**A Post on Local Llama Reading a Post on GitHub Executing a Python Script**

I've been working on a project that involves using a local llama to read and execute a Python script from GitHub. The process was quite fascinating, and I'm excited to share my experience with you.

So, I started by posting a message on the local llama's screen, which included a link to a Twitter post about GPT2. The llama then proceeded to read the post, and I could see it processing the information in its brain. It was like watching a tiny computer process data in real-time.

After reading the post, I asked the llama to execute a Python script that was true, and to my surprise, it worked pretty well. The script executed without any issues, and the llama seemed to be learning from the experience.

I then took a look at the image associated with this experiment, which showed the Carpati GPT2 reproduction image. It was fascinating to see how the llama's brain was processing this information and generating responses accordingly.

The next step was to analyze the recall feature of the local llama implementation. I used an open-source code called Rag, which allowed me to embed my history text into the system. The process was straightforward, and I was able to generate new embeddings for my history text using the Python script.

To test the recall feature, I asked the llama to read a post about GPT2 on X but forgot who the author was. The llama then fetched some information from its archive and provided me with the correct answer. It also found a related screenshot of the Carpati GPT2 reproduction image, which was fascinating.

I then tried to alter my query and ask if I had any PNG files about GPT2 from X. According to the archive data, I had one PNG file, which turned out to be the Carpati GPT2 reproduction image. This was a great success, and I was able to see how the local llama's implementation was working.

Overall, I'm very happy with the results of this experiment, and I believe that this technology has a lot of potential for future development. While it may not be suitable for widespread use due to privacy concerns, it was a fascinating prototype that showed what we might be able to achieve locally in the future.

**The Rag Implementation**

For those interested in implementing this system on their own, I used an open-source code called Rag, which allows you to embed your history text into the system. The process is straightforward, and you can find more information about it by following the link in the description.

To use Rag, you'll need to clear your cache first and then run the Python script to generate new embeddings for your history text. This will allow you to start searching through your archive data and retrieving relevant information.

In my case, I used the command `python Reco ragp D- clear cache` to clear my cache and then ran `python Reco ragp save embeddings to Vault embeddings Json` to generate new embeddings for my history text.

This allowed me to ask questions about my documents, including a post about GPT2 on X. According to the archive data, I had read a post about GPT2 by Andre car party on X for Twitter, and also found a related screenshot of the Carpati GPT2 reproduction image.

The final result was that I was able to retrieve all the relevant information from my archive data using the Rag implementation. This was a great success, and I'm excited to see where this technology will go in the future.

**Future Development**

I believe that this technology has a lot of potential for future development. While it may not be suitable for widespread use due to privacy concerns, it's an exciting prototype that shows what we might be able to achieve locally in the future.

One area that I think holds a lot of promise is improving the vision model used by the local llama. If we can develop a better vision model, we'll be able to retrieve more accurate and relevant information from our archive data.

In addition to this, I think there are many other ways to improve the Rag implementation and make it more user-friendly. For example, we could add more features to allow users to search for specific documents or images in their archive data.

Overall, I'm excited to see where this technology will go in the future, and I hope that others will find it interesting and useful.

"WEBVTTKind: captionsLanguage: entoday's project is of course going to be heavily inspired by the kind of controversial Microsoft recall feature so I really want to see if we can create our own version of this my plan was to do this 100% locally but the wish wers just weren't good enough the performance was not stable so I had to kind of abandon that and just make a prototype using GPD 40 because that is kind of the far by the best wish model I have tried so far so yeah it's a bit shame but I wanted it to be locally but uh yeah that could be something for the future so let me just go through kind of how we set this up and how I wanted this to work so I divided this into three phases so we have the record phase we have the analyze phase and to kind of use this we have a rag phase I'm going to explain that but let's just start here on the record phase so basically when we fire up our script this is going to screenshot our computer screen of course and it's going to say those screenshots and bring them to further analyze and put them into a rag system right but I kind of wanted to uh Implement something that kind of monitor pixel changes on our screen because we don't want to spam the same screenshot over and over again so I implemented something that looks at our Monitor and if there's a 5% pixel change to kind of the previous screenshot then it's going to take a new screenshot and save that and bring it over over to the analyze phase I also implemented a step down here that GPT 40 creates a custom screenshot name uh based on kind of the analyze uh feature here from GPT 40 because we want to Archive those screenshots so we can look them up later that is kind of the ID behind the recall feature right if we go to the analyze phas now you can see uh when a screenshot is Sav is going to be analyzed by GT40 so this is going to extract you inter actions what happened in the screen any URLs and of course the name that is associated with the screenshot and this is going to be put in our archive uh but also the screenshot is saved to a new folder so we can look it up if we want to go back in time and see what we did in this exactly Frozen moment in time right uh I think that's kind of how this Microsoft recall feature works so I thought it was pretty interesting and it does work uh so let's just move on to kind of the rag phase this is how we can actually use this system and here you can kind of see we just take the archive that is kind of the user action plus the link to the screenshot uh we have saved in archive and we create embeddings from this uh this is done locally so we are using a local AMA embeddings models and we use llama 3 to kind of search over that rag space right uh so here we can just search for let's say something like uh did I visit Discord yesterday and then you can kind of find the associated screenshot when you did visit let's say Discord what you talked about on Discord because you kind of get the recent action that you talked about on the Discord screenshot and you get the name so you can look up the screenshot and see exactly what you did so that is basically the whole ID behind this uh so now I think we just going to take a quick look at the code kind of how I set this up and let's bring it out in action and see if it actually works but before we do that if you want to learn more about how llms work data analysis science and all that stuff take a look at today's sponsor brilliant.org are you eager to dive into the world of data analysis or understand how large language models work then you're going to love brilliant.org the sponsor of today's video brilliant turns complex subjects into engaging Hands-On experiences that make learning fun and effective I especially like the building regression models course that is perfect for Learners at any level you learn how to visualize massive data sets make better informed decisions from the bias theorum to multiple linear regression another favorite mine is of course the how llms work course this immersive AI Workshop lets you explore the mechanics of large language models showing you how they build vocabulary and generate different outputs like poetry or cover letters freance approach to learning is proven to be six times more effective than traditional lectures by solving real problems you build critical thinking skills and a powerful daily learning Habit to try everything brilliant has to offer for free for 30 days visit brilliant.org allabout aai or just click the link in the description below go start your Learning Journey today a big thanks to brilliant for sponsoring this video okay so now let's walk through some of the most important functions in our code to actually make this work so I just want to start with the analyze screenshot functions because this is kind of the the most important part of course uh you can see we are running the GP 4 all model uh it's just so good like it's by far the best wish model I ever tried uh I just wish there was like an open source model that could be on this level soon so we can make this 100% locally I think how bad cool that would be but now it's like should you I don't even think you should use this because this is sending kind of your uh proprietary data sometimes out with the API and stuff so this is just kind of prototype for something hopefully we can have in the future but it is really fun to play around with and it does work but let's just focus on the code here now for a while so you can see uh so let's let me show you kind of the The Prompt I use here extract the most important information from what is happening in the image include URLs on the website if applicable because I want to be able to kind of extract the URLs I was on let's say I remember something I was on I was on that web page but I don't remember exactly the URL then I can kind of search up that information and find the url I was on so yeah that is kind of the idea behind this it's a pretty simple prompt but it works for my my use case here okay so now we kind of come to the chunking part this is important for the rag right and this is kind of taken from my EC local rag setup so we're going to divide it into chunks of Maximum thousand characters and every yeah how should you say this every action is kind of chunked into the history. text so we can use it in rag to search up so I just wanted to show you an example so you can see here uh here is kind of one action we took from a screenshot so you can see we have the PNG image here that is associated with this uh with this uh saved action so the user was engaged in multiple activities on the computer running a python script executing a python script called recall in power cell terminal and you can see reviewing a directory and we have a directory path working on a canva project so we got a lot of information from this and we can also look up this image here if we go to my folder now you can see I have the archive user activity recall 12 cona AI so here you can see everything that happened in that image so this is kind of associated with this uh action right and we have another uh image here the user was watching a YouTube video called Introducing co- pilot PC by Microsoft and this is our other screenshot so you can see it here so yeah I think it's working pretty good and that is kind of how we set this up uh we also have a GPD for Oat to kind of rewrite this queries so you're an expert extracting information from a text uh we use that uh and here is kind of the compare screenshot function so we take the previous screenshot and compare it with the new one and if kind of the difference in pixels is uh set percentage here uh then we're going to execute on that screenshot because if I just leave my screen on like this there's no pixel changes so we're not going to take a screenshot then only if the user does something else that is kind of the idea behind it and that is one I kind of understood that Microsoft is trying to do as well so I set the diff percentage to five I don't know what the optimal solution is here uh but I set it to five now and seems to be working pretty good so yeah you can see here the GPD 4 oat prompt is kind of we feed in the result from the image description right and then we say from the image description about extract information uh what the user is doing on the computer include a URL if applicable so the reason I kind of run it again with a new uh function here is because I had some results just running it from this uh that was not too good so I tried to run it again over a new function just by feeding in the results and it seemed to work pretty good so uh I kind of left it like this and here is kind of the relevant file name query if we look at the names of our images you can see my file name here is Microsoft co-pilots PC keynote and this file name comes from using the gp4 oat model and Generator short concise relevant file name for the following description this file name is quite important because uh this can be used in our rag phase to get keywords so if we type Microsoft co-pilot this will kind of pop up in our rag search right that is why I want these file names to be relevant and not just some random name and yeah is there anything else to say here about the code it's pretty straightforward uh it kind of ends up at the end here that we are chunking uh everything and putting it into this history text on kind of a new line so it's ready to be embedded in our rag model uh I might do like a follow-up video video on my member section if people are interested in diving deeper into this of course this code is going to be uploaded to the community GitHub so if you want straight access to this just become a member of the channel follow the link in the description and I will invite you to our community GitHub and our community Discord and yeah at the end there if we want to stop this we can just keyboard interrupt and this will exit uh other than that it's just going to run in a true true true Loop until we stop it I also added this 3 second delay before starting because when we fire up our terminal here yeah I just wanted a small sleep here and yeah that is basically it uh to do this um compare screenshots we are using open CV2 and seems to be working pretty good so I think I just want to show you kind of In Action Now how this works and what we can do with this okay so let's run our script now and let's kind of see how this works now so uh let's say I just wanted to start my day at working so I'm just going to fire up this script I'm just going to go to this website here I'm just going to pretend I'm reading this so hopefully now this has taken a screenshot of this right uh so I just want to leave it here now because now you can kind of see it's measuring the different percentage so let's say now we switch to this x poost okay so now we kind of changed our image and then you can see if we go back here the percentage changed to 26% and that means that we took a new screenshot right because the pixels changed and we take a look at this post on X and now we got a second different uh percentage because we brought up the terminal but if we let this just run now we're probably just going to stay on this so now just let's just operate the computer as normal this is going to run in the background right so we can see here we are on kind of the local llama Reddit post we can read a bit about this of course this is not going to take screenshots every single second so it's going to be a bit it's not perfect right we can click onto this GitHub page and hopefully we are collecting screenshots now as we are scrolling but of course it's not going to be perfect so I think we're just going to stop it here now and then we can kind of take a look at the results and how we can implement this into rag R so if we go back now to our history. text we can kind of reload this and we should have some more yeah information here so you can see a carati gpt2 reproduction llmc the users reading a post uh Andre carpon X yes we did that the users reading a post on local llama reading a post on GitHub executing a python script that's true and yeah you can see this worked pretty good let's take a look at the images that is kind of associated with this so let's take a look at the carpati gpt2 reproduction image so we can just go here Cara 2 uh yes so you can see this is the image of the the Twitter post we read right or the X post so yeah we can actually align this with our information and find the screenshot here so I think the next step now is going to be to look at our Rag and how we can embed this and start using our recall feature information so here we are basically using exactly the setup I had on my E Local rag you can find that that's open source you can just follow the link in description to find this code so basically the only difference here is now that instead of uh feeding in our w we call it uh in the previous version we're just feeding in our history. text right and embedding that and we can start searching over it so let's just fire up the terminal here and kind of let's clear our cach first and then we can embed our new history here right so let's just run python Reco ragp D- clear cache so we kind of clear our previous cache okay and then we can generate new embeddings on history. text save embeddings to Vault embeddings Json and now we can start asking question about our yeah documents but it is actually our recall uh information right so let's try to ask I read a post about gpt2 on X but I forgot who the author was okay so we are fetching some information about gpd2 here from our archive right according to your archive data you read a post about Yeah by Andre car party on X for Twitter but reproducing the gp2 Mor okay yeah that's good let's also try to find our um related screenshot do I have any PNG Files about gpt2 okay so you can see according to Archive data you have two PNG files related to gpt2 reproducing gpt2 Gib uh recall diff percentage okay so let's check this reproducing GPT through 124 million 90 minutes so we can go to our archive here and you can see reproducing gpt2 so this is actually from the GitHub so not exactly our X um post but yeah I guess it found something let's try to alter that and add X in maybe let's ask do I have any PNG Files about gpt2 from X according to Archive data you have one PNG file yes gp2 from X okay perfect so this is the car party gpt2 reproduction llmc so let's see if that works and yeah perfect so if I was a bit more specific there I could actually find the post we took the screenshot of on X so yeah I got to say I'm pretty happy how this worked and it is actually kind of how I wanted this to work so this means that you can kind of get like a imagine you get like a big storage here in your history and you get like a ton of different screenshots here this could be helpful if you wanted to kind of track back and see what you did maybe yesterday and stuff like that so I think it's working kind of how I wanted this to work with this rag face implementation so yeah that is basically what I wanted to share with you today like I said uh in the video if you want access to this just follow the link in the description and become a member of the channel I will probably upload this uh code tomorrow uh I might even do a more Deep dive into how this exactly work if people want that uh but yeah I found it interesting it's a shame that we can't do it or that I couldn't actually do it locally uh yet and maybe some of you can list if you actually find a good way to do this locally please leave a comment uh and I want to see it and I want to try it and maybe we can do something together uh but other than that uh I think it worked pretty good I'm very happy how it turned out and it was not that hard to implement and I think we could kind of upgrade this in the future and that's going to be very interesting uh so yeah should you use this probably no because you're sending a lot of private information over the API and yeah I'm not sure uh I don't think I'm going to use this actively but it was a fun prototype to Showcase what we might have uh running locally in the future we just need a bit of a better Vision model and I think everything should work pretty good uh other than that yeah thank you for tuning in and got some cool projects coming up so look out for that and don't forget to check out brilliant.org Link in the description thank you for tuning in and I'll see you again on Sundaytoday's project is of course going to be heavily inspired by the kind of controversial Microsoft recall feature so I really want to see if we can create our own version of this my plan was to do this 100% locally but the wish wers just weren't good enough the performance was not stable so I had to kind of abandon that and just make a prototype using GPD 40 because that is kind of the far by the best wish model I have tried so far so yeah it's a bit shame but I wanted it to be locally but uh yeah that could be something for the future so let me just go through kind of how we set this up and how I wanted this to work so I divided this into three phases so we have the record phase we have the analyze phase and to kind of use this we have a rag phase I'm going to explain that but let's just start here on the record phase so basically when we fire up our script this is going to screenshot our computer screen of course and it's going to say those screenshots and bring them to further analyze and put them into a rag system right but I kind of wanted to uh Implement something that kind of monitor pixel changes on our screen because we don't want to spam the same screenshot over and over again so I implemented something that looks at our Monitor and if there's a 5% pixel change to kind of the previous screenshot then it's going to take a new screenshot and save that and bring it over over to the analyze phase I also implemented a step down here that GPT 40 creates a custom screenshot name uh based on kind of the analyze uh feature here from GPT 40 because we want to Archive those screenshots so we can look them up later that is kind of the ID behind the recall feature right if we go to the analyze phas now you can see uh when a screenshot is Sav is going to be analyzed by GT40 so this is going to extract you inter actions what happened in the screen any URLs and of course the name that is associated with the screenshot and this is going to be put in our archive uh but also the screenshot is saved to a new folder so we can look it up if we want to go back in time and see what we did in this exactly Frozen moment in time right uh I think that's kind of how this Microsoft recall feature works so I thought it was pretty interesting and it does work uh so let's just move on to kind of the rag phase this is how we can actually use this system and here you can kind of see we just take the archive that is kind of the user action plus the link to the screenshot uh we have saved in archive and we create embeddings from this uh this is done locally so we are using a local AMA embeddings models and we use llama 3 to kind of search over that rag space right uh so here we can just search for let's say something like uh did I visit Discord yesterday and then you can kind of find the associated screenshot when you did visit let's say Discord what you talked about on Discord because you kind of get the recent action that you talked about on the Discord screenshot and you get the name so you can look up the screenshot and see exactly what you did so that is basically the whole ID behind this uh so now I think we just going to take a quick look at the code kind of how I set this up and let's bring it out in action and see if it actually works but before we do that if you want to learn more about how llms work data analysis science and all that stuff take a look at today's sponsor brilliant.org are you eager to dive into the world of data analysis or understand how large language models work then you're going to love brilliant.org the sponsor of today's video brilliant turns complex subjects into engaging Hands-On experiences that make learning fun and effective I especially like the building regression models course that is perfect for Learners at any level you learn how to visualize massive data sets make better informed decisions from the bias theorum to multiple linear regression another favorite mine is of course the how llms work course this immersive AI Workshop lets you explore the mechanics of large language models showing you how they build vocabulary and generate different outputs like poetry or cover letters freance approach to learning is proven to be six times more effective than traditional lectures by solving real problems you build critical thinking skills and a powerful daily learning Habit to try everything brilliant has to offer for free for 30 days visit brilliant.org allabout aai or just click the link in the description below go start your Learning Journey today a big thanks to brilliant for sponsoring this video okay so now let's walk through some of the most important functions in our code to actually make this work so I just want to start with the analyze screenshot functions because this is kind of the the most important part of course uh you can see we are running the GP 4 all model uh it's just so good like it's by far the best wish model I ever tried uh I just wish there was like an open source model that could be on this level soon so we can make this 100% locally I think how bad cool that would be but now it's like should you I don't even think you should use this because this is sending kind of your uh proprietary data sometimes out with the API and stuff so this is just kind of prototype for something hopefully we can have in the future but it is really fun to play around with and it does work but let's just focus on the code here now for a while so you can see uh so let's let me show you kind of the The Prompt I use here extract the most important information from what is happening in the image include URLs on the website if applicable because I want to be able to kind of extract the URLs I was on let's say I remember something I was on I was on that web page but I don't remember exactly the URL then I can kind of search up that information and find the url I was on so yeah that is kind of the idea behind this it's a pretty simple prompt but it works for my my use case here okay so now we kind of come to the chunking part this is important for the rag right and this is kind of taken from my EC local rag setup so we're going to divide it into chunks of Maximum thousand characters and every yeah how should you say this every action is kind of chunked into the history. text so we can use it in rag to search up so I just wanted to show you an example so you can see here uh here is kind of one action we took from a screenshot so you can see we have the PNG image here that is associated with this uh with this uh saved action so the user was engaged in multiple activities on the computer running a python script executing a python script called recall in power cell terminal and you can see reviewing a directory and we have a directory path working on a canva project so we got a lot of information from this and we can also look up this image here if we go to my folder now you can see I have the archive user activity recall 12 cona AI so here you can see everything that happened in that image so this is kind of associated with this uh action right and we have another uh image here the user was watching a YouTube video called Introducing co- pilot PC by Microsoft and this is our other screenshot so you can see it here so yeah I think it's working pretty good and that is kind of how we set this up uh we also have a GPD for Oat to kind of rewrite this queries so you're an expert extracting information from a text uh we use that uh and here is kind of the compare screenshot function so we take the previous screenshot and compare it with the new one and if kind of the difference in pixels is uh set percentage here uh then we're going to execute on that screenshot because if I just leave my screen on like this there's no pixel changes so we're not going to take a screenshot then only if the user does something else that is kind of the idea behind it and that is one I kind of understood that Microsoft is trying to do as well so I set the diff percentage to five I don't know what the optimal solution is here uh but I set it to five now and seems to be working pretty good so yeah you can see here the GPD 4 oat prompt is kind of we feed in the result from the image description right and then we say from the image description about extract information uh what the user is doing on the computer include a URL if applicable so the reason I kind of run it again with a new uh function here is because I had some results just running it from this uh that was not too good so I tried to run it again over a new function just by feeding in the results and it seemed to work pretty good so uh I kind of left it like this and here is kind of the relevant file name query if we look at the names of our images you can see my file name here is Microsoft co-pilots PC keynote and this file name comes from using the gp4 oat model and Generator short concise relevant file name for the following description this file name is quite important because uh this can be used in our rag phase to get keywords so if we type Microsoft co-pilot this will kind of pop up in our rag search right that is why I want these file names to be relevant and not just some random name and yeah is there anything else to say here about the code it's pretty straightforward uh it kind of ends up at the end here that we are chunking uh everything and putting it into this history text on kind of a new line so it's ready to be embedded in our rag model uh I might do like a follow-up video video on my member section if people are interested in diving deeper into this of course this code is going to be uploaded to the community GitHub so if you want straight access to this just become a member of the channel follow the link in the description and I will invite you to our community GitHub and our community Discord and yeah at the end there if we want to stop this we can just keyboard interrupt and this will exit uh other than that it's just going to run in a true true true Loop until we stop it I also added this 3 second delay before starting because when we fire up our terminal here yeah I just wanted a small sleep here and yeah that is basically it uh to do this um compare screenshots we are using open CV2 and seems to be working pretty good so I think I just want to show you kind of In Action Now how this works and what we can do with this okay so let's run our script now and let's kind of see how this works now so uh let's say I just wanted to start my day at working so I'm just going to fire up this script I'm just going to go to this website here I'm just going to pretend I'm reading this so hopefully now this has taken a screenshot of this right uh so I just want to leave it here now because now you can kind of see it's measuring the different percentage so let's say now we switch to this x poost okay so now we kind of changed our image and then you can see if we go back here the percentage changed to 26% and that means that we took a new screenshot right because the pixels changed and we take a look at this post on X and now we got a second different uh percentage because we brought up the terminal but if we let this just run now we're probably just going to stay on this so now just let's just operate the computer as normal this is going to run in the background right so we can see here we are on kind of the local llama Reddit post we can read a bit about this of course this is not going to take screenshots every single second so it's going to be a bit it's not perfect right we can click onto this GitHub page and hopefully we are collecting screenshots now as we are scrolling but of course it's not going to be perfect so I think we're just going to stop it here now and then we can kind of take a look at the results and how we can implement this into rag R so if we go back now to our history. text we can kind of reload this and we should have some more yeah information here so you can see a carati gpt2 reproduction llmc the users reading a post uh Andre carpon X yes we did that the users reading a post on local llama reading a post on GitHub executing a python script that's true and yeah you can see this worked pretty good let's take a look at the images that is kind of associated with this so let's take a look at the carpati gpt2 reproduction image so we can just go here Cara 2 uh yes so you can see this is the image of the the Twitter post we read right or the X post so yeah we can actually align this with our information and find the screenshot here so I think the next step now is going to be to look at our Rag and how we can embed this and start using our recall feature information so here we are basically using exactly the setup I had on my E Local rag you can find that that's open source you can just follow the link in description to find this code so basically the only difference here is now that instead of uh feeding in our w we call it uh in the previous version we're just feeding in our history. text right and embedding that and we can start searching over it so let's just fire up the terminal here and kind of let's clear our cach first and then we can embed our new history here right so let's just run python Reco ragp D- clear cache so we kind of clear our previous cache okay and then we can generate new embeddings on history. text save embeddings to Vault embeddings Json and now we can start asking question about our yeah documents but it is actually our recall uh information right so let's try to ask I read a post about gpt2 on X but I forgot who the author was okay so we are fetching some information about gpd2 here from our archive right according to your archive data you read a post about Yeah by Andre car party on X for Twitter but reproducing the gp2 Mor okay yeah that's good let's also try to find our um related screenshot do I have any PNG Files about gpt2 okay so you can see according to Archive data you have two PNG files related to gpt2 reproducing gpt2 Gib uh recall diff percentage okay so let's check this reproducing GPT through 124 million 90 minutes so we can go to our archive here and you can see reproducing gpt2 so this is actually from the GitHub so not exactly our X um post but yeah I guess it found something let's try to alter that and add X in maybe let's ask do I have any PNG Files about gpt2 from X according to Archive data you have one PNG file yes gp2 from X okay perfect so this is the car party gpt2 reproduction llmc so let's see if that works and yeah perfect so if I was a bit more specific there I could actually find the post we took the screenshot of on X so yeah I got to say I'm pretty happy how this worked and it is actually kind of how I wanted this to work so this means that you can kind of get like a imagine you get like a big storage here in your history and you get like a ton of different screenshots here this could be helpful if you wanted to kind of track back and see what you did maybe yesterday and stuff like that so I think it's working kind of how I wanted this to work with this rag face implementation so yeah that is basically what I wanted to share with you today like I said uh in the video if you want access to this just follow the link in the description and become a member of the channel I will probably upload this uh code tomorrow uh I might even do a more Deep dive into how this exactly work if people want that uh but yeah I found it interesting it's a shame that we can't do it or that I couldn't actually do it locally uh yet and maybe some of you can list if you actually find a good way to do this locally please leave a comment uh and I want to see it and I want to try it and maybe we can do something together uh but other than that uh I think it worked pretty good I'm very happy how it turned out and it was not that hard to implement and I think we could kind of upgrade this in the future and that's going to be very interesting uh so yeah should you use this probably no because you're sending a lot of private information over the API and yeah I'm not sure uh I don't think I'm going to use this actively but it was a fun prototype to Showcase what we might have uh running locally in the future we just need a bit of a better Vision model and I think everything should work pretty good uh other than that yeah thank you for tuning in and got some cool projects coming up so look out for that and don't forget to check out brilliant.org Link in the description thank you for tuning in and I'll see you again on Sunday\n"