Speaker 1: The last two weeks have been packed with exciting new releases in the open source field. First, we saw the release of Browseruse, which is a groundbreaking tool for automating practically every web-based task, and it's completely open source. It features a user-friendly web interface, and it outshines traditional web automation agents with an impressive 89% in terms of its accuracy on the Web Agent Accuracy Benchmark. It allows you to automate nearly any web-based task, and there's a possibility to even automate desktop tasks with prompts. Not only is it self-correcting, meaning it can dynamically handle errors, but it can also support any large language model, including local models like Llama 3.3 or DeepSeek version 3. Now speaking of DeepSeek, the team made waves this week, and as you saw from my recent videos, they released a new model called the DeepSeek R1, which is an open source large language model that outperforms proprietary models like OpenAI's GPT-4 Omni, as well as Anthropic's Cloud 3.5 SONNET on nearly every benchmark test. It's fully open source, and it has a distilled version which is available for local installation, making it even more accessible for smaller grade computers. Now, it's a major step forward, but now what you can do is that you can use this with the browser use framework. So this way, you can combine its exceptional reasoning capabilities of the model with this new web automation process within browser use. And together, they basically stand out for an amazing open source automation tool that you can get started with today. Just take a look at this example of the DeepSeek R1 with browser use in action. In this case, it's working on tweeting out a post with the DeepSeek tagged. Now, in this case, it is something that will think before it basically works on responding on performing an action. Because this is a reasoning model, which takes the right steps to accurately perform the action. Which means that with the new R1 model with browser use, you're going to get more accurate and detailed responses and execution. Before we get started, I got a huge new update. This is where I've launched a new newsletter. This is something that's going to be sent out on a weekly basis. And it's essentially going to be updating you on the latest AI advancements, comparison of different large language models, AI news, as well as ranking different AI agents. So definitely go ahead and subscribe to this because you don't want to miss out on free AI news. Now, this is just a side note. But what's funny is that OpenAI just released their operator this past week as well, which is a new web-based agent, a part of the OpenAI suite of tools. But the only thing is, is that this is something that costs $200 a month, which is a part of their pro plan subscription. So they have been basically gatekeeping this behind a paywall. Whereas browser use is something that is open source and completely for free. And the great thing with browser use is that it actually shows the entire web page, not just the visible screen that you see right now. And it connects directly to your real browser where there's no login needed. And it can even be integrated into your own application for real automation. So you can see that there's a lot of benefits of using the browser use over something like the operator, which was just something that was released this week. So now let's get started and showcase how you can install this. What you'll need to do first is have the prerequisites fulfilled. You need to make sure that you have Git installed to help you clone the repository locally onto your computer. You'll need Python as a programming language. You'll need UV to set your environment variables. As well as playwright and VS code. Once you have these prerequisites fulfilled, go ahead over to the browser use web UI GitHub repository. Once you're here, scroll all the way to the top, click on the screen button, copy this link to the clipboard. And then you can scroll back down here. And then I want you to open up command prompt. Once you have opened this up, type in git clone and then paste in the link. And in this case, since I have it already installed, I'm going to go over to the web UI directory. But for your case, if you don't have it installed, it's going to start off by cloning the repository within your command prompt. Once that is done, just follow through with the same command, which is cd web UI. Once that is done, you can scroll back down to this section. And what you want to do first is you want to activate your Python environment. So go ahead and set this up. This is going to start your environment. Once it has been created, then go ahead and activate the environment with this following command if you're on Windows. But if you're on Linux or Mac OS, you would want to use the source command. Then you can go ahead and install the package for browser use, which is this pip install browser use. But you want to make sure you do that within the environment. So go ahead and copy this if you're following through with my commands. So in this case, we're going to go ahead and paste this into our command prompt. It's going to go ahead and install all the necessary requirements. And once that is done, we're going to need to install playwright. So once this is finished, we're going to then install playwright. So it looks like it's installed. So we're going to go ahead and then install playwright. Then you can simply go ahead and run the web UI with this Python command. So go ahead and copy this. Go back into your command prompt and paste this in and click enter. This will start this up within your local host with this port. So this will take a couple of seconds, but then you will be able to access the web UI browser use within your browser. And there we go. We have the browser use web UI on our local web browser. So now what we can do is you can configure the agent settings. You can also configure your LLMs configuration within this tab over here. Get the browser settings. You have the ability to run the agent over here. And you can also have it so that it could record your interactions on the web with the agent, as well as getting results over here. So let's go ahead and run an agent. Now, here's the most important part. Now, if you're going to be wanting to use the DeepSeq model, you're not going to be able to find it within the model name tab over here. You're going to need to go over to OLAMA, and you're going to need to then install the DeepSeq model with OLAMA. And then you're going to be able to find the R1 model over here. And this is actually going to be better for you, because then you can have it locally so that you can use it for other sorts of tasks. And you won't even need to pay for any API costs. But there is a way to configure it so that it uses the DeepSeq Reasoner API, but you would need to configure it within the API environment settings. But in this case, I believe it's easier if you just go ahead and install it. And most computers will be able to install the different distilled models that DeepSeq provides for the R1. Just to showcase how you can install the DeepSeq model locally, you can install this with OLAMA. So first things first, you need to make sure you have that installed for your operating system. Once you have that installed, go over to the model card for DeepSeq R1. Once you are here, you can then install based off the different distilled models. So you have it from 1.5 all the way to 671B. Obviously, no one's going to be able to run these two. So you'd want to go for something like 14B as well as 32B, depending on the requirements that are there for this to handle on your computer. And you can obviously search this online to see which one works. But say if you want to install the 14 billion parameter one, you can go ahead, copy this, make sure you have OLAMA running in the back. So go ahead and open up OLAMA. Then go over to your command prompt. And once you have that opened up, go ahead and paste this in and click enter. This will start installing this 14 billion parameter model. But I'm going to go ahead and install the 32B. So I was actually just playing around with it. And in this case, I had the DeepSeq model. Go ahead and search open AI within Google. And this was something that I was capable of doing pretty quickly. It took approximately 20 seconds for this task to be completed. And in this case, it was able to search up open AI. And it was able to also execute this task quite quickly by going ahead and clicking on the open AI link as well. Let's now have the DeepSeq R1 go over and find me the cheapest flights from New York to Moscow. So let's go ahead and run this agent and have it execute this task. I truly love the DeepSeq R1 model. In this case, it was able to use its thinking model to process this query even further. In this case, it was able to use its deep analytics to find the cheapest price for a flight from New York to Russia. And you can see how many steps it took, as well as how many times it thought to itself to execute this task. Let's just see this agent in action. You can see that it is on trip.com. And it's sourcing flights from New York to Moscow, which is in Russia. It's then searching through different dates. That is going to be able to showcase which date is going to be the cheapest. And then it's going to find me the exact flight with the round trip. So let's see this as it goes towards finding the cheapest flight. And you can see right now, it is sorting it through the lowest price to the highest price. And it was able to find something within the range of $1,200. Now, it's truly amazing how it was able to find the cheapest price, which was around $1,200. Now, I have been searching even on trip.com, as well as off of Google Flights. And I haven't been able to find any sort of price that is similar to that range, which was in the $1,200 from New York to Moscow with the same dates, January 25th to 28th, which you saw within that video clip. But essentially, this is the capability that you get with the browser use, as well as having it combined with DeepSeek R1. You're going to have smarter, accurate responses and better generation. If you like this video and would love to support the channel, you can consider donating to my channel through the Super Thanks option below. Or you can consider joining our private Discord, where you can access multiple subscriptions to different AI tools for free on a monthly basis, plus daily AI news and exclusive content, plus a lot more. But that's basically it for today's video on this new integration of the R1 with browser use. I definitely recommend that you try this out with all the links in the description below. It is truly remarkable, and I definitely recommend that you try to attempt to use it so that you could even potentially automate various areas of your workflow. But with that thought, guys, thank you guys so much for watching. I hope you enjoyed today's video. I'll leave all these links in the description below. Make sure you follow me on the Patreon if you haven't already. 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