280 | So for example, this morning I asked ChatGPT the following, "How much caffeine is in one shot of

00:13:19.520 | Americana?" And I was curious because I was comparing it to matcha. Now ChatGPT will tell

00:13:24.800 | me that this is roughly 63 milligrams of caffeine or so. Now the reason I'm asking ChatGPT this

00:13:29.520 | question that I think this is okay is, number one, I'm not asking about any knowledge that is very

00:13:35.280 | recent. So I do expect that the model has sort of read about how much caffeine there is in one shot.

00:13:40.240 | I don't think this information has changed too much. And number two, I think this information

00:13:44.720 | is extremely frequent on the internet. This kind of a question and this kind of information has

00:13:48.880 | occurred all over the place on the internet. And because there were so many mentions of it,

00:13:53.200 | I expect the model to have good memory of it and its knowledge. So there's no tool use and

00:13:58.160 | the model, the zip file, responded that there's roughly 63 milligrams. Now I'm not guaranteed

00:14:03.920 | that this is the correct answer. This is just its vague recollection of the internet. But I

00:14:10.000 | can go to primary sources and maybe I can look up, okay, caffeine and Americano and I could verify

00:14:16.400 | that, yeah, it looks to be about 63 is roughly right. And you can look at primary sources to

00:14:20.640 | decide if this is true or not. So I'm not strictly speaking guaranteed that this is true, but I think

00:14:25.200 | probably this is the kind of thing that ChatGPT would know. Here's an example of a conversation

00:14:29.840 | I had two days ago, actually. And there's another example of a knowledge-based conversation and

00:14:34.880 | things that I'm comfortable asking of ChatGPT with some caveats. So I'm a bit sick, I have runny nose

00:14:39.840 | and I want to get meds that help with that. So it told me a bunch of stuff. And I want my nose to

00:14:47.680 | not be runny. So I gave it a clarification based on what it said. And then it kind of gave me some

00:14:52.240 | of the things that might be helpful with that. And then I looked at some of the meds that I have

00:14:56.320 | at home. And I said, does DayQuil or NightQuil work? And it went off and it kind of like went

00:15:01.360 | over the ingredients of DayQuil and NightQuil and whether or not they help mitigate runny nose.

00:15:07.040 | Now, when these ingredients are coming here, again, remember, we are talking to a zip file

00:15:12.320 | that has a recollection of the internet. I'm not guaranteed that these ingredients are correct.

00:15:16.960 | And in fact, I actually took out the box and I looked at the ingredients and I made sure

00:15:20.800 | that NightQuil ingredients are exactly these ingredients. And I'm doing that because I don't

00:15:26.160 | always fully trust what's coming out here, right? This is just a probabilistic statistical

00:15:30.480 | recollection of the internet. But that said, conversations of DayQuil and NightQuil, these

00:15:35.600 | are very common meds. Probably there's tons of information about a lot of this on the internet.

00:15:40.880 | And this is the kind of things that the model have pretty good recollection of. So actually,

00:15:45.600 | these were all correct. And then I said, okay, well, I have NightQuil. How fast would it act

00:15:51.200 | roughly? And it kind of tells me. And then is acetaminophen basically a Tylenol? And it says,

00:15:56.800 | yes. So this is a good example of how ChachAPT was useful to me. It is a knowledge-based query.

00:16:02.000 | This knowledge sort of isn't recent knowledge. This is all coming from the knowledge of the

00:16:06.880 | model. I think this is common information. This is not a high-stakes situation. I'm checking

00:16:11.920 | ChachAPT a little bit. But also, this is not a high-stakes situation, so no big deal. So I popped

00:16:16.800 | a NightQuil and indeed it helped. But that's roughly how I'm thinking about what's coming

00:16:21.680 | back here. Okay, so at this point, I want to make two notes. The first note I want to make is that

00:16:26.960 | naturally as you interact with these models, you'll see that your conversations are growing

00:16:31.040 | longer, right? Anytime you are switching topic, I encourage you to always start a new chat.

00:16:37.200 | When you start a new chat, as we talked about, you are wiping the context window of tokens

00:16:42.640 | and resetting it back to zero. If it is the case that those tokens are not anymore useful to your

00:16:47.600 | next query, I encourage you to do this because these tokens in this window are expensive.

00:16:52.400 | And they're expensive in kind of like two ways. Number one, if you have lots of tokens here,

00:16:58.320 | then the model can actually find it a little bit distracting. So if this was a lot of tokens,

00:17:04.000 | this is kind of like the working memory of the model. The model might be distracted by all the

00:17:09.440 | tokens in the past when it is trying to sample tokens much later on. So it could be distracting

00:17:15.360 | and it could actually decrease the accuracy of the model and of its performance. And number two,

00:17:20.080 | the more tokens are in the window, the more expensive it is by a little bit, not by too much,

00:17:25.200 | but by a little bit to sample the next token in the sequence. So your model is actually slightly

00:17:30.000 | slowing down. It's becoming more expensive to calculate the next token and the more tokens

00:17:35.040 | there are here. And so think of the tokens in the context window as a precious resource.

00:17:41.280 | Think of that as the working memory of the model and don't overload it with irrelevant information

00:17:47.840 | and keep it as short as you can. And you can expect that to work faster and slightly better.

00:17:53.360 | Of course, if the information actually is related to your task, you may want to keep it in there.

00:17:57.360 | But I encourage you to, as often as you can, basically start a new chat whenever you are

00:18:02.400 | switching topic. The second thing is that I always encourage you to keep in mind what model you are

00:18:07.440 | actually using. So here on the top left, we can drop down and we can see that we are currently

00:18:11.520 | using GPT 4.0. Now, there are many different models of many different flavors, and there are

00:18:17.520 | too many actually, but we'll go through some of these over time. So we are using GPT 4.0 right

00:18:22.240 | now. And in everything that I've shown you, this is GPT 4.0. Now, when I open a new incognito

00:18:27.200 | window, so if I go to chatgpt.com and I'm not logged in, the model that I'm talking to here,

00:18:33.440 | so if I just say hello, the model that I'm talking to here might not be GPT 4.0. It might be a

00:18:38.560 | smaller version. Now, unfortunately, OpenAI does not tell me when I'm not logged in what model I'm

00:18:43.600 | using, which is kind of unfortunate. But it's possible that you are using a smaller, kind of

00:18:48.240 | dumber model. So if we go to the chatgpt pricing page here, we see that they have three basic

00:18:54.640 | tiers for individuals, the free, plus, and pro. And in the free tier, you have access to what's

00:19:01.280 | called GPT 4.0 mini. And this is a smaller version of GPT 4.0. It is a smaller model with a smaller

00:19:08.000 | number of parameters. It's not going to be as creative, like its writing might not be as good.

00:19:12.240 | Its knowledge is not going to be as good. It's going to probably hallucinate a bit more, etc.

00:19:16.640 | But it is kind of like the free offering, the free tier. They do say that you have limited access to

00:19:21.760 | 4.0 and 3.0 mini, but I'm not actually 100% sure. Like, it didn't tell us which model we were using,

00:19:26.880 | so we just fundamentally don't know. Now, when you pay for $20 per month, even though it doesn't say

00:19:32.640 | this, I think basically, like, they're screwing up on how they're describing this. But if you go to

00:19:37.600 | fine print, limit supply, we can see that the plus users get 80 messages every three hours for GPT 4.0.

00:19:46.240 | So that's the flagship biggest model that's currently available as of today. That's available

00:19:52.320 | and that's what we want to be using. So if you pay $20 per month, you have that with some limits.

00:19:56.800 | And then if you pay for $200 per month, you get the pro, and there's a bunch of additional goodies

00:20:01.680 | as well as unlimited GPT 4.0. And we're going to go into some of this because I do pay for

00:20:06.080 | pro subscription. Now, the whole takeaway I want you to get from this is be mindful of the models

00:20:12.560 | that you're using. Typically, with these companies, the bigger models are more expensive to

00:20:16.480 | calculate. And so therefore, the companies charge more for the bigger models. And so make those

00:20:23.760 | trade-offs for yourself, depending on your usage of LLMs. Have a look at if you can get away with

00:20:29.280 | the cheaper offerings. And if the intelligence is not good enough for you and you're using this

00:20:32.560 | professionally, you may really want to consider paying for the top tier models that are available

00:20:36.320 | from these companies. In my case, in my professional work, I do a lot of coding and a lot of things

00:20:40.560 | like that. And this is still very cheap for me. So I pay this very gladly because I get access to

00:20:46.160 | some really powerful models that I'll show you in a bit. So yeah, keep track of what model you're

00:20:51.360 | using and make those decisions for yourself. I also want to show you that all the other LLM

00:20:56.480 | providers will all have different pricing tiers with different models at different tiers that

00:21:01.760 | you can pay for. So for example, if we go to Claude from Anthropic, you'll see that I am

00:21:06.240 | paying for the professional plan and that gives me access to Claude 3.5 Sonnet. And if you are

00:21:11.760 | not paying for a pro plan, then probably you only have access to maybe Haiku or something like that.

00:21:15.760 | And so use the most powerful model that kind of like works for you. Here's an example of me using

00:21:22.480 | Claude a while back. I was asking for just travel advice. So I was asking for a cool city to go to,

00:21:28.800 | and Claude told me that Zermatt in Switzerland is really cool. So I ended up going there for

00:21:33.360 | a New Year's break following Claude's advice. But this is just an example of another thing that I

00:21:37.920 | find these models pretty useful for is travel advice and ideation and getting pointers that

00:21:42.960 | you can research further. Here we also have an example of Gemini.google.com. So this is from

00:21:49.040 | Google. I got Gemini's opinion on the matter and I asked it for a cool city to go to, and it also

00:21:54.880 | recommended Zermatt. So that was nice. So I like to go between different models and asking them

00:22:00.080 | similar questions and seeing what they think about. And for Gemini also on the top left,

00:22:04.640 | we also have a model selector. So you can pay for the more advanced tiers and use those models.

00:22:10.240 | Same thing goes for Grok, just released. We don't want to be asking Grok 2 questions because we know

00:22:16.160 | that Grok 3 is the most advanced model. So I want to make sure that I pay enough and such that I

00:22:22.400 | have Grok 3 access. So for all these different providers, find the one that works best for you,

00:22:28.240 | experiment with different providers, experiment with different pricing tiers for the problems

00:22:31.920 | that you are working on. And that's kind of, and often I end up personally just paying for a lot

00:22:37.120 | of them and then asking all of them the same question. And I kind of refer to all these

00:22:42.480 | models as my LLM council. So they're kind of like the council of language models. If I'm trying to

00:22:47.840 | figure out where to go on a vacation, I will ask all of them. And so you can also do that for

00:22:52.320 | yourself if that works for you. Okay, the next topic I want to now turn to is that of thinking

00:22:57.680 | models, quote unquote. So we saw in the previous video that there are multiple stages of training.

00:23:02.320 | Pre-training goes to supervised fine-tuning, goes to reinforcement learning. And reinforcement

00:23:08.080 | learning is where the model gets to practice on a large collection of problems that resemble

00:23:13.760 | the practice problems in the textbook. And it gets to practice on a lot of math and code problems.

00:23:19.040 | And in the process of reinforcement learning, the model discovers thinking strategies that

00:23:26.000 | lead to good outcomes. And these thinking strategies, when you look at them,

00:23:30.000 | they very much resemble kind of the inner monologue you have when you go through problem solving.

00:23:34.400 | So the model will try out different ideas, it will backtrack, it will revisit assumptions,

00:23:40.160 | and it will do things like that. Now, a lot of these strategies are very difficult to hard code

00:23:44.640 | as a human labeler, because it's not clear what the thinking process should be. It's only in the

00:23:48.880 | reinforcement learning that the model can try out lots of stuff. And it can find the thinking process

00:23:53.520 | that works for it with its knowledge and its capabilities. So this is the third stage of

00:23:59.760 | training these models. This stage is relatively recent, so only a year or two ago. And all of the

00:24:06.400 | different LLM labs have been experimenting with these models over the last year. And this is kind

00:24:10.560 | of like seen as a large breakthrough recently. And here we looked at the paper from DeepSeek

00:24:17.040 | that was the first to basically talk about it publicly. And they had a nice paper about

00:24:22.160 | incentivizing reasoning capabilities in LLMs via reinforcement learning. So that's the paper that

00:24:26.720 | we looked at in the previous video. So we now have to adjust our cartoon a little bit, because

00:24:30.960 | basically what it looks like is our emoji now has this optional thinking bubble. And when you are

00:24:38.640 | using a thinking model, which will do additional thinking, you are using the model that has been

00:24:43.840 | additionally tuned with reinforcement learning. And qualitatively, what does this look like?

00:24:49.360 | Well, qualitatively, the model will do a lot more thinking. And what you can expect is that you will

00:24:54.160 | get higher accuracies, especially on problems that are, for example, math, code, and things

00:24:58.880 | that require a lot of thinking. Things that are very simple might not actually benefit from this,

00:25:04.400 | but things that are actually deep and hard might benefit a lot. And so, but basically what you're

00:25:11.440 | paying for it is that the models will do thinking, and that can sometimes take multiple minutes,

00:25:16.000 | because the models will emit tons and tons of tokens over a period of many minutes,

00:25:20.000 | and you have to wait, because the model is thinking just like a human would think.

00:25:23.520 | But in situations where you have very difficult problems, this might translate to higher accuracy.

00:25:29.280 | So let's take a look at some examples. So here's a concrete example when I was stuck on a programming

00:25:34.160 | problem recently. So something called the gradient check fails, and I'm not sure why,

00:25:38.960 | and I copy pasted the model, my code. So the details of the code are not important,

00:25:44.560 | but this is basically an optimization of a multi-layer perceptron, and details are not

00:25:50.160 | important. It's a bunch of code that I wrote, and there was a bug because my gradient check

00:25:53.920 | didn't work, and I was just asking for advice. And GPT-4.0, which is the flagship, most powerful

00:25:59.200 | model for open AI, but without thinking, just kind of like went into a bunch of things that

00:26:05.840 | it thought were issues or that I should double check, but actually didn't really solve the

00:26:09.200 | problem. Like all the things that it gave me here are not the core issue of the problem.

00:26:15.040 | So the model didn't really solve the issue, and it tells me about how to debug it and so on.

00:26:20.880 | But then what I did was, here in the dropdown, I turned to one of the thinking models. Now,

00:26:26.960 | for open AI, all of these models that start with O are thinking models. O1, O3 mini, O3 mini high,

00:26:35.280 | and O1 pro, pro mode, are all thinking models. And they're not very good at naming their models,

00:26:41.520 | but that is the case. And so here they will say something like, "Uses advanced reasoning," or

00:26:47.840 | "Good at coding logics," and stuff like that. But these are basically all tuned with reinforcement

00:26:53.120 | learning. And because I am paying for $200 per month, I have access to O1 pro mode,

00:26:59.840 | which is best at reasoning. But you might want to try some of the other ones depending on your

00:27:06.480 | pricing tier. And when I gave the same model, the same prompt to O1 pro, which is the best

00:27:13.040 | at reasoning model, and you have to pay $200 per month for this one,

00:27:17.440 | then the exact same prompt, it went off and it thought for one minute. And it went through a

00:27:23.520 | sequence of thoughts, and open AI doesn't fully show you the exact thoughts. They just kind of

00:27:28.560 | give you a little summaries of the thoughts. But it thought about the code for a while,

00:27:33.440 | and then it actually came back with the correct solution. It noticed that the parameters are

00:27:37.920 | mismatched in how I pack and unpack them, and et cetera. So this actually solved my problem.

00:27:41.920 | And I tried out giving the exact same prompt to a bunch of other LLMs. So for example, Claude,

00:27:48.240 | I gave Claude the same problem, and it actually noticed the correct issue and solved it. And it

00:27:55.520 | did that even with Sonnet, which is not a thinking model. So Claude 3.5 Sonnet, to my knowledge,

00:28:02.640 | is not a thinking model. And to my knowledge, Anthropic, as of today, doesn't have a thinking

00:28:07.680 | model deployed, but this might change by the time you watch this video. But even without thinking,

00:28:13.440 | this model actually solved the issue. When I went to Gemini, I asked it, and it also solved the

00:28:20.000 | issue, even though I also could have tried the thinking model, but it wasn't necessary.

00:28:24.160 | I also gave it to Grok, Grok 3 in this case, and Grok 3 also solved the problem after a bunch of

00:28:30.800 | stuff. So it also solved the issue. And then finally, I went to Perplexity.ai. And the reason

00:28:39.200 | I like Perplexity is because when you go to the model dropdown, one of the models that they host

00:28:43.520 | is this DeepSeq R1. So this has the reasoning with the DeepSeq R1 model, which is the model that we

00:28:50.560 | saw over here. This is the paper. So Perplexity just hosts it and makes it very easy to use.

00:28:58.800 | So I copy pasted it there and I ran it. And I think they render, they like really render it

00:29:04.560 | terribly. But down here, you can see the raw thoughts of the model, even though you have to

00:29:12.480 | expand them. But you see like, okay, the user is having trouble with the gradient check, and then

00:29:17.600 | it tries out a bunch of stuff. And then it says, but wait, when they accumulate the gradients,

00:29:21.120 | they're doing the thing incorrectly. Let's check the order. The parameters are packed as this,

00:29:26.000 | and then it notices the issue. And then it kind of like says, that's a critical mistake.

00:29:31.920 | And so it kind of like thinks through it and you have to wait a few minutes and then also comes up

00:29:35.200 | with the correct answer. So basically, long story short, what do I want to show you?

00:29:40.960 | There exists a class of models that we call thinking models. All the different providers

00:29:44.960 | may or may not have a thinking model. These models are most effective for difficult problems in math

00:29:50.880 | and code and things like that. And in those kinds of cases, they can push up the accuracy

00:29:55.600 | of your performance. In many cases, like if you're asking for travel advice or something like that,

00:30:00.080 | you're not going to benefit out of a thinking model. There's no need to wait for one minute

00:30:04.000 | for it to think about some destinations that you might want to go to. So for myself, I usually try

00:30:10.720 | out the non-thinking models because their responses are really fast. But when I suspect the response

00:30:15.040 | is not as good as it could have been, and I want to give the opportunity to the model to think a

00:30:19.280 | bit longer about it, I will change it to a thinking model, depending on whichever one you have

00:30:24.160 | available to you. Now, when you go to Grok, for example, and when I start a new conversation with

00:30:30.320 | Grok, when you put the question here, like, "Hello," you should put something important here,

00:30:37.040 | you see here, "Think." So let the model take its time. So turn on "Think," and then click "Go."

00:30:43.440 | And when you click "Think," Grok, under the hood, switches to the thinking model. And all the

00:30:49.200 | different OLLM providers will kind of like have some kind of a selector for whether or not you

00:30:52.960 | want the model to think, or whether it's okay to just like go with the previous kind of generation

00:30:59.680 | of the models. Okay, now the next section I want to continue to is to tool use. So far, we've only

00:31:06.960 | talked to the language model through text. And this language model is, again, this zip file in

00:31:12.080 | a folder, it's inert, it's closed off, it's got no tools, it's just a neural network that can emit

00:31:18.000 | tokens. So what we want to do now, though, is we want to go beyond that. And we want to give the

00:31:22.720 | model the ability to use a bunch of tools. And one of the most useful tools is an internet search.

00:31:28.880 | And so let's take a look at how we can make models use internet search. So for example,

00:31:34.000 | again, using concrete examples from my own life, a few days ago, I was watching White Lotus season

00:31:40.000 | three. And I watched the first episode. And I love this TV show, by the way. And I was curious when

00:31:45.600 | the episode two was coming out. And so in the old world, you would imagine you go to Google or

00:31:51.920 | something like that, you put in like new episodes of White Lotus season three, and then you start

00:31:56.160 | clicking on these links. And maybe open a few of them. Or something like that, right? And you start

00:32:02.880 | like searching through it and trying to figure it out. And sometimes you luck out and you get a

00:32:07.040 | schedule. But many times you might get really crazy ads, there's a bunch of random stuff going on,

00:32:13.680 | and it's just kind of like an unpleasant experience, right? So wouldn't it be great if a

00:32:17.120 | model could do this kind of a search for you, visit all the webpages, and then take all those

00:32:23.840 | webpages, take all their content and stuff it into the context window, and then basically give you

00:32:31.600 | the response. And that's what we're going to do now. Basically, we have a mechanism or a way,

00:32:37.120 | we introduce a mechanism for the model to emit a special token that is some kind of a

00:32:43.360 | search the internet token. And when the model emits the search the internet token,

00:32:48.480 | the chat GPT application, or whatever LLM application it is you're using, will stop

00:32:54.720 | sampling from the model. And it will take the query that the model gave, it goes off, it does

00:32:59.920 | a search, it visits webpages, it takes all of their text, and it puts everything into the context

00:33:05.840 | window. So now you have this internet search tool that itself can also contribute tokens into our

00:33:12.640 | context window. And in this case, it would be like lots of internet webpages, and maybe there's 10

00:33:17.600 | of them, and maybe it just puts it all together. And this could be thousands of tokens coming from

00:33:21.680 | these webpages, just as we were looking at them ourselves. And then after it has inserted all

00:33:26.480 | those webpages into the context window, it will reference back to your question as to, hey, when

00:33:32.880 | is this season getting released? And it will be able to reference the text and give you the correct

00:33:37.680 | answer. And notice that this is a really good example of why we would need internet search.

00:33:42.240 | Without the internet search, this model has no chance to actually give us the correct answer.

00:33:47.760 | Because like I mentioned, this model was trained a few months ago, the schedule probably was not

00:33:51.600 | known back then. And so when White Lotus Season 3 is coming out is not part of the

00:33:56.880 | real knowledge of the model. And it's not in the zip file, most likely, because this is something

00:34:03.200 | that was presumably decided on in the last few weeks. And so the model has to basically go off

00:34:07.360 | and do internet search to learn this knowledge. And it learns it from the webpages, just like you

00:34:11.680 | and I would without it. And then it can answer the question once that information is in the context

00:34:16.080 | window. And remember, again, that the context window is this working memory. So once we load

00:34:22.000 | the articles, once all of these articles think of their text as being copy pasted into the context

00:34:30.080 | window, now they're in a working memory, and the model can actually answer those questions,

00:34:35.040 | because it's in the context window. So basically, long story short, don't do this manually,

00:34:41.280 | but use tools like Perplexity as an example. So Perplexity.ai had a really nice sort of LLM that

00:34:48.720 | was doing internet search. And I think it was like the first app that really convincingly did this.

00:34:53.920 | More recently, Chachibti also introduced a search button. It says search the web. So we're going to

00:34:59.360 | take a look at that in a second. For now, when are new episodes of White Lotus Season 3 getting

00:35:04.080 | released? You can just ask. And instead of having to do the work manually, we just hit enter,

00:35:08.960 | and the model will visit these web pages, it will create all the queries, and then it will give you

00:35:12.560 | the answer. So it just kind of did a ton of the work for you. And then you can, usually there

00:35:19.120 | will be citations, so you can actually visit those web pages yourself, and you can make sure these

00:35:24.080 | are not hallucinations from the model, and you can actually double check that this is actually

00:35:28.320 | correct. Because it's not in principle guaranteed, it's just something that may or may not work.

00:35:36.240 | If we take this, we can also go to, for example, Chachibti, say the same thing. But now,

00:35:40.640 | when we put this question in, without actually selecting search, I'm not actually 100% sure what

00:35:45.200 | the model will do. In some cases, the model will actually know that this is recent knowledge,

00:35:51.280 | and that it probably doesn't know, and it will create a search. In some cases, we have to declare

00:35:55.760 | that we want to do the search. In my own personal use, I would know that the model doesn't know,

00:36:00.160 | and so I would just select search. But let's see first, let's see if what happens.

00:36:04.720 | Okay, searching the web, and then it prints stuff, and then it cites. So the model actually detected

00:36:12.320 | itself that it needs to search the web, because it understands that this is some kind of a recent

00:36:16.480 | information, etc. So this was correct. Alternatively, if I create a new conversation,

00:36:20.880 | I could have also selected search, because I know I need to search. Enter. And then it does the same

00:36:26.320 | thing, searching the web, and that's the result. So basically, when you're using these LLM,

00:36:31.920 | look for this. For example, Grok. Excuse me. Let's try Grok. Without it, without selecting search.

00:36:43.360 | Okay, so the model does some search, just knowing that it needs to search,

00:36:46.720 | and gives you the answer. So basically, let's see what Claude does.

00:36:52.720 | You see, so Claude doesn't actually have the search tool available. So we'll say,

00:37:00.160 | as of my last update in April 2024, this last update is when the model went through pre-training.

00:37:06.960 | And so Claude is just saying, as of my last update, the knowledge cutoff of April 2024,

00:37:12.960 | it was announced, but it doesn't know. So Claude doesn't have

00:37:17.040 | the internet search integrated as an option, and will not give you the answer.

00:37:22.480 | I expect that this is something that Anthropic might be working on.

00:37:24.880 | Let's try Gemini, and let's see what it says. Unfortunately, no official release date for

00:37:31.840 | White Lotus Season 3 yet. So Gemini 2.0 Pro Experimental does not have access to internet

00:37:40.800 | search, and doesn't know. We could try some of the other ones, like 2.0 Flash. Let me try that.

00:37:50.160 | Okay, so this model seems to know, but it doesn't give citations. Oh wait, okay, there we go.

00:37:55.440 | Sources and related content. So you see how 2.0 Flash actually has the internet search tool,

00:38:02.400 | but I'm guessing that the 2.0 Pro, which is the most powerful model that they have,

00:38:09.120 | this one actually does not have access. And in here, it actually tells us,

00:38:13.120 | 2.0 Pro Experimental lacks access to real-time info and some Gemini features.

00:38:17.200 | So this model is not fully wired with internet search. So long story short, we can get models

00:38:24.320 | to perform Google searches for us, visit the webpages, pull in the information to the context

00:38:29.440 | window, and answer questions. And this is a very, very cool feature. But different models,

00:38:35.440 | possibly different apps, have different amount of integration of this capability,

00:38:41.040 | and so you have to be kind of on the lookout for that. And sometimes the model will automatically

00:38:44.960 | detect that they need to do search, and sometimes you're better off telling the model that you want

00:38:49.600 | it to do the search. So when I'm doing GPT 4.0 and I know that this requires a search,

00:38:55.360 | you probably want to tick that box. So that's search tools. I wanted to show you a few more

00:39:02.320 | examples of how I use the search tool in my own work. So what are the kinds of queries that I use?

00:39:08.160 | And this is fairly easy for me to do because usually for these kinds of cases, I go to

00:39:12.800 | Perplexity just out of habit, even though ChatGPT today can do this kind of stuff as well, as do

00:39:18.240 | probably many other services as well. But I happen to use Perplexity for these kinds of search queries.

00:39:24.080 | So whenever I expect that the answer can be achieved by doing basically something like

00:39:29.600 | Google search and visiting a few of the top links, and the answer is somewhere in those top links,

00:39:34.400 | whenever that is the case, I expect to use the search tool, and I come to Perplexity. So here

00:39:39.120 | are some examples. Is the market open today? And this was on precedence day, I wasn't 100% sure.

00:39:46.640 | So Perplexity understands what is today, it will do the search and it will figure out that on

00:39:51.360 | precedence day this was closed. Where's White Lotus season three filmed? Again, this is something that

00:39:57.440 | I wasn't sure that a model would know in its knowledge. This is something niche, so maybe

00:40:01.760 | there's not that many mentions of it on the internet. And also this is more recent, so I

00:40:06.400 | don't expect a model to know by default. So this was a good fit for the search tool. Does Vercel

00:40:15.440 | offer PostgreSQL database? So this was a good example of this, because this kind of stuff

00:40:22.320 | changes over time, and the offerings of Vercel, which is a company, may change over time, and I

00:40:29.440 | want the latest. And whenever something is latest or something changes, I prefer to use the search

00:40:34.240 | tool, so I come to Perplexity. What is the Apple launch tomorrow, and what are some of the rumors?

00:40:40.480 | So again, this is something recent. Where is the Singles Inferno season four cast? Must know. So

00:40:48.720 | this is, again, a good example, because this is very fresh information. Why is the Palantir stock

00:40:54.240 | going up? What is driving the enthusiasm? When is Civilization VII coming out exactly?

00:41:02.240 | This is an example also. Has Brian Johnson talked about the toothpaste he uses?

00:41:06.000 | And I was curious, basically, about what Brian does. And again, it has the two features. Number

00:41:11.680 | one, it's a little bit esoteric, so I'm not 100% sure if this is at scale on the internet and would

00:41:16.560 | be part of knowledge of a model. And number two, this might change over time, so I want to know

00:41:21.200 | what toothpaste he uses most recently. And so this is a good fit, again, for a search tool.

00:41:25.360 | Is it safe to travel to Vietnam? This can potentially change over time.

00:41:30.400 | And then I saw a bunch of stuff on Twitter about a USAID, and I wanted to know what's the deal,

00:41:35.360 | so I searched about that. And then you can dive in a bunch of ways here.

00:41:40.000 | But this use case here is along the lines of, "I see something trending, and I'm curious what's

00:41:46.160 | happening. What is the gist of it?" And so I very often just quickly bring up a search of what's

00:41:51.840 | happening, and then get a model to just give me a gist of roughly what happened. Because a lot of

00:41:56.800 | the individual tweets or posts might not have the full context just by itself. So these are examples

00:42:02.160 | of how I use a search tool. Okay, next up, I would like to tell you about this capability called Deep

00:42:07.440 | Research. And this is fairly recent, only as of like a month or two ago. But I think it's incredibly

00:42:12.800 | cool and really interesting, and kind of went under the radar for a lot of people, even though

00:42:16.400 | I think it shouldn't have. So when we go to Chachapiti Pricing here, we notice that Deep

00:42:21.360 | Research is listed here under Pro. So it currently requires $200 per month. So this is the top tier.

00:42:27.120 | However, I think it's incredibly cool. So let me show you by example in what kinds of scenarios

00:42:33.200 | you might want to use it. Roughly speaking, Deep Research is a combination of internet search and

00:42:40.160 | thinking, and rolled out for a long time. So the model will go off, and it will spend tens of

00:42:46.880 | minutes doing with Deep Research. And the first sort of company that announced this was Chachapiti

00:42:53.840 | as part of its pro offering very recently, like a month ago. So here's an example. Recently,

00:43:00.000 | I was on the internet buying supplements, which I know is kind of crazy. But Brian Johnson has this

00:43:05.520 | starter pack, and I was kind of curious about it. And there's the thing called longevity mix, right?

00:43:09.840 | And it's got a bunch of health actives. And I want to know what these things are, right? And

00:43:15.200 | of course, so like CAKG, like what the hell is this? Boost energy production for sustained

00:43:20.400 | vitality. What does that mean? So one thing you could of course do is you could open up Google

00:43:25.440 | search and look at the Wikipedia page or something like that, and do everything that you're kind of

00:43:29.760 | used to. But Deep Research allows you to basically take an alternate route. And it kind of like

00:43:36.320 | processes a lot of this information for you and explains it a lot better. So as an example, we can

00:43:41.360 | do something like this. This is my example prompt. CAKG is one of the health actives in Brian Johnson's

00:43:47.360 | blueprint at 2.5 grams per serving. Can you do research on CAKG? Tell me about why it might

00:43:53.760 | be found in the longevity mix. It's possible efficacy in humans or animal models. It's

00:43:58.800 | potential mechanism of action, any potential concerns or toxicity or anything like that.

00:44:02.880 | Now, here I have this button available to me, and you won't unless you pay $200 per month right now.

00:44:10.000 | But I can turn on Deep Research. So let me copy paste this and hit go.

00:44:13.360 | And now the model will say, "Okay, I'm going to research this." And then sometimes it likes

00:44:19.200 | to ask clarifying questions before it goes off. So a focus on human clinical studies,

00:44:24.160 | animal models, or both. So let's say both. Specific sources, all sources, I don't know.

00:44:31.280 | A comparison to other longevity compounds, not needed. Comparison, just AKG.

00:44:38.240 | We can be pretty brief. The model understands. And we hit go. And then, okay, I'll research CAKG,

00:44:44.960 | starting research. And so now we have to wait for probably about 10 minutes or so. And if you'd like

00:44:50.160 | to click on it, you can get a bunch of preview of what the model is doing on a high level.

00:44:53.760 | So this will go off and it will do a combination of, like I said, thinking and internet search.

00:45:00.160 | But it will issue many internet searches. It will go through lots of papers. It will look at papers

00:45:05.120 | and it will think and it will come back 10 minutes from now. So this will run for a while. Meanwhile,

00:45:10.640 | while this is running, I'd like to show you equivalents of it in the industry. So inspired

00:45:17.440 | by this, a lot of people were interested in cloning it. And so one example is, for example,

00:45:21.680 | perplexity. So perplexity, when you go through the model dropdown, has something called deep

00:45:25.840 | research. And so you can issue the same queries here. And we can give this to the model. And so

00:45:32.480 | you can issue the same queries here. And we can give this to perplexity. And then grok, as well,

00:45:38.560 | has something called deep search instead of deep research. But I think that grok's deep

00:45:42.720 | search is kind of like deep research, but I'm not 100% sure. So we can issue grok deep search

00:45:48.480 | as well. Grok three, deep search, go. And this model is going to go off as well.

00:45:56.640 | Now, I think, where's my Chachapiti? So Chachapiti is kind of like maybe a quarter done.

00:46:04.080 | Perplexity is going to be done soon. Okay, still thinking. And grok is still going as well.

00:46:11.600 | I like grok's interface the most. It seems like, okay, so basically it's looking up all kinds of

00:46:17.680 | papers, WebMD, browsing results, and it's kind of just getting all this. Now, while this is all

00:46:23.920 | going on, of course, it's accumulating a giant context window and it's processing all that

00:46:28.720 | information, trying to kind of create a report for us. So key points, what is CAKG and why is

00:46:37.360 | it in the longevity mix? How is it associated to longevity, et cetera? And so it will do citations

00:46:43.280 | and it will kind of like tell you all about it. And so this is not a simple and short response.

00:46:47.280 | This is kind of like, almost like a custom research paper on any topic you would like.

00:46:52.320 | And so this is really cool and it gives a lot of references potentially for you to go off

00:46:55.920 | and do some of your own reading and maybe ask some clarifying questions afterwards. But it's

00:47:00.080 | actually really incredible that it gives you all these like different citations and processes the

00:47:03.680 | information for you a little bit. Let's see if Perplexity finished. Okay, Perplexity is still

00:47:09.200 | researching and Chachapiti is also researching. So let's briefly pause the video and I'll come

00:47:15.680 | back when this is done. Okay, so Perplexity finished and we can see some of the report

00:47:19.600 | that it wrote up. So there's some references here and some basic description. And then Chachapiti

00:47:26.960 | also finished and it also thought for five minutes, looked at 27 sources and produced a report.

00:47:32.880 | So here it talked about research in worms, Drosophila in mice and in human trials that

00:47:41.360 | are ongoing. And then the proposed mechanism of action and some safety and potential concerns

00:47:47.760 | and references, which you can dive deeper into. So usually in my own work right now,

00:47:53.680 | I've only used this maybe for like 10 to 20 queries so far, something like that. Usually

00:47:58.400 | I find that the Chachapiti offering is currently the best. It is the most thorough, it reads the

00:48:03.360 | best, it is the longest, it makes most sense when I read it. And I think the Perplexity and the Grok

00:48:09.920 | are a little bit shorter and a little bit briefer and don't quite get into the same detail as the

00:48:17.200 | deep research from Google, from Chachapiti right now. I will say that everything that is given to

00:48:22.720 | you here, again, keep in mind that even though it is doing research and it's pulling stuff in,

00:48:27.760 | there are no guarantees that there are no hallucinations here. Any of this can be

00:48:32.400 | hallucinated at any point in time. It can be totally made up, fabricated, misunderstood by

00:48:36.160 | the model. So that's why these citations are really important. Treat this as your first draft.

00:48:41.040 | Treat this as papers to look at, but don't take this as definitely true. So here, what I would

00:48:47.520 | do now is I would actually go into these papers and I would try to understand, is Chachapiti

00:48:52.240 | understanding it correctly? And maybe I have some follow-up questions, et cetera, so you can do all

00:48:55.680 | that. But still incredibly useful to see these reports once in a while, to get a bunch of

00:49:00.800 | sources that you might want to descend into afterwards. Okay, so just like before,

00:49:04.960 | I wanted to show a few brief examples of how I've used deep research. So for example, I was

00:49:10.480 | trying to change a browser because Chrome upset me, and so it deleted all my tabs. So I was looking

00:49:19.440 | at either Brave or Arc, and I was most interested in which one is more private. And basically,

00:49:25.920 | Chachapiti compiled this report for me, and this was actually quite helpful. And I went into some

00:49:30.080 | of the sources and I understood why Brave is basically TLDR significantly better. And that's

00:49:36.560 | why, for example, here, I'm using Brave because I've switched to it now. And so this is an example

00:49:41.600 | of basically researching different kinds of products and comparing them. I think that's a

00:49:45.760 | good fit for deep research. Here, I wanted to know about a life extension in mice. So it kind of gave

00:49:51.280 | me a very long reading, but basically mice are an animal model for longevity, and different labs

00:49:58.400 | have tried to extend it with various techniques. And then here, I wanted to explore LLM labs in the

00:50:04.240 | USA, and I wanted a table of how large they are, how much funding they've had, et cetera. So this

00:50:11.440 | is the table that it produced. Now, this table is basically hit and miss, unfortunately. So I wanted

00:50:16.320 | to show it as an example of a failure. I think some of these numbers, I didn't fully check them,

00:50:21.040 | but they don't seem way too wrong. Some of this looks wrong. But the big omission I definitely

00:50:26.960 | see is that XAI is not here, which I think is a really major omission. And then also, conversely,

00:50:32.480 | Hugging Face should probably not be here because I asked specifically about LLM labs in the USA.

00:50:37.200 | And also, Eleuther AI, I don't think should count as a major LLM lab due to mostly its resources.

00:50:45.280 | And so I think it's kind of a hit and miss. Things are missing. I don't fully trust these numbers. I

00:50:50.160 | have to actually look at them. And so again, use it as a first draft. Don't fully trust it. Still

00:50:56.000 | very helpful. That's it. So what's really happening here that is interesting is that we are providing

00:51:01.600 | the LLM with additional concrete documents that it can reference inside its context window. So the

00:51:08.480 | model is not just relying on the knowledge, the hazy knowledge of the world through its parameters

00:51:14.240 | and what it knows in its brain. We're actually giving it concrete documents. It's as if you and

00:51:19.600 | I reference specific documents like on the internet or something like that, while we are kind of

00:51:25.040 | producing some answer for some question. Now, we can do that through an internet search or like a

00:51:29.440 | tool like this. But we can also provide these LLMs with concrete documents ourselves through a file

00:51:35.200 | upload. And I find this functionality pretty helpful in many ways. So as an example, let's

00:51:40.320 | look at Clod because they just released Clod 3.7 while I was filming this video. So this is a new

00:51:45.120 | Clod model that is now the state of the art. And notice here that we have thinking mode now as a

00:51:51.440 | 3.7. And so normal is what we looked at so far, but they just released extended best for math and

00:51:57.280 | coding challenges. And what they're not saying, but it's actually true under the hood, probably

00:52:01.360 | most likely, is that this was trained with reinforcement learning in a similar way that

00:52:05.600 | all the other thinking models were produced. So what we can do now is we can upload the documents

00:52:11.440 | that we wanted to reference inside its context window. So as an example, there's this paper that

00:52:16.960 | came out that I was kind of interested in. It's from ARC Institute. And it's basically a language

00:52:23.040 | model trained on DNA. And so I was kind of curious. I mean, I'm not from biology, but I was kind of

00:52:29.040 | curious what this is. And this is a perfect example of what LLMs are extremely good for because you

00:52:35.520 | can upload these documents to the LLM and you can load this PDF into the context window and then ask

00:52:41.120 | questions about it and basically read the documents together with an LLM and ask questions off it.

00:52:47.120 | So the way you do that is you basically just drag and drop. So we can take that PDF and just drop it

00:52:52.240 | here. This is about 30 megabytes. Now, when Claude gets this document, it is very likely that they

00:53:02.240 | actually discard a lot of the images and that kind of information. I don't actually know exactly what

00:53:08.320 | they do under the hood and they don't really talk about it. But it's likely that the images are

00:53:12.480 | thrown away. Or if they are there, they may not be as well understood as you and I would

00:53:19.680 | understand them potentially. And it's very likely that what's happening under the hood is that

00:53:23.440 | this PDF is basically converted to a text file and that text file is loaded into the token window.

00:53:29.440 | And once it's in the token window, it's in the working memory and we can ask questions off it.

00:53:34.000 | So typically when I start reading papers together with any of these LLMs, I just ask for,

00:53:39.440 | "Can you give me a summary of this paper?" Let's see what Claude 3.7 says.

00:53:49.040 | Okay, I'm exceeding the length limit of this chat. Oh, God. Really? Oh, damn. Okay,

00:54:00.800 | well, let's try chat GPT. Can you summarize this paper?

00:54:10.640 | And we're using GPT 4.0 and we're not using thinking, which is okay. We can start by not

00:54:22.720 | thinking. Reading documents. Summary of the paper, "Genome Modeling and Design Across All

00:54:31.760 | Domains of Life." So this paper introduces Evo 2 large-scale biological foundation model

00:54:36.800 | and then key features and so on. So I personally find this pretty helpful. And then we can kind

00:54:48.080 | of go back and forth. And as I'm reading through the abstract and the introduction, et cetera,

00:54:52.560 | I am asking questions of the LLM and it's kind of making it easier for me to understand the paper.

00:54:57.920 | Another way that I like to use this functionality extensively is when I'm reading books. It is

00:55:02.960 | rarely ever the case anymore that I read books just by myself. I always involve an LLM to help

00:55:08.240 | me read a book. So a good example of that recently is The Wealth of Nations, which I was reading

00:55:13.120 | recently. And it is a book from 1776 written by Adam Smith and it's kind of like the foundation

00:55:17.840 | of classical economics. And it's a really good book. And it's kind of just very interesting to

00:55:22.400 | me that it was written so long ago, but it has a lot of modern day kind of like, it's just got a

00:55:27.360 | lot of insights that I think are very timely even today. So the way I read books now, as an example,

00:55:32.960 | is you basically pull up the book and you have to get access to like the raw content of that

00:55:38.720 | information. In the case of Wealth of Nations, this is easy because it is from 1776. So you can

00:55:43.440 | just find it on Wealth Project Gutenberg as an example. And then basically find the chapter that

00:55:49.360 | you are currently reading. So as an example, let's read this chapter from book one. And this chapter

00:55:54.960 | I was reading recently, and it kind of goes into the division of labor and how it is limited by the

00:56:01.840 | extent of the market. Roughly speaking, if your market is very small, then people can't specialize.

00:56:07.600 | And specialization is what is basically huge. Specialization is extremely important for wealth

00:56:14.640 | creation because you can have experts who specialize in their simple little task. But you

00:56:21.040 | can only do that at scale because without the scale, you don't have a large enough market to

00:56:26.720 | sell to your specialization. So what we do is we copy paste this book, this chapter, at least.

00:56:34.640 | This is how I like to do it. We go to say Claude and we say something like, "We are reading the

00:56:41.360 | Wealth of Nations." Now remember, Claude has knowledge of the Wealth of Nations but probably

00:56:47.520 | doesn't remember exactly the content of this chapter. So it wouldn't make sense to ask Claude

00:56:53.200 | questions about this chapter directly because he probably doesn't remember what the chapter is

00:56:56.960 | about. But we can remind Claude by loading this into the context window. So we're reading the

00:57:02.160 | Wealth of Nations. "Please summarize this chapter to start." And then what I do here is I copy paste.

00:57:09.360 | Now in Claude, when you copy paste, they don't actually show all the text inside the text box.

00:57:15.440 | They create a little text attachment when it is over some size. And so we can click enter.

00:57:21.360 | And we just kind of like start off. Usually I like to start off with a summary of what this

00:57:27.840 | chapter is about just so I have a rough idea. And then I go in and I start reading the chapter.

00:57:32.400 | And if at any point we have any questions, then we just come in and just ask our question.

00:57:37.360 | And I find that basically going hand in hand with LLMs dramatically increases my retention,

00:57:43.520 | my understanding of these chapters. And I find that this is especially the case when you're

00:57:47.520 | reading, for example, documents from other fields, like for example, biology, or for example,

00:57:53.440 | documents from a long time ago, like 1776, where you sort of need a little bit of help of even

00:57:58.080 | understanding what the basics of the language. Or for example, I would feel a lot more courage

00:58:03.440 | approaching a very old text that is outside of my area of expertise. Maybe I'm reading Shakespeare,

00:58:07.920 | or I'm reading things like that. I feel like LLMs make a lot of reading very dramatically more

00:58:14.320 | accessible than it used to be before, because you're not just right away confused. You can

00:58:18.800 | actually kind of go slowly through it and figure it out together with the LLM in hand. So I use this

00:58:24.640 | extensively, and I think it's extremely helpful. I'm not aware of tools, unfortunately, that make

00:58:29.920 | this very easy for you. Today, I do this clunky back and forth. So literally, I will find the book

00:58:36.240 | somewhere, and I will copy paste stuff around. And I'm going back and forth. And it's extremely

00:58:41.520 | awkward and clunky. And unfortunately, I'm not aware of a tool that makes this very easy for you.

00:58:46.000 | But obviously, what you want is as you're reading a book, you just want to highlight the passage and

00:58:50.320 | ask questions about it. This currently, as far as I know, does not exist. But this is extremely

00:58:55.360 | helpful. I encourage you to experiment with it. And don't read books alone. Okay, the next very

00:59:01.680 | powerful tool that I now want to turn to is the use of a Python interpreter, or basically giving

00:59:07.440 | the ability to the LLM to use and write computer programs. So instead of the LLM giving you an

00:59:15.360 | answer directly, it has the ability now to write a computer program and to emit special tokens

00:59:22.160 | that the ChachiPT application recognizes as, hey, this is not for the human. This is basically

00:59:29.840 | saying that whatever I output it here is actually a computer program, please go off and run it and

00:59:35.120 | give me the result of running that computer program. So it is the integration of the language

00:59:40.720 | model with a programming language here, like Python. So this is extremely powerful. Let's

00:59:46.480 | see the simplest example of where this would be used and what this would look like. So if I go

00:59:52.960 | go to ChachiPT, and I give it some kind of a multiplication problem, let's say 30 times 9,

00:59:57.280 | or something like that, then this is a fairly simple multiplication. And you and I can probably

01:00:03.440 | do something like this in our head, right? Like 30 times 9, you can just come up with the result

01:00:07.840 | of 270, right? So let's see what happens. Okay, so LLM did exactly what I just did. It calculated

01:00:15.600 | the result of the multiplication to be 270. But it's actually not really doing math, it's actually

01:00:20.320 | more like almost memory work. But it's easy enough to do in your head. So there was no tool use

01:00:27.680 | involved here. All that happened here was just the zip file doing next token prediction and gave the

01:00:34.720 | correct result here in its head. The problem now is what if we want something more, more complicated?

01:00:40.560 | So what is this times this? And now of course, this, if I asked you to calculate this, you would

01:00:49.200 | give up instantly because you know that you can't possibly do this in your head. And you would be

01:00:53.360 | looking for a calculator. And that's exactly what the LLM does now too. And OpenAI has trained

01:00:58.720 | ChachiPT to recognize problems that it cannot do in its head, and to rely on tools instead. So what

01:01:04.960 | I expect ChachiPT to do for this kind of a query is to turn to tool use. So let's see what it looks

01:01:09.520 | like. Okay, there we go. So what's opened up here is what's called the Python interpreter.

01:01:16.320 | And Python is basically a little programming language. And instead of the LLM telling you

01:01:21.840 | directly what the result is, the LLM writes a program. And then not shown here are special

01:01:28.000 | tokens that tell the ChachiPT application to please run the program. And then the LLM pauses

01:01:33.520 | execution. Instead, the Python program runs, creates a result, and then passes this result

01:01:40.720 | back to the language model as text. And the language model takes over and tells you that

01:01:45.360 | the result of this is that. So this is tool use, incredibly powerful. And OpenAI has trained

01:01:52.000 | ChachiPT to kind of like know in what situations to lean on tools. And they've taught it to do

01:01:58.320 | that by example. So human labelers are involved in curating datasets that kind of tell the model

01:02:05.440 | by example in what kinds of situations it should lean on tools and how. But basically, we have a

01:02:10.320 | Python interpreter. And this is just an example of multiplication. But this is significantly more

01:02:16.640 | powerful. So let's see what we can actually do inside programming languages. Before we move on,

01:02:22.240 | I just wanted to make the point that unfortunately, you have to kind of keep track of which LLMs that

01:02:27.920 | you're talking to have different kinds of tools available to them. Because different LLMs might

01:02:32.880 | not have all the same tools. And in particular, LLMs that do not have access to the Python

01:02:36.800 | interpreter or programming language, or are unwilling to use it might not give you correct

01:02:41.200 | results in some of these harder problems. So as an example, here we saw that ChachiPT correctly

01:02:47.280 | used a programming language and didn't do this in its head. Grok3 actually, I believe, does not

01:02:52.720 | have access to a programming language, like a Python interpreter. And here, it actually does

01:02:58.560 | this in its head and gets remarkably close. But if you actually look closely at it, it gets it wrong.

01:03:05.200 | This should be 120 instead of 060. So Grok3 will just hallucinate through this multiplication

01:03:12.080 | and do it in its head and get it wrong. But actually, like remarkably close. Then I tried

01:03:18.240 | Clod. And Clod actually wrote, in this case, not Python code, but it wrote JavaScript code.

01:03:23.920 | But JavaScript is also a programming language and gets the correct result. Then I came to Gemini

01:03:29.360 | and I asked 2.0 Pro. And Gemini did not seem to be using any tools. There's no indication of that.

01:03:35.840 | And yet, it gave me what I think is the correct result, which actually kind of surprised me.

01:03:40.160 | So Gemini, I think, actually calculated this in its head correctly. And the way we can tell that

01:03:46.080 | this is, which is kind of incredible, the way we can tell that it's not using tools is we can just

01:03:50.480 | try something harder. What is, we have to make it harder for it. Okay, so it gives us some result.

01:03:59.360 | And then I can use my calculator here, and it's wrong, right? So this is using my MacBook Pro

01:04:06.400 | calculator. And two, it's not correct, but it's like remarkably close, but it's not correct.

01:04:13.040 | But it will just hallucinate the answer. So I guess like my point is, unfortunately,

01:04:19.360 | the state of the LLMs right now is such that different LLMs have different tools available

01:04:24.160 | to them, and you kind of have to keep track of it. And if they don't have the tools available,

01:04:28.800 | they'll just do their best, which means that they might hallucinate a result for you. So that's

01:04:33.920 | something to look out for. Okay, so one practical setting where this can be quite powerful is what's

01:04:38.480 | called chatgpt advanced data analysis. And as far as I know, this is quite unique to chatgpt itself.

01:04:45.120 | And it basically gets chatgpt to be kind of like a junior data analyst who you can kind of

01:04:51.840 | collaborate with. So let me show you a concrete example without going into full detail. So first,

01:04:57.520 | we need to get some data that we can analyze and plot and chart, etc. So here in this case, I said,

01:05:03.280 | let's research open AI evaluation as an example. And I explicitly asked chatgpt to use the search

01:05:08.160 | tool because I know that under the hood, such a thing exists. And I don't want it to be hallucinating

01:05:13.360 | data to me, I want it to actually look it up and back it up and create a table where each year

01:05:18.320 | we have the valuation. So these are the open AI evaluations over time. Notice how in 2015,

01:05:24.400 | it's not applicable. So the valuation is like unknown. Then I said now plot this use log scale

01:05:30.640 | for y axis. And so this is where this gets powerful. chatgpt goes off and writes a program

01:05:36.320 | that plots the data over here. So it created a little figure for us, and it sort of ran it and

01:05:43.600 | showed it to us. So this can be quite nice and valuable because it's a very easy way to basically

01:05:48.080 | collect data, upload data in a spreadsheet, visualize it, etc. I will note some of the

01:05:53.520 | things here. So as an example, notice that we had NA for 2015, but chatgpt when it was writing the

01:05:59.920 | code, and again, I would always encourage you to scrutinize the code, it put in 0.1 for 2015.

01:06:06.240 | And so basically, it implicitly assumed that it made the assumption here in code

01:06:12.160 | that the valuation of 2015 was 100 million. And because it put in 0.1, and kind of like did it

01:06:19.120 | without telling us. So it's a little bit sneaky. And that's why you kind of have to pay attention

01:06:23.200 | a little bit to the code. So I'm familiar with the code, and I always read it. But I think

01:06:28.880 | I would be hesitant to potentially recommend the use of these tools,

01:06:32.320 | if people aren't able to like read it and verify it a little bit for themselves.

01:06:36.400 | Now, fit a trendline and extrapolate until the year 2030. Mark the expected valuation in 2030.

01:06:44.640 | So it went off, and it basically did a linear fit. And it's using scipy's curve fit. And it did this

01:06:52.880 | and came up with a plot. And it told me that the valuation based on the trend in 2030 is

01:06:59.680 | approximately 1.7 trillion, which sounds amazing, except here, I became suspicious, because I see

01:07:06.480 | that chatgpt is telling me it's 1.7 trillion. But when I look here at 2030, it's printing

01:07:12.720 | 20271.7b. So it's extrapolation when it's printing the variable is inconsistent with 1.7 trillion.

01:07:22.000 | This makes it look like that valuation should be about 20 trillion. And so that's what I said,

01:07:27.280 | print this variable directly by itself, what is it? And then it sort of like rewrote the code

01:07:32.080 | and gave me the variable itself. And as we see in the label here, it is indeed 20271.7b, etc.

01:07:40.080 | So in 2030, the true exponential trend extrapolation would be a valuation of 20 trillion.

01:07:51.040 | So I was like, I was trying to confront chatgpt, and I was like, you lied to me, right? And it's

01:07:54.400 | like, yeah, sorry, I messed up. So I guess I like this example, because number one, it shows the

01:08:01.200 | power of the tool in that it can create these figures for you. And it's very nice. But I think

01:08:07.040 | number two, it shows the trickiness of it, where, for example, here, it made an implicit assumption.

01:08:13.920 | And here, it actually told me something. It told me just the wrong, it hallucinated 1.7 trillion.

01:08:19.920 | So again, it is kind of like a very, very junior data analyst. It's amazing that it can plot

01:08:24.880 | figures. But you have to kind of still know what this code is doing. And you have to be careful

01:08:30.320 | and scrutinize it and make sure that you are really watching very closely, because your junior

01:08:34.960 | analyst is a little bit absent minded, and not quite right all the time. So really powerful,

01:08:42.240 | but also be careful with this. I won't go into full details of advanced data analysis. But

01:08:47.680 | there were many videos made on this topic. So if you would like to use some of this in your work,

01:08:53.040 | then I encourage you to look at some of these videos. I'm not going to go into the full detail.

01:08:57.600 | So a lot of promise, but be careful. Okay, so I've introduced you to ChachiPT and advanced

01:09:02.880 | data analysis, which is one powerful way to basically have LLMs interact with code,

01:09:07.760 | and add some UI elements like showing of figures and things like that. I would now like to

01:09:12.880 | introduce you to one more related tool. And that is specific to Claude, and it's called artifacts.

01:09:18.320 | So let me show you by example what this is. So you have a conversation with Claude. And I'm asking,

01:09:25.680 | generate 20 flashcards from the following text. And for the text itself, I just came to the Adam

01:09:32.640 | Smith Wikipedia page, for example, and I copy pasted this introduction here. So I copy pasted

01:09:38.240 | this here and ask for flashcards. And Claude responds with 20 flashcards. So for example,

01:09:45.440 | when was Adam Smith baptized on June 16, etc. When did he die? What was his nationality, etc.

01:09:52.320 | So once we have the flashcards, we actually want to practice these flashcards. And so this is where

01:09:57.760 | I continue the conversation, I say, now use the artifacts feature to write a flashcards app to

01:10:03.280 | test these flashcards. And so Claude goes off and writes code for an app that basically formats all

01:10:13.280 | of this into flashcards. And that looks like this. So what Claude wrote specifically was this core

01:10:19.760 | code here. So it uses a react library, and then basically creates all these components,

01:10:26.160 | it hardcodes the q&a into this app, and then all the other functionality of it. And then

01:10:33.120 | the cloud interface basically is able to load these react components directly in your browser.

01:10:37.840 | And so you end up with an app. So when was Adam Smith baptized, and you can click to reveal the

01:10:44.480 | answer. And then you can say whether you got it correct or not. When did he die? What was his

01:10:50.080 | nationality, etc. So you can imagine doing this, and then maybe we can reset the progress or

01:10:54.880 | shuffle the cards, etc. So what happened here is that Claude wrote us a super duper custom app just

01:11:02.400 | for us, right here. And typically, what we're used to is some software engineers write apps,

01:11:10.640 | they make them available, and then they give you maybe some way to customize them, or maybe to

01:11:14.160 | upload flashcards. Like for example, the Anki app, you can import flashcards and all this kind of

01:11:18.720 | stuff. This is a very different paradigm, because in this paradigm, Claude just writes the app just

01:11:24.000 | for you and deploys it here in your browser. Now keep in mind that a lot of apps you'll find on the

01:11:29.920 | internet, they have entire backends, etc. There's none of that here, there's no database or anything

01:11:34.160 | like that. But these are like local apps that can run in your browser. And they can get fairly

01:11:39.760 | sophisticated and useful in some cases. So that's Claude artifacts. Now to be honest, I'm not

01:11:46.240 | actually a daily user of artifacts. I use it once in a while. I do know that a large number of people

01:11:52.160 | are experimenting with it, and you can find a lot of artifacts showcases because they're easy to

01:11:56.240 | share. So these are a lot of things that people have developed, various timers and games and

01:12:01.440 | things like that. But the one use case that I did find very useful in my own work is basically the

01:12:08.960 | use of diagrams, diagram generation. So as an example, let's go back to the book chapter of

01:12:14.880 | Adam Smith that we were looking at. What I do sometimes is we are reading The Wealth of Nations

01:12:20.240 | by Adam Smith, I'm attaching chapter three and book one, please create a conceptual diagram of

01:12:24.640 | this chapter. And when Claude hears conceptual diagram of this chapter, very often it will write

01:12:30.480 | code that looks like this. And if you're not familiar with this, this is using the mermaid

01:12:36.960 | library to basically create or define a graph. And then this is plotting that mermaid diagram.

01:12:45.280 | And so Claude analyzed the chapter and figures out that, okay, the key principle that's being

01:12:50.080 | communicated here is as follows, that basically the division of labor is related to the extent

01:12:55.920 | of the market, the size of it. And then these are the pieces of the chapter. So there's the

01:13:00.560 | comparative example of trade and how much easier it is to do on land and on water, and the specific

01:13:07.280 | example that's used. And that geographic factors actually make a huge difference here. And then

01:13:12.560 | the comparison of land transport versus water transport and how much easier water transport is.

01:13:18.160 | And then here we have some early civilizations that have all benefited from basically the

01:13:24.240 | availability of water transport and have flourished as a result of it because they

01:13:27.840 | support specialization. So if you're a conceptual kind of like visual thinker,

01:13:33.200 | and I think I'm a little bit like that as well, I like to lay out information as like a tree like

01:13:39.040 | this, and it helps me remember what that chapter is about very easily. And I just really enjoy these

01:13:43.440 | diagrams and like kind of getting a sense of like, okay, what is the layout of the argument? How is

01:13:47.360 | it arranged spatially and so on. And so if you're like me, then you will definitely enjoy this.

01:13:52.400 | And you can make diagrams of anything, of books, of chapters, of source codes, of anything really.

01:13:59.360 | And so I specifically find this fairly useful. Okay, so I've shown you that LLMs are quite good

01:14:05.120 | at writing code. So not only can they emit code, but a lot of the apps like

01:14:10.080 | ChatGPT and Cloud and so on have started to like partially run that code in the browser.

01:14:16.160 | So ChatGPT will create figures and show them, and Cloud Artifacts will actually like integrate

01:14:21.840 | your React component and allow you to use it right there in line in the browser.

01:14:25.600 | Now, actually, majority of my time personally and professionally is spent writing code.

01:14:31.760 | But I don't actually go to ChatGPT and ask for snippets of code, because that's way too slow.

01:14:35.920 | Like ChatGPT just doesn't have the context to work with me professionally to create code.

01:14:41.840 | And the same goes for all the other LLMs. So instead of using features of these LLMs in

01:14:48.400 | a web browser, I use a specific app. And I think a lot of people in the industry do as well.

01:14:52.880 | And this can be multiple apps by now, VS Code, Windsurf, Cursor, etc. So I like to use Cursor

01:15:00.960 | currently. And this is a separate app you can get for your, for example, Macbook. And it works with

01:15:06.320 | the files on your file system. So this is not a web inter... this is not some kind of a web page

01:15:11.680 | you go to. This is a program you download, and it references the files you have on your computer.

01:15:16.800 | And then it works with those files and edits them with you. So the way this looks is as follows.

01:15:22.800 | Here I have a simple example of a React app that I built over a few minutes with Cursor.

01:15:30.880 | And under the hood, Cursor is using Cloud 3.7 Sonnet. So under the hood, it is calling the API

01:15:38.000 | of Anthropic and asking Cloud to do all of this stuff. But I don't have to manually go to Cloud

01:15:45.920 | and copy-paste chunks of code around. This program does that for me and has all of the context of the

01:15:51.440 | files in the directory and all this kind of stuff. So the app that I developed here is a very simple

01:15:56.560 | tic-tac-toe as an example. And Cloud wrote this in a few, probably a minute. And we can just play.

01:16:04.240 | X can win. Or we can tie. Oh wait, sorry, I accidentally won. You can also tie.

01:16:14.560 | And I'd just like to show you briefly, this is a whole separate video of how you would use Cursor

01:16:19.840 | to be efficient. I just want you to have a sense that I started from a completely new project,

01:16:25.120 | and I asked the Composer app here, as it's called, the Composer feature, to basically set up a new

01:16:31.520 | React repository, delete a lot of the boilerplate, please make a simple tic-tac-toe app. And all of

01:16:39.200 | this stuff was done by Cursor. I didn't actually really do anything except for write five sentences.

01:16:43.280 | And then it changed everything and wrote all the CSS, JavaScript, etc. And then I'm running it here

01:16:50.240 | and hosting it locally and interacting with it in my browser. So that's Cursor. It has the context

01:16:57.520 | of your apps, and it's using Cloud remotely through an API without having to access the

01:17:03.040 | web page. And a lot of people, I think, develop in this way at this time. And these tools have

01:17:11.200 | become more and more elaborate. So in the beginning, for example, you could only say,

01:17:16.240 | change, like, oh, Control-K, please change this line of code to do this or that. And then after

01:17:23.200 | that, there was a Control-L, Command-L, which is, oh, explain this chunk of code.

01:17:27.760 | And you can see that there's going to be an LLM explaining this chunk of code. And what's happening

01:17:34.080 | under the hood is it's calling the same API that you would have access to if you actually did enter

01:17:38.640 | here. But this program has access to all the files, so it has all the context. And now what we're up

01:17:44.800 | to is not Command-K and Command-L. We're now up to Command-I, which is this tool called Composer.

01:17:51.680 | And especially with the new agent integration, the Composer is like an autonomous agent on your

01:17:56.480 | codebase. It will execute commands. It will change all the files as it needs to. It can edit across

01:18:04.160 | multiple files. And so you're mostly just sitting back and you're giving commands. And the name for

01:18:11.520 | this is called Vibe Coding, a name with that I think I probably minted. And Vibe Coding just

01:18:17.600 | refers to giving control to Composer and just telling it what to do and hoping that it works.

01:18:25.040 | Now, worst comes to worst, you can always fall back to the good old programming because we have

01:18:30.160 | all the files here, we can go over all the CSS, and we can inspect everything. And if you're a

01:18:36.000 | programmer, then in principle, you can change this arbitrarily. But now you have a very helpful

01:18:40.240 | system that can do a lot of the low-level programming for you. So let's take it for a

01:18:44.720 | spin briefly. Let's say that when either X or O wins, I want confetti or something.

01:18:52.960 | And let's just see what it comes up with.

01:18:55.840 | Okay, I'll add a confetti effect when a player wins the game. It wants me to run React Confetti,

01:19:06.000 | which apparently is a library that I didn't know about. So we'll just say okay. It installed it,

01:19:12.160 | and now it's going to update the app. So it's updating app.tsx, the TypeScript file,

01:19:19.600 | to add the confetti effect when a player wins. And it's currently writing the code,

01:19:23.440 | so it's generating. And we should see it in a bit. Okay, so it basically added this chunk of code,

01:19:31.760 | and a chunk of code here, and a chunk of code here.

01:19:35.520 | And then we'll also add some additional styling to make the winning cell stand out.

01:19:41.280 | Okay, still generating. Okay, and it's adding some CSS for the winning cells.

01:19:49.920 | So honestly, I'm not keeping full track of this. It imported React Confetti.

01:19:55.360 | This all seems pretty straightforward and reasonable, but I'd have to actually like

01:20:00.720 | really dig in. Okay, it wants to add a sound effect when a player wins, which is pretty

01:20:07.120 | ambitious, I think. I'm not actually 100% sure how it's going to do that, because I don't know

01:20:12.480 | how it gains access to a sound file like that. I don't know where it's going to get the sound file

01:20:16.560 | from. But every time it saves a file, we actually are deploying it. So we can actually try to

01:20:26.080 | refresh and just see what we have right now. Also, it added a new effect. You see how it kind

01:20:31.760 | of like fades in, which is kind of cool. And now we'll win. Whoa, okay. Didn't actually expect that

01:20:39.600 | to work. This is really elaborate now. Let's play again. Whoa. Okay. Oh, I see. So it actually

01:20:55.840 | paused and it's waiting for me. So it wants me to confirm the command. So make public sounds.

01:21:00.400 | I had to confirm it explicitly. Let's create a simple audio component to play victory sound,

01:21:08.400 | sound/victorymp3. The problem with this will be the victory.mp3 doesn't exist. So I wonder what

01:21:15.520 | it's going to do. It's downloading it. It wants to download it from somewhere. Let's just go along

01:21:21.920 | with it. Let's add a fallback in case the sound file doesn't exist. In this case, it actually

01:21:32.080 | does exist. And yep, we can git add and we can basically create a git commit out of this.

01:21:42.720 | Okay. So the composer thinks that it is done. So let's try to take it for a spin.

01:21:49.600 | Okay. So yeah, pretty impressive. I don't actually know where it got this sound file from.

01:21:55.520 | I don't know where this URL comes from, but maybe this just appears in a lot of repositories and

01:22:01.600 | sort of cloud kind of like knows about it. But I'm pretty happy with this. So we can accept all. And

01:22:08.800 | that's it. And then we, as you can get a sense of, we could continue to play the sound file.

01:22:15.120 | And worst comes to worst, if we can't debug anything, we can always fall back to

01:22:19.920 | standard programming instead of vibe coding. Okay. So now I would like to switch gears again.

01:22:24.880 | Everything we've talked about so far had to do with interacting with the model via text. So we

01:22:30.720 | type text in and it gives us text back. What I'd like to talk about now is to talk about different

01:22:36.400 | modalities. That means we want to interact with the model via text. So we want to interact with

01:22:41.840 | these models in more native human formats. So I want to speak to it and I want it to speak back

01:22:46.560 | to me and I want to give images or videos to it and vice versa. I want it to generate images and

01:22:52.080 | videos back. So it needs to handle the modalities of speech and audio and also of images and video.

01:22:58.800 | So the first thing I want to cover is how can you very easily just talk to these models?

01:23:05.520 | So I would say roughly in my own news, 50% of the time I type stuff out on the keyboard and 50% of

01:23:12.320 | the time I'm actually too lazy to do that. And I just prefer to speak to the model. And when I'm

01:23:17.440 | on mobile, on my phone, that's even more pronounced. So probably 80% of my queries are just

01:23:24.080 | speech because I'm too lazy to type it out on the phone. Now, on the phone, things are a little bit

01:23:28.880 | easy. So right now the Chatshubpt app looks like this. So I'm going to click on that and I'm going

01:23:33.920 | to type in my email address and I'm going to type in my email address. So I'm going to type in my

01:23:37.680 | email address and I'm going to type in my email address. So I'm going to type in my email address

01:23:42.160 | and I'm going to type in my email address. So I'm going to type in my email address and I'm going

01:23:46.960 | to type in my email address. So I'm going to type in my email address and I'm going to type in my

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01:23:56.240 | and I'm going to type in my email address. So I'm going to type in my email address. So I'm going

01:24:00.960 | to type in my email address and I'm going to type in my email address. So I'm going to type in my

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01:36:02.320 | >> Oh, you mean the whole Trudeau Castro love child theory? It's wild, but I wouldn't put it

01:36:09.120 | past those political dynasties. I mean, they were all buddies back in the day. Makes you wonder if

01:36:14.320 | there's some secret handshake club we're not part of. What's next? Aliens in Area 51?

01:36:19.520 | >> Bro, this is some kind of like Illuminati level stuff.

01:36:23.840 | >> Illuminati, shadow governments, lizard people. It's all connected, my friend. Trudeau, Castro,

01:36:32.560 | they might just be the tip of the iceberg. Next thing you know, we'll find out the queen is

01:36:36.880 | actually a robot. You think I'm joking? >> Okay, let's try the sexy mode. Okay, hello?

01:36:41.520 | >> Hey there, Andre. I can feel the electricity in the air already. Let me take the lead tonight.

01:36:48.720 | Picture this. We're in a dimly lit room, the air thick with anticipation.

01:36:53.200 | >> I see where this is going. >> Me, your eyes locked on mine,

01:36:58.720 | and I slowly start to undress. Each piece of clothing falling.

01:37:03.360 | >> Okay, so I think you get the idea. I think, yeah, I think you get the idea.

01:37:08.800 | Okay, and one more paradigm I wanted to show you of interacting with language models via audio

01:37:14.880 | is this Notebook LM from Google. So when you go to notebooklm.google.com, the way this works is on

01:37:23.920 | the left you have sources, and you can upload any arbitrary data here. So it's raw text or it's web

01:37:29.040 | pages or it's PDF files, et cetera. So I uploaded this PDF about this foundation model for genomic

01:37:34.960 | sequence analysis from ARC Institute. And then once you put this here, this enters the context

01:37:41.600 | window of the model. And then we can, number one, we can chat with that information. So we can ask

01:37:46.560 | questions and get answers. But number two, what's kind of interesting is on the right they have this

01:37:50.320 | deep dive podcast. So there's a generate button. You can press it and wait like a few minutes,

01:37:57.440 | and it will generate a custom podcast on whatever sources of information you put in here.

01:38:01.840 | So for example, here we got about a 30-minute podcast generated for this paper. And it's really

01:38:08.960 | interesting to be able to get podcasts on demand. And I think it's kind of like interesting and

01:38:12.720 | therapeutic. If you're going out for a walk or something like that, I sometimes upload a few

01:38:16.880 | things that I'm kind of passively interested in and I want to get a podcast about. And it's just

01:38:20.800 | something fun to listen to. So let's see what this looks like just very briefly.

01:38:25.760 | OK, so get this. We're diving into AI that understands DNA.

01:38:29.760 | Really fascinating stuff.

01:38:30.960 | Not just reading it, but predicting how changes can impact everything.

01:38:36.160 | Yeah.

01:38:36.400 | From a single protein all the way up to an entire organism.

01:38:39.440 | It's really remarkable. And there's this new biological foundation model called EVO2

01:38:44.240 | that is really at the forefront of all this.

01:38:46.080 | EVO2, OK.

01:38:47.280 | And it's trained on a massive data set called OpenGenome2, which covers over nine--

01:38:53.680 | OK, I think you get the rough idea. So there's a few things here. You can customize the podcast

01:38:58.960 | and what it is about with special instructions. You can then regenerate it. And you can also

01:39:03.440 | enter this thing called interactive mode, where you can actually break in and ask a question

01:39:06.960 | while the podcast is going on, which I think is kind of cool.

01:39:10.240 | So I use this once in a while when there are some documents or topics or papers that I'm not

01:39:15.680 | usually an expert in. And I just kind of have a passive interest in. And I'm going out for a walk

01:39:20.560 | or I'm going out for a long drive. And I want to have a custom podcast on that topic. And so I find

01:39:26.080 | that this is good in niche cases like that, where it's not going to be covered by another podcast

01:39:32.320 | that's actually created by humans. It's kind of like an AI podcast about any arbitrary niche

01:39:37.440 | topic you'd like. So that's Notebook Column. And I wanted to also make a brief pointer to

01:39:44.080 | this podcast that I generated. It's like a season of a podcast called Histories of Mysteries.

01:39:49.200 | And I uploaded this on Spotify. And here I just selected some topics that I'm interested in.

01:39:57.040 | And I generated a deep dive podcast on all of them. And so if you'd like to get a sense of

01:40:01.920 | what this tool is capable of, then this is one way to just get a qualitative sense. Go on this,

01:40:06.640 | find this on Spotify and listen to some of the podcasts here and get a sense of what it can do.

01:40:12.160 | And then play around with some of the documents and sources yourself. So that's the podcast

01:40:17.360 | generation interaction using Notebook Column. Okay, next up, what I want to turn to is images.

01:40:22.560 | So just like audio, it turns out that you can re-represent images in tokens. And we can

01:40:29.920 | represent images as token streams. And we can get language models to model them in the same way as

01:40:35.920 | we've modeled text and audio before. The simplest possible way to do this, as an example, is you can

01:40:41.200 | take an image and you can basically create like a rectangular grid and chop it up into little patches.

01:40:46.480 | And then image is just a sequence of patches. And every one of those patches you quantize.

01:40:51.520 | So you basically come up with a vocabulary of say 100,000 possible patches. And you represent each

01:40:57.200 | patch using just the closest patch in your vocabulary. And so that's what allows you to

01:41:02.720 | take images and represent them as streams of tokens. And then you can put them into context

01:41:06.960 | windows and train your models with them. So what's incredible about this is that the language model,

01:41:11.680 | the transformer neural network itself, it doesn't even know that some of the tokens happen to be

01:41:15.920 | text, some of the tokens happen to be audio, and some of them happen to be images. It just models

01:41:21.280 | statistical patterns of token streams. And then it's only at the encoder and at the decoder that

01:41:26.960 | we secretly know that, okay, images are encoded in this way, and then streams are decoded in this

01:41:32.880 | way back into images or audio. So just like we handled audio, we can chop up images into tokens

01:41:38.720 | and apply all the same modeling techniques and nothing really changes, just the token streams

01:41:43.280 | change and the vocabulary of your tokens changes. So now let me show you some concrete examples of

01:41:48.880 | how I've used this functionality in my own life. Okay, so starting off with the image input,

01:41:53.760 | I want to show you some examples that I've used LLMs where I was uploading images.

01:42:00.320 | So if you go to your favorite ChassisPT or other LLM app, you can upload images usually and ask

01:42:06.240 | questions of them. So here's one example where I was looking at the nutrition label of Brian

01:42:11.040 | Johnson's longevity mix. And basically, I don't really know what all these ingredients are,

01:42:15.120 | right? And I want to know a lot more about them and why they are in the longevity mix.

01:42:18.400 | And this is a very good example where first I want to transcribe this into text. And the reason I like

01:42:24.800 | to first transcribe the relevant information into text is because I want to make sure that the model

01:42:29.760 | is seeing the values correctly. Like I'm not 100% certain that it can see stuff. And so here when it

01:42:36.160 | puts it into a table, I can make sure that it saw it correctly. And then I can ask questions of this

01:42:40.880 | text. And so I like to do it in two steps whenever possible. And then for example, here I asked it to

01:42:46.720 | group the ingredients, and I asked it to basically rank them in how safe probably they are. Because I

01:42:52.800 | want to get a sense of, okay, which of these ingredients are, you know, super basic ingredients

01:42:57.600 | that are found in your multivitamin? And which of them are a bit more kind of like suspicious or

01:43:03.840 | strange or not as well studied or something like that. So the model was very good in helping me

01:43:08.640 | think through basically what's in the longevity mix and what may be missing on like why it's in

01:43:13.520 | there, et cetera. And this is again, a good first draft for my own research afterwards.

01:43:18.080 | The second example I want to show is that of my blood test. So very recently I did like a panel

01:43:24.800 | of my blood test. And what they sent me back was this like 20 page PDF, which is super useless.

01:43:29.840 | What am I supposed to do with that? So obviously I want to know a lot more information. So what I

01:43:33.760 | did here is I uploaded all my results. So first I did the lipid panel as an example,

01:43:40.000 | and I uploaded little screenshots of my lipid panel. And then I made sure that ChachiPT sees

01:43:44.400 | all the correct results. And then it actually gives me an interpretation. And then I kind of

01:43:49.440 | iterated and you can see that the scroll bar here is very low because I uploaded piece by piece all

01:43:53.600 | of my blood test results, which are great by the way. I was very happy with this blood test.

01:44:00.800 | And so what I wanted to say is number one, pay attention to the transcription and make

01:44:05.520 | sure that it's correct. And number two, it is very easy to do this because on MacBook,

01:44:09.920 | for example, you can do control shift command four, and you can draw a window and it copy pastes that

01:44:18.000 | window into a clipboard. And then you can just go to your ChachiPT and you can control V or command

01:44:23.440 | V to paste it in. And you can ask about that. So it's very easy to like take chunks of your screen

01:44:28.160 | and ask questions about them using this technique. And then the other thing I would say about this

01:44:34.400 | is that of course this is medical information and you don't want it to be wrong. I will say that in

01:44:38.320 | the case of blood test results, I feel more confident trusting ChachiPT a bit more because

01:44:43.120 | this is not something esoteric. I do expect there to be like tons and tons of documents about blood

01:44:47.920 | test results. And I do expect that the knowledge of the model is good enough that it kind of

01:44:51.600 | understands these numbers, these ranges, and I can tell it more about myself and all this kind

01:44:55.840 | of stuff. So I do think that it is quite good. But of course you probably want to talk to an

01:45:01.280 | actual doctor as well. But I think this is a really good first draft and something that maybe

01:45:05.200 | gives you things to talk about with your doctor, etc. Another example is I do a lot of math and

01:45:11.280 | code. I found this tricky question in a paper recently. And so I copy pasted this expression

01:45:18.960 | and I asked for it in text because then I can copy this text and I can ask a model what it thinks

01:45:25.760 | the value of x is evaluated at pi or something like that. It's a trick question. You can try it

01:45:30.000 | yourself. Next example, here I had a Colgate toothpaste and I was a little bit suspicious

01:45:35.760 | about all the ingredients in my Colgate toothpaste. And I wanted to know what the hell is all this.

01:45:39.280 | So this is Colgate. What the hell is all these things? So it transcribed it and then it told me

01:45:43.840 | a bit about these ingredients and I thought this was extremely helpful. And then I asked it, okay,

01:45:49.360 | which of these would be considered safest and also potentially less safe? And then I asked it,

01:45:55.120 | okay, if I only care about the actual function of the toothpaste and I don't really care about

01:45:59.440 | other useless things like colors and stuff like that, which of these could we throw out?

01:46:03.120 | And it said that, okay, these are the essential functional ingredients and this is a bunch of

01:46:06.800 | random stuff you probably don't want in your toothpaste. And basically, spoiler alert,

01:46:13.360 | most of the stuff here shouldn't be there. And so it's really upsetting to me that companies

01:46:18.000 | put all this stuff in your food or cosmetics and stuff like that when it really doesn't need to be

01:46:25.440 | there. The last example I wanted to show you is, so this is a meme that I sent to a friend and my

01:46:33.120 | friend was confused like, oh, what is this meme? I don't get it. And I was showing them that Chachi

01:46:38.000 | Petit can help you understand memes. So I copy pasted this meme and asked explain. And basically,

01:46:46.800 | this explains the meme that, okay, multiple crows, a group of crows is called a murder.

01:46:53.440 | And so when this crow gets close to that crow, it's like an attempted murder.

01:46:56.800 | So yeah, Chachi Petit was pretty good at explaining this joke. Okay, now vice versa,

01:47:03.920 | you can get these models to generate images. And the OpenAI offering of this is called DALI,

01:47:09.360 | and we're on the third version. And it can generate really beautiful images on basically

01:47:14.000 | given arbitrary prompts. Is this the Golden Temple in Kyoto, I think? I visited, so this is really

01:47:19.680 | beautiful. And so it can generate really stylistic images and can ask for any arbitrary style of any

01:47:26.080 | arbitrary topic, et cetera. Now, I don't actually personally use this functionality way too often.

01:47:30.960 | So I cooked up a random example just to show you. But as an example, what are the big headlines

01:47:35.440 | news today? There's a bunch of headlines around politics, health, international entertainment,

01:47:41.040 | and so on. And I used search tool for this. And then I said, generate an image that summarizes

01:47:46.160 | today. And so having all of this in the context, we can generate an image like this, that kind of

01:47:51.520 | like summarizes today, just as an example. And the way I use this functionality is usually for

01:48:00.160 | arbitrary content creation. So as an example, when you go to my YouTube channel, then this video,

01:48:05.680 | Let's Reproduce GPT-2, this image over here was generated using a competitor actually to DALI

01:48:12.880 | called ideogram.ai. And the same for this image that's also generated by an AI. And this image

01:48:19.280 | as well was generated, I think, also by ideogram, or this may have been Chachapiti, I'm not sure.

01:48:24.160 | So I use some of the tools interchangeably. So I use it to generate icons and things like that.

01:48:28.880 | And you can just kind of like ask for whatever you want. Now, I will note that the way that this

01:48:34.800 | actually works, the image output, it's not done fully in the model currently with DALI 3. With

01:48:42.720 | DALI 3, this is a separate model that takes text and creates image. And what's actually happening

01:48:48.480 | under the hood here, in the current iteration of Chachapiti, is when I say generate an image that

01:48:53.360 | summarizes today, this will actually, under the hood, create a caption for that image. And that

01:48:59.680 | caption is sent to a separate model that is an image generator model. And so it's kind of like

01:49:04.640 | stitched up in this way. But it's not like super important to, I think, fully understand at this

01:49:09.440 | point. So that is image output. Now, next up, I want to show you an extension where the model

01:49:17.280 | can actually like see video. And this is included in what I showed you previously called advanced

01:49:23.600 | voice. It's not available on the web app, but it's available on the mobile app. So if we go to

01:49:29.680 | advanced voice, and we connect. So hello, I'm demonstrating the video capabilities. And I'm

01:49:35.840 | showing to the camera that there is a little video icon over there. And when you press it,

01:49:40.720 | you can basically see, you can basically show the model different things.

01:49:44.240 | I can see the camera feed. It looks like you've got some black acoustic foam panels on the wall.

01:49:51.200 | Are you testing out how well they reduce echo in the room?

01:49:53.920 | Yeah, I was testing out the acoustic foam. That's right. But it's not actually that helpful.

01:49:58.880 | Sometimes placement can make a big difference. Have you tried covering more corners or

01:50:03.120 | experimenting with different layouts? Okay, well, let's let's look at this. What book is this? Do

01:50:08.000 | you know? Yes, that's Genghis Khan and the making of the modern world by Jack Weatherford. It's a

01:50:14.560 | fascinating book about the impact of Genghis Khan on world history. Yeah, pretty good. Do you see

01:50:19.440 | this other book? What is it? That's surely you're joking, Mr. Feynman by Richard Feynman.

01:50:26.080 | It's a collection of humorous anecdotes from the physicist's life. It's really entertaining.

01:50:30.880 | And what is this? That's an Aeronet 4, a portable CO2 monitor. It measures carbon dioxide levels,

01:50:40.240 | temperature, humidity, and air pressure. It's quite useful for monitoring indoor air quality.

01:50:45.440 | So we're currently at 713, right? Do you think that's good or what?

01:50:50.640 | A CO2 level of 713 ppm is generally okay. It's normal for indoor spaces. Ideally, you want it

01:50:56.880 | below 800 ppm. If it goes above 1000, you might need more ventilation. Got it. And how about this

01:51:02.720 | map? Do you know what this map is? Yes, that looks like a map of Middle Earth from J.R.R. Tolkien's

01:51:10.000 | The Lord of the Rings. It's a great piece for any fan of the series. Yeah, good job. Thank you for

01:51:15.120 | the demonstration.

01:51:16.000 | You're welcome. Glad I could help. If you need anything else, just let me know.

01:51:19.200 | So that's a brief demo. You basically have the camera running, you can point it at stuff,

01:51:25.920 | and you can just talk to the model. It is quite magical, super simple to use. I don't personally

01:51:31.120 | use it in my daily life because I'm kind of like a power user of all the chat GPT apps,

01:51:35.840 | and I don't kind of just like go around pointing at stuff and asking the model for stuff.

01:51:40.560 | I usually have very targeted queries about code and programming, etc. But I think if I was

01:51:44.400 | demonstrating some of this to my parents or my grandparents and have them interact in a very

01:51:48.240 | natural way, this is something that I would probably show them, because they can just point

01:51:53.040 | the camera at things and ask questions. Now, under the hood, I'm not actually 100% sure that they

01:51:57.840 | currently consume the video. I think they actually still just take image sections, like for example,

01:52:05.600 | they take one image per second or something like that. But from your perspective as a user of the

01:52:10.640 | tool, it definitely feels like you can just stream it video and have it make sense. So I think that's

01:52:16.800 | pretty cool as a functionality. And finally, I want to briefly show you that there's a lot of

01:52:21.120 | tools now that can generate videos, and they are incredible, and they're very rapidly evolving.

01:52:25.440 | I'm not going to cover this too extensively because I don't... I think it's relatively

01:52:30.240 | self-explanatory. I don't personally use them that much in my work, but that's just because I'm not

01:52:34.000 | in a kind of a creative profession or something like that. So this is a tweet that compares a

01:52:38.400 | number of AI video generation models as an example. This tweet is from about a month ago,

01:52:43.200 | so this may have evolved since. But I just wanted to show you that all of these models were asked

01:52:50.160 | to generate, I guess, a tiger in a jungle. And this is a tweet from a month ago, so this may

01:52:55.440 | have evolved since. But I just wanted to show you that all of these models were asked to generate,

01:53:00.480 | I guess, a tiger in a jungle. And they're all quite good. I think right now, VO2, I think,

01:53:06.720 | is really near state-of-the-art and really good. Yeah, that's pretty incredible, right?

01:53:14.320 | This is OpenAI Sora,

01:53:22.960 | etc. And you can compare and contrast and use some of these tools that are dedicated to this problem.

01:53:27.600 | Okay, and the final topic I want to turn to is some quality of life features that I think are

01:53:33.760 | quite worth mentioning. So the first one I want to talk about is ChatGPT memory feature.

01:53:38.960 | So say you're talking to ChatGPT, and you say something like, "When roughly do you think we'll

01:53:45.520 | speak Hollywood?" Now, I'm actually surprised that ChatGPT gave me an answer here because

01:53:50.080 | I feel like very often, these models are very averse to actually having any opinions. And they

01:53:54.960 | say something along the lines of, "Oh, I'm just an AI. I'm here to help. I don't have any opinions,"

01:53:58.880 | and stuff like that. So here, actually, it seems to have an opinion and say it says that the last

01:54:04.960 | true peak before franchises took over was 1990s to early 2000s. So I actually happen to really

01:54:10.480 | agree with ChatGPT here. And I really agree, so totally agreed. Now, I'm curious what happens

01:54:19.280 | here. Okay, so nothing happened. So what you can, basically, every single conversation like we

01:54:28.560 | talked about begins with empty token window and goes until the end. The moment I do new conversation

01:54:35.120 | or a new chat, everything gets wiped clean. But ChatGPT does have an ability to save information

01:54:41.200 | from chat to chat, but it has to be invoked. So sometimes ChatGPT will trigger it automatically,

01:54:47.680 | but sometimes you have to ask for it. So basically, say something along the lines of,

01:54:52.080 | "Can you please remember this?" Or like, "Remember my preference," or whatever,

01:55:00.080 | something like that. So what I'm looking for is, I think it's going to work. There we go.

01:55:08.560 | So you see this memory updated, believes that late 1990s and early 2000s was the greatest

01:55:15.280 | peak of Hollywood, et cetera. Yeah. And then it also went on a bit about 1970.

01:55:23.360 | And then it allows you to manage memories. So we'll look into that in a second. But what's

01:55:28.080 | happening here is that ChatGPT wrote a little summary of what it learned about me as a person

01:55:32.800 | and recorded this text in its memory bank. And a memory bank is basically a separate piece of

01:55:40.160 | ChatGPT that is kind of like a database of knowledge about you. And this database of

01:55:45.760 | knowledge is always prepended to all the conversations so that the model has access to

01:55:50.960 | it. And so I actually really like this because every now and then the memory updates whatever

01:55:56.480 | you have conversations with ChatGPT. And if you just let this run and you just use ChatGPT

01:56:00.960 | naturally, then over time, it really gets to know you to some extent. And it will start to make

01:56:06.480 | references to the stuff that's in the memory. And so when this feature was announced, I wasn't 100%

01:56:11.360 | sure if this was going to be helpful or not. But I think I'm definitely coming around. And I've

01:56:16.080 | used this in a bunch of ways. And I definitely feel like ChatGPT is knowing me a little bit

01:56:21.440 | better over time and is being a bit more relevant to me. And it's all happening just by sort of

01:56:27.920 | natural interaction and over time through this memory feature. So sometimes it will trigger it

01:56:33.840 | explicitly. And sometimes you have to ask for it. Okay, now I thought I was going to show you some

01:56:38.400 | of the memories and how to manage them. But actually, I just looked and it's a little too

01:56:41.680 | personal, honestly. So it's just a database, it's a list of little text strings, those text strings,

01:56:47.920 | just make it to the beginning. And you can edit the memories, which I really like. And you can,

01:56:53.280 | you know, add memories, delete memories, manage your memories database. So that's incredible.

01:56:58.480 | I will also mention that I think the memory feature is unique to ChatGPT. I think that

01:57:03.600 | other LLMs currently do not have this feature. And I will also say that, for example, ChatGPT is

01:57:10.000 | very good at movie recommendations. And so I actually think that having this in its memory

01:57:15.040 | will help it create better movie recommendations for me. So that's pretty cool. The next thing I

01:57:19.840 | wanted to briefly show is custom instructions. So you can, to a very large extent, modify your

01:57:25.680 | ChatGPT and how you like it to speak to you. And so I quite appreciate that as well. You can come

01:57:32.000 | to settings, customize ChatGPT. And you see here, it says, what traits should ChatGPT have?

01:57:39.440 | And I just kind of like told it, just don't be like an HR business partner, just talk to me

01:57:43.920 | normally. And also just give me, I just love explanations, educations, insights, et cetera.

01:57:48.800 | So be educational whenever you can. And you can just probably type anything here and you can

01:57:52.560 | experiment with that a little bit. And then I also experimented here with telling it my identity.

01:58:00.000 | I'm just experimenting with this, et cetera. And I'm also learning Korean. And so here I'm kind of

01:58:06.560 | telling it that when it's giving me Korean, it should use this tone of formality. Otherwise,

01:58:11.520 | sometimes, or this is like a good default setting. Because otherwise, sometimes it might give me the

01:58:16.080 | informal or it might give me the way too formal and sort of tone. And I just want this tone by

01:58:21.600 | default. So that's an example of something I added. And so anything you want to modify about

01:58:25.440 | ChatGPT globally between conversations, you would kind of put it here into your custom instructions.

01:58:30.800 | And so I quite welcome this. And this, I think you can do with many other LLMs as well. So look

01:58:36.640 | for it somewhere in the settings. Okay. And the last feature I wanted to cover is custom GPTs,

01:58:41.760 | which I use once in a while. And I like to use them specifically for language learning the most.

01:58:46.720 | So let me give you an example of how I use these. So let me first show you maybe,

01:58:51.040 | they show up on the left here. So let me show you this one, for example, Korean Detailed Translator.

01:58:56.960 | So no, sorry, I want to start with this one, Korean Vocabulary Extractor. So basically,

01:59:04.000 | the idea here is I give it, this is a custom GPT, I give it a sentence, and it extracts vocabulary

01:59:12.400 | in dictionary form. So here, for example, given this sentence, this is the vocabulary. And notice

01:59:18.480 | that it's in the format of Korean, semicolon, English. And this can be copy pasted into Anki

01:59:26.160 | flashcards app. And basically, this kind of, this means that it's very easy to turn a sentence

01:59:34.640 | into flashcards. And now the way this works is basically, if we just go under the hood,

01:59:39.040 | and we go to edit GPT, you can see that you're just kind of like, this is all just done via

01:59:45.920 | prompting. Nothing special is happening here. The important thing here is instructions. So when I

01:59:50.640 | pop this open, I just kind of explain a little bit of, okay, background information, I'm learning

01:59:54.880 | Korean, I'm beginner, instructions. I will give you a piece of text, and I want you to extract

02:00:00.960 | the vocabulary. And then I give it some example output. And basically, I'm being detailed. And

02:00:07.920 | when I give instructions to LLMs, I always like to number one, give it sort of the description,

02:00:13.760 | but then also give it examples. So I like to give concrete examples. And so here are four concrete

02:00:19.360 | examples. And so what I'm doing here really is I'm constructing what's called a few shot prompt.

02:00:23.680 | So I'm not just describing a task, which is kind of like asking for performance in a zero shot

02:00:28.240 | manner, just like do it without examples. I'm giving it a few examples. And this is now a few

02:00:32.640 | shot prompt. And I find that this always increases the accuracy of LLMs. So kind of, that's a,

02:00:37.600 | I think, a general good strategy. And so then when you update and save this LLM, then just given a

02:00:45.840 | single sentence, it does that task. And so notice that there's nothing new and special going on.

02:00:50.960 | All I'm doing is I'm saving myself a little bit of work, because I don't have to basically start

02:00:56.720 | from scratch, and then describe the whole setup in detail. I don't have to tell ChachiPT all of this

02:01:04.800 | each time. And so what this feature really is, is that it's just saving you prompting time.

02:01:10.000 | If there's a certain prompt that you keep reusing, then instead of reusing that prompt and copy

02:01:15.360 | pasting it over and over again, just create a custom Chachi, custom GPT, save that prompt a

02:01:20.640 | single time. And then what's changing per sort of use of it is the different sentence. So if

02:01:26.720 | I give it a sentence, it always performs this task. And so this is helpful if there are certain

02:01:31.920 | prompts or certain tasks that you always reuse. The next example that I think transfers to every

02:01:37.200 | other language would be basic translation. So as an example, I have this sentence in Korean,

02:01:42.800 | and I want to know what it means. Now many people will go to just Google Translate or something

02:01:46.720 | like that. Now famously, Google Translate is not very good with Korean. So a lot of people

02:01:51.440 | use Naver or Papago and so on. So if you put that here, it kind of gives you a translation.

02:01:58.000 | Now, these translations often are okay as a translation. But I don't actually really

02:02:03.120 | understand how this sentence goes to this translation. Like where are the pieces I need

02:02:07.040 | to like I want to know more and I want to be able to ask clarifying questions and so on. And so here

02:02:11.600 | it kind of breaks it up a little bit, but it's just like not as good because a bunch of it gets

02:02:16.480 | omitted, right? And those are usually particles and so on. So I basically built a much better

02:02:20.960 | translator in Chachi, PT, and I think it works significantly better. So I have a Korean detailed

02:02:25.440 | translator. And when I put that same sentence here, I get what I think is a much, much better

02:02:30.320 | translation. So it's three in the afternoon now, and I want to go to my favorite cafe.

02:02:34.560 | And this is how it breaks up. And I can see exactly how all the pieces of it translate

02:02:40.800 | part by part into English. So chigumin, afternoon, etc. So all of this. And what's really beautiful

02:02:48.960 | about this is not only can I see all the little detail of it, but I can ask clarifying questions.

02:02:55.920 | Right here. And we can just follow up and continue the conversation. So this is, I think,

02:02:59.680 | significantly better, significantly better in translation than anything else you can get. And

02:03:04.000 | if you're learning different language, I would not use a different translator other than Chachi,

02:03:08.160 | PT. It understands a ton of nuance. It understands slang. It's extremely good. And I don't know why

02:03:16.240 | translators even exist at this point. And I think GPT is just so much better. Okay. And so the way

02:03:21.600 | this works, if we go to here is if we edit this GPT, just so we can see briefly, then these are

02:03:28.560 | the instructions that I gave it, you'll be giving a sentence a Korean, your task is to translate the

02:03:33.280 | whole sentence into English first, and then break up the entire translation in detail. And so here,

02:03:39.120 | again, I'm creating a few shot prompt. And so here's how I kind of gave it the examples because

02:03:43.360 | they're a bit more extended. So I used kind of like an XML like language, just so that the model

02:03:48.960 | understands that the example one begins here and ends here. And I'm using XML kind of tags.

02:03:54.400 | And so here's the input, I gave it, and here's the desired output. And so I just give it a few

02:04:00.320 | examples, and I kind of like specify them in detail. And then I have a few more instructions

02:04:07.360 | here. I think this is actually very similar to human, how you might teach a human a task,

02:04:12.560 | like you can explain in words what they're supposed to be doing. But it's so much better if

02:04:16.080 | you show them by example, how to perform the task. And humans, I think, can also learn in a few shot

02:04:20.400 | manner significantly more efficiently. And so you can program this in whatever way you like. And

02:04:26.000 | then you get a custom translator that is designed just for you, and is a lot better than what you

02:04:31.280 | would find on the internet. And empirically, I find that ChachiPT is quite good at translation,

02:04:36.480 | especially for like a basic beginner like me right now. Okay, maybe the last one that I'll show you

02:04:41.760 | just because I think it ties a bunch of functionality together is as follows. Sometimes I'm,

02:04:46.480 | for example, watching some Korean content. And here we see we have the subtitles, but the subtitles

02:04:51.600 | are baked into video, into the pixels. So I don't have direct access to the subtitles. And so what I

02:04:57.120 | can do here is I can just screenshot this. And this is a scene between Jinyoung and Seulgi in

02:05:02.560 | Singles Inferno. So I can just take it, and I can paste it here. And then this custom GPT I called

02:05:10.160 | KoreanCAP, first OCRs it, then it translates it, and then it breaks it down. And so basically it

02:05:17.040 | does that. And then I can continue watching, and anytime I need help, I will copy-paste the

02:05:22.240 | screenshot here, and this will basically do that translation. And if we look at it under the hood,

02:05:27.840 | in Edit GPT, you'll see that in the instructions, it just simply gives out, it just breaks down the

02:05:37.440 | instructions. So you'll be given an image crop from a TV show, Singles Inferno, but you can

02:05:41.440 | change this, of course. And it shows a tiny piece of dialogue. So I'm giving the model sort of a

02:05:46.320 | heads up and a context for what's happening. And these are the instructions. So first OCR it,

02:05:51.520 | then translate it, and then break it down. And then you can do whatever format you like.

02:05:56.400 | And you can play with this and improve it, but this is just a simple example,

02:06:00.320 | and this works pretty well. So yeah, these are the kinds of custom GPTs that I've built for myself.

02:06:06.400 | A lot of them have to do with language learning, and the way you create these is you come here,

02:06:11.360 | and you click My GPTs, and you basically create a GPT, and you can configure it arbitrarily here.

02:06:17.840 | And as far as I know, GPTs are fairly unique to ChatGPT, but I think some of the other LLM apps

02:06:24.640 | probably have a similar kind of functionality, so you may want to look for it in the project

02:06:29.680 | settings. Okay, so I could go on and on about covering all the different features that are

02:06:34.000 | available in ChatGPT and so on, but I think this is a good introduction and a good bird's-eye view

02:06:39.440 | of what's available right now, what people are introducing, and what to look out for.

02:06:43.680 | So in summary, there is a rapidly growing, changing, and shifting, and thriving ecosystem

02:06:51.360 | of LLM apps like ChatGPT. ChatGPT is the first and the incumbent, and it's probably the most

02:06:58.000 | feature-rich out of all of them, but all of the other ones are very rapidly growing and becoming

02:07:03.920 | either reaching feature parity or even overcoming ChatGPT in some specific cases. As an example,

02:07:10.080 | ChatGPT now has internet search, but I still go to Perplexity, because Perplexity was doing search

02:07:16.720 | for a while, and I think their models are quite good. Also, if I want to kind of prototype some

02:07:22.400 | simple web apps, and I want to create diagrams and stuff like that, I really like Cloud Artifacts,

02:07:27.280 | which is not a feature of ChatGPT. If I just want to talk to a model, then I think ChatGPT

02:07:33.280 | Advanced Voice is quite nice today, and if it's being too cagey with you, then you can switch to

02:07:38.160 | Grok, things like that. So basically, all the different apps have some strengths and weaknesses,

02:07:42.800 | but I think ChatGPT by far is a very good default and the incumbent and most feature-rich.

02:07:47.600 | Okay, what are some of the things that we are keeping track of when we're thinking about these

02:07:52.240 | apps and between their features? So the first thing to realize, and that we looked at, is you're

02:07:56.880 | talking basically to a zip file. Be aware of what pricing tier you're at, and depending on the

02:08:02.160 | pricing tier, which model you are using. If you are using a model that is very large, that model

02:08:10.080 | is going to have basically a lot of world knowledge, and it's going to be able to answer complex

02:08:15.040 | questions, it's going to have very good writing, it's going to be a lot more creative in its writing,

02:08:19.440 | and so on. If the model is very small, then probably it's not going to be as creative,

02:08:23.840 | it has a lot less world knowledge, and it will make mistakes. For example, it might hallucinate.

02:08:27.840 | On top of that, a lot of people are very interested in these models that are thinking

02:08:35.280 | and trained with reinforcement learning, and this is the latest frontier in research today.

02:08:38.640 | So in particular, we saw that this is very useful and gives additional accuracy in problems like

02:08:44.720 | math, code, and reasoning. So try without reasoning first, and if your model is not

02:08:50.640 | solving that kind of a problem, try to switch to a reasoning model and look for that in the

02:08:55.040 | user interface. On top of that, then we saw that we are rapidly giving the models a lot more tools.

02:09:01.760 | So as an example, we can give them an internet search. So if you're talking about some fresh

02:09:05.280 | information or knowledge that is probably not in the zip file, then you actually want to use an

02:09:09.840 | internet search tool, and not all of these apps have it. In addition, you may want to give it

02:09:15.600 | access to a Python interpreter so that it can write programs. So for example, if you want to

02:09:20.240 | generate figures or plots and show them, you may want to use something like advanced data analysis.

02:09:24.800 | If you're prototyping some kind of a web app, you might want to use artifacts, or if you are

02:09:28.400 | generating diagrams, because it's right there and in line inside the app. Or if you're programming

02:09:33.280 | professionally, you may want to turn to a different app like Cursor and Composer. On top of all this,

02:09:39.600 | there's a layer of multi-modality that is rapidly becoming more mature as well, and that you may

02:09:44.160 | want to keep track of. So we were talking about both the input and the output of all the different

02:09:49.200 | modalities, not just text, but also audio, images, and video. And we talked about the fact that some

02:09:54.560 | of these modalities can be sort of handled natively inside the language model. Sometimes

02:09:59.760 | these models are called omni-models or multi-modal models, so they can be handled natively by the

02:10:04.560 | language model, which is going to be a lot more powerful, or they can be tacked on as a separate

02:10:09.280 | model that communicates with the main model through text or something like that. So that's

02:10:14.160 | a distinction to also sometimes keep track of. And on top of all this, we also talked about quality

02:10:18.640 | of life features. So for example, file uploads, memory features, instructions, GPTs, and all this

02:10:23.600 | kind of stuff. And maybe the last sort of piece that we saw is that all of these apps have usually

02:10:30.320 | a web kind of interface that you can go to on your laptop, or also a mobile app available on

02:10:35.520 | your phone. And we saw that many of these features might be available on the app in the browser, but

02:10:40.560 | not on the phone, and vice versa. So that's also something to keep track of. So all of this is a

02:10:45.120 | little bit of a zoo. It's a little bit crazy, but these are the kinds of features that exist that

02:10:49.360 | you may want to be looking for when you're working across all of these different apps.

02:10:52.640 | And you probably have your own favorite in terms of personality or capability or something like

02:10:57.040 | that, but these are some of the things that you want to be thinking about and looking for and

02:11:01.760 | experimenting with over time. So I think that's a pretty good intro for now. Thank you for watching.

02:11:07.440 | I hope my examples were interesting or helpful to you, and I will see you next time.

How I use LLMs

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