So please humor me. 所以请幽默一下我。
I've got a couple more admin things before we get into action. 在我们开始行动之前，我还有一些管理方面的事情。
So I wanted to talk about how I've positioned this course and whether it's right for you. 所以我想谈谈我如何定位这门课程以及它是否适合您。
You may be new to coding someone that's never written a line of code before. 您可能是以前从未写过一行代码的人的编码新手。
You might be someone that's already considers yourself an agent engineer. 您可能已经认为自己是代理工程师。
Those are the two extremes. 这是两个极端。
Or you might be in the middle. 或者你可能处于中间。
You're already a Python coder or you are an AI engineer. 您已经是一名 Python 程序员或者您是一名人工智能工程师。
Maybe you're even someone that's taken my LLM engineering course, in which case, thank you and welcome. 也许您甚至参加了我的法学硕士工程课程，在这种情况下，谢谢您并欢迎您。
But I'm here to tell you that this course is designed to appeal to everyone across that entire continuum. 但我在这里告诉您，本课程旨在吸引整个连续体中的每个人。
There is something here for everyone. 这里适合每个人。
The people in the yellow boxes in the middle. 中间黄色方框里的人。
You're going to have the best time with this. 你将会度过最美好的时光。
It's most well positioned for you, that is for sure. 毫无疑问，它最适合您。
That goes without saying. 那不用说了。
For the people that are new to coding, it's going to be challenging. 对于刚接触编码的人来说，这将是一个挑战。
There's no doubt you're going to have to have a lot of patience, but this course is okay. 毫无疑问，您需要有很大的耐心，但是这门课程还可以。
You'll be able to get there and build amazing things. 你将能够到达那里并建造出令人惊奇的东西。
For someone who's already an agent engineer that's built some agent platforms before. 对于已经是代理工程师并且之前构建过一些代理平台的人来说。
You may want to speed through some of this, but I've made sure that there's advanced material covered 您可能想快速浏览其中的一些内容，但我已确保涵盖了高级材料
too. 也。
We're going to be doing some really interesting things at every point and building some great projects 我们将在每个点上做一些非常有趣的事情并构建一些伟大的项目
too. 也。
So I'm here to say, if you're new to coding, take some time with the guides that I've written. 所以我在这里想说的是，如果您是编码新手，请花一些时间阅读我编写的指南。
You'll find them in the guides folder when we install the repo. 当我们安装存储库时，您将在指南文件夹中找到它们。
And I've used that as a way to build up your basic, your foundational expertise so that you can hit 我用它来建立你的基本专业知识，这样你就可以达到
the ground running for Python coders and particularly for AI engineers. Python 程序员，尤其是人工智能工程师的基础。
If you've taken the LLM engineering course, it's perfect. 如果您修读了 LLM 工程课程，那就完美了。
Then you're going to have a great time. 那么你将会度过一段美好的时光。
And for agent engineer, focus on the projects. 对于代理工程师来说，专注于项目。
That's when we really put things into action. 那是我们真正付诸行动的时候。
I'm really proud of the projects we've built. 我对我们建造的项目感到非常自豪。
I think you're going to enjoy them, even if you've got some experience with this already. 我认为您会喜欢它们，即使您已经有一些经验。
And for anyone that hasn't taken the LM engineering course, I'll be sure to put links to it in the 对于任何没有参加过 LM 工程课程的人，我一定会在
course resources. 课程资源。
And it's something that I really do see that as complementary, and it's not necessarily a prerequisite 我确实认为这是互补的，而且不一定是先决条件
to this course. 到这门课程。
You can come into this course without having taken it for sure, but if you have taken it, you will 您可以在没有参加过该课程的情况下参加本课程，但如果您已经参加过该课程，您将会
be really well equipped for this course because it builds up a lot of depth of expertise in terms of 为这门课程做好充分的准备，因为它在以下方面积累了很多深度的专业知识
what it means to work to to choose and apply LMS to solve problems. 选择并应用 LMS 来解决问题意味着什么？
So in just a moment, we're going to be starting to code. 稍后我们将开始编码。
I know you're antsy. 我知道你很烦躁。
I know you're waiting for this. 我知道你在等这个。
Can't he stop talking and get to it? 他就不能停止说话并开始做事吗？
We will be. 我们会的。
But I do want to tell you that the first thing we're going to have to do is set up your environment 但我确实想告诉你，我们要做的第一件事就是设置你的环境
and setting up an environment, a big data science environment at the frontier of what's possible. 并建立一个环境，一个处于可能前沿的大数据科学环境。
It can be a bit of a painful process. 这可能是一个有点痛苦的过程。
I'm here to help. 我是来帮忙的。
I'm here to make it go smoothly. 我来这里是为了让事情顺利进行。
But please do have something of a thick skin for knowing that you may hit some challenges. 但请厚脸皮，因为你知道你可能会遇到一些挑战。
We'll figure them out. 我们会解决它们的。
We'll get them fixed. 我们会把它们修好。
We'll get you up and running the. 我们将帮助您启动并运行。
This time, though, we're going to be helped out by a couple of really great friends in the form of 不过，这一次，我们将得到几位非常好的朋友的帮助，他们是
the platform cursor, which we'll be using as our IDE. 平台光标，我们将使用它作为我们的 IDE。
And I just love cursor. 我就是喜欢光标。
Cursor, of course, is the AI platform that's powered by Llms that allows us to be so productive, 当然，Cursor 是由 Llms 提供支持的人工智能平台，它使我们能够如此高效，
and it's built on VSCode and and it's just great. 它是基于 VSCode 构建的，非常棒。
And so it's going to be a lot of fun working with that. 因此，与此合作将会非常有趣。
But perhaps more specifically for building the environment, we're going to be using this product called 但也许更具体地说，为了构建环境，我们将使用这个名为
UV, which is you can think of as something which is replacing Anaconda that I used in the LLM engineering UV，你可以认为它正在取代我在 LLM 工程中使用的 Anaconda
course. 课程。
And it's a sort of built on top of, of using virtual environments. 它是一种建立在虚拟环境之上的技术。
It's really fast and it's really simple and it just works. 它非常快，非常简单，而且很有效。
I am such a fan of UV. 我非常喜欢紫外线。
It's actually a student on the LLM engineering course that that first drew my attention to it and said, 实际上是一名法学硕士工程课程的学生首先引起了我的注意，他说：
this is what we should be using. 这就是我们应该使用的。
And and it was too late for LM engineering. 对于 LM 工程来说已经太晚了。
But it's not too late for this course. 但对于这门课程来说还不算太晚。
And it turns out that UV has become so popular that most of the agent frameworks that we'll be looking 事实证明，UV 已经变得如此流行，以至于我们将要寻找的大多数代理框架
at already have UV at their core. 他们已经以紫外线为核心。
Crew is, in a way, crew is kind of like built with UV, as you'll see. 从某种程度上来说，Crew 有点像用 UV 建造的，正如你所看到的。
So UV is really easy to use. 所以UV真的很容易使用。
It is nothing like Anaconda for people that have done this before. 对于以前这样做过的人来说，它与 Anaconda 完全不同。
And it's it's basically very, very similar to using virtual AMS and you are going to love it. 它基本上与使用虚拟 AMS 非常非常相似，您一定会喜欢它的。
So I know it's always annoying to have to bring in something new to the mix, but it will be worth it. 所以我知道必须引入新的东西总是很烦人，但这是值得的。
You're going to thank me for this. 你会为此感谢我的。
You're gonna you're gonna love UV and it's written in rust and everyone loves things that are written 你会喜欢 UV 的，它是用铁锈写成的，每个人都喜欢写出来的东西
in rust. 生锈了。
So there you go. 所以就这样吧。
Get ready for that. 做好准备吧。
There's actually one more difficult conversation that we have to have, and it's about APIs. 实际上，我们还必须进行一场更困难的对话，它是关于 API 的。
So look, this course does involve making calls to frontier models like OpenAI and Deep Seek. 所以看，本课程确实涉及调用 OpenAI 和 Deep Seek 等前沿模型。
And they come at a price. 它们是有代价的。
There is a price point there. 那里有一个价格点。
And I want to make the point that it is completely up to you. 我想强调的是，这完全取决于你。
You can choose. 你可以选择。
I use both open AI and deep seq, but you can choose to only use deep seq throughout for a much lower 我同时使用开放 AI 和深度 seq，但您可以选择仅使用深度 seq，从而获得更低的成本
cost. 成本。
And Gemini you can also use. 双子座你也可以使用。
And right now Gemini has a free tier. 现在 Gemini 有免费套餐。
I don't know how long that will last, but so you can use Gemini for free for most of the course. 我不知道这会持续多久，但你可以在课程的大部分时间里免费使用 Gemini。
You can also use Alama completely free just running open source models locally on your computer. 您还可以完全免费地使用 Alama，只需在计算机上本地运行开源模型即可。
No cost at all. 完全没有成本。
But there are some trade offs here. 但这里需要一些权衡。
If you want to use any kind of good model, it will take a very long time. 如果你想使用任何一种好的模型，都需要很长的时间。
If you use the quick models like llama 3.2, then it might have troubles being coherent, and you certainly 如果您使用像 llama 3.2 这样的快速模型，那么它可能会在连贯性方面遇到麻烦，而且您当然
won't get to see the kinds of results that I'll be getting when I'm using the frontier models, but 当我使用前沿模型时，我不会看到我会得到的结果，但是
that allows you to have a sort of balance. 这可以让你获得某种平衡。
You can run things locally for free and see one set of results, and you can watch me do it with the 您可以免费在本地运行并查看一组结果，并且您可以观看我使用
frontier models. 前沿模型。
If you don't want to spend, the API charges yourself. 如果您不想花钱，API 会自行收费。
So what are these API charges? 那么这些 API 费用是多少呢？
Well, different countries might have different pricing. 嗯，不同的国家可能有不同的定价。
What I can tell you is that for me, running this entire course, I spent well under $5 on any of the 我可以告诉你的是，对于我来说，运行整个课程，我在任何一个项目上花费的钱都低于 5 美元。
LLM model costs. LLM模型费用。
It's very cheap. 非常便宜。
OpenAI the first time you use it, you need to put down at least $5 upfront. 第一次使用 OpenAI 时，您需要至少预付 5 美元。
And I kind of pay as you go that you spend against. 我是按你所花的钱来支付的。
So that's annoying. 所以这很烦人。
So you might have to do that if you haven't done it before. 因此，如果您以前没有这样做过，您可能必须这样做。
But other than that, the actual amount that we'll spend will be relatively small, a matter of a few 但除此之外，我们实际花的钱会比较少，也就几个而已
dollars. 美元。
And it's very hard to spend very much on deep sea because it's so cheap. 而且很难在深海上花很多钱，因为它太便宜了。
Now, I will say that in the last week I have an option to use a proper real time market data provider 现在，我要说的是，在上周我可以选择使用适当的实时市场数据提供商
that I use, and that cost me $20. 我用过，花了我 20 美元。
But that's not necessary. 但这是没有必要的。
There are free options as well, but if you get into it like I do then, then you might do that. 也有免费的选择，但如果你像我一样进入它，那么你可能会这样做。
And you know, I just want to make the point at the end. 你知道，我只是想在最后强调这一点。
People do have like like people get frustrated about API costs. 人们确实对 API 成本感到沮丧。
There's something about them that are a bit galling, because it feels like everyone is sort of nickel 他们身上有一些让人有点恼火的东西，因为感觉每个人都是镍币
and diming you. 并调暗你。
You're getting charged a little bit here, a little bit there, another API cost. 您会在这里收取一点费用，那里收取一点费用，还有另一项 API 费用。
And I understand that even though I say these things are very cheap, a few dollars, it adds up. 而且我明白，尽管我说这些东西很便宜，几美元，但加起来就多了。
And that's not always cheap. 这并不总是便宜的。
And particularly with exchange rates and different economies, that might be quite a burden. 尤其是在汇率和不同经济体的情况下，这可能是一个相当大的负担。
But I do want to make the point that you have to keep in mind what's going on. 但我确实想强调一点，你必须记住正在发生的事情。
When we run inference on these large llms, there are trillions of floating point calculations going 当我们对这些大型 llms 进行推理时，会进行数万亿次浮点计算
on. 在。
This is heavy machinery. 这是重型机械。
And so it's not like these companies are just just making extra cash off you. 所以这些公司并不只是从你身上赚取额外的现金。
I'm sure they are quite profitable, but but most of it, the margins are relatively slim because there's 我确信他们的利润相当可观，但大多数情况下，利润相对较小，因为
a lot of compute costs associated with running these massive models. 与运行这些大型模型相关的大量计算成本。
And if you think of the price of buying a new laptop or even a big like a gamer box that could actually 如果您考虑购买一台新笔记本电脑甚至像游戏盒这样的大设备的价格，实际上可能会
run larger models itself, you'd be talking about thousands of dollars. 运行更大的模型本身，你会谈论数千美元。
So this kind of API pricing, while it can feel, as I say, a little bit galling sometimes having to 因此，正如我所说，这种 API 定价虽然有时让人感觉有点令人难堪
pay each of these different APIs. 为这些不同的 API 支付费用。
It is worth keeping in mind the context of what's going on behind the scenes. 值得记住幕后发生的事情的背景。
These companies need to pay their electricity bills. 这些公司需要支付电费。
There's a lot of compute, and we're getting real value from what we use. 有大量的计算，我们正在从我们使用的东西中获得真正的价值。
So I don't know if that helps or not, but it gives you some context. 所以我不知道这是否有帮助，但它为您提供了一些背景信息。
Please do keep in mind you don't need to spend anything on APIs if you don't want to. 请记住，如果您不愿意，则无​​需在 API 上花费任何费用。
So there are three things for you to remember. 因此，您需要记住三件事。
The first of them is that there are resources that accompany this course, and they're really great. 第一个是本课程附带的资源，而且它们真的很棒。
There's a website where I've put links, and I've got extra content in the form of YouTube videos that 我在一个网站上放置了链接，并且以 YouTube 视频的形式提供了额外的内容
should should be helpful. 应该会有帮助。
There's in GitHub, there's a whole section with different guides to teach different kinds of skills 在 GitHub 上，有一整个部分包含不同的指南来教授不同类型的技能
that you might need. 你可能需要的。
And there's some troubleshooting guides in there too that will fix problems for you and the labs. 那里还有一些故障排除指南，可以为您和实验室解决问题。
So the labs I'm updating them all the time, the labs you can think of them as like an ongoing a fluid 所以我一直在更新它们的实验室，你可以将它们视为持续流动的流体
resource. 资源。
Every time you do a git pull to get the latest, you'll get refreshed with new content. 每次执行 git pull 来获取最新内容时，您都会因新内容而焕然一新。
Maybe not every time, but most times I'm trying to keep them to be a living, breathing resource for 也许不是每次，但大多数时候我都在努力让它们成为一个有生命力、有呼吸的资源。
you. 你。
Then remember to stay patient. 然后记得保持耐心。
Remember that if you hit problems, if you hit roadblocks, that's actually a great learning opportunity. 请记住，如果您遇到问题、遇到障碍，这实际上是一个很好的学习机会。
Because figuring out what's going on, figuring out how to solve this is one of the best ways to learn 因为弄清楚发生了什么，弄清楚如何解决这个问题是最好的学习方法之一
and try and go with it. 并尝试去接受它。
Try and enjoy it as much as you can. 尝试并尽可能地享受它。
There's some juicy projects and figuring out why they don't work first time. 有一些有趣的项目，并弄清楚为什么它们第一次不起作用。
That's part of the fun. 这是乐趣的一部分。
And then the third point is that if all else fails, or even if nothing else fails, then contact me. 然后第三点是，如果其他一切都失败了，或者即使其他都没有失败，请联系我。
I'm here. 我在这儿。
I love this stuff. 我喜欢这个东西。
I'm actually super responsive. 我实际上反应超级灵敏。
If you email me or if you LinkedIn with me, then I'll respond very quickly. 如果您给我发电子邮件或通过 LinkedIn 联系我，我会很快回复。
Unless I'm in a meeting or I'm traveling or something, or I'm asleep. 除非我正在开会、旅行或者其他什么事情，或者我睡着了。
For people in different time zones, I do tend to reply really quickly. 对于不同时区的人，我确实倾向于很快回复。
People are always surprised. 人们总是感到惊讶。
In fact, a lot of people think that I'm I'm an AI agent. 事实上，很多人认为我是一个AI特工。
When I reply, they're like, do you have an agent of yourself? 当我回复时，他们会问，你有自己的经纪人吗？
But no, it's the real me, I will reply, I love getting questions. 但不，这是真实的我，我会回答，我喜欢收到问题。
I love answering so, so do feel free to reach out. 我喜欢回答这个问题，所以请随时与我们联系。
Be in touch. 保持联系。
I welcome ideas and thoughts and questions and whatever you got. 我欢迎您提出想法、想法和问题以及任何您能想到的东西。
And by all means, if you if you're up for it, then LinkedIn with me I some people uh, like to uh, 无论如何，如果你愿意的话，那么 LinkedIn 和我一起 我有些人呃，喜欢呃，
don't feel comfortable doing that for some reason, but I'm very open to it. 由于某种原因，我觉得这样做不太舒服，但我对此很开放。
I love building a community of people in data science and that can support each other. 我喜欢建立一个数据科学领域的社区，并且可以互相支持。
That can if people are looking for for to hire someone or if they're looking for jobs. 如果人们正在寻找雇用某人或正在寻找工作，则可以这样做。
And I can help connect people. 我可以帮助人们建立联系。
So it's a really great way for me to build and contribute to the community. 所以这对我来说是一个构建社区并为社区做出贡献的好方法。
And this is really important. 这非常重要。
But one of the things that people did from my last course is they would post projects that they've done 但人们在我上一门课程中所做的一件事是他们会发布他们已经完成的项目
and tag me on them, or things that they've done from the course, and I can then amplify it, because 并在他们身上标记我，或者他们在课程中所做的事情，然后我可以放大它，因为
I can then jump in with some comments and, and make some observations or say how great it is, they've 然后我可以加入一些评论，并提出一些观察或说它有多棒，他们已经
done something, and that will then be available to all the other students from the course. 完成某件事，然后该课程的所有其他学生都可以使用该内容。
You can weigh in as well. 你也可以权衡一下。
And this has really happened, and it allows you to share things and amplify them. 这确实发生了，它让你可以分享事物并放大它们。
And LinkedIn is a great place to do that. LinkedIn 是实现这一目标的好地方。
And it will be something that that will be seen, perhaps by future clients of yours or perhaps by future 这将是你未来的客户或未来的客户将会看到的东西
employers of yours as well. 还有你的雇主。
So it's a really good thing to do. 所以这确实是一件好事。
So I strongly encourage it. 所以我强烈鼓励这样做。
LinkedIn with me and share on LinkedIn. 与我联系 LinkedIn 并在 LinkedIn 上分享。
It's a great resource and I can't wait to see what you're doing. 这是一个很棒的资源，我迫不及待地想看看你在做什么。
So at this point, you've put up with half an hour of me yammering away. 所以现在你已经忍受了我半个小时的喋喋不休了。
Finally, it's time for action. 最后，是时候采取行动了。
We're going to the lab. 我们要去实验室。
We're going to set up your environment. 我们将设置您的环境。
The next video is for PC people. 下一个视频是针对电脑用户的。
We're going to have a video for PC setup, and then the video after that is for Mac people and Linux. 我们将有一个针对 PC 设置的视频，然后是针对 Mac 用户和 Linux 的视频。
You should probably join in with the Mac people. 您也许应该加入 Mac 人员的行列。
It'll be similar enough for you. 它对你来说足够相似了。
And then we will reconvene on the video after that. 之后我们将重新讨论视频。
So it's going to be PC, followed by Mac, followed by us all back together again with fully built environments. 因此，将是 PC，然后是 Mac，最后我们将通过完全构建的环境再次聚集在一起。
Let's do it. 我们开始做吧。