OpenAI-o1 x Cursor | Use Cases - XML Prompting - AI Coding ++

Summary

In this video, the creator explores the use of OpenAI's GPT-3.5 and GPT-4 models on the coding platform, Cursor. They test different Cursor rules to enhance productivity and experiment with structured prompts using XML tags for clearer instructions. The video compares the efficiency of GPT-3.5 and GPT-4 for coding tasks, highlighting GPT-3.5's speed and reliability. The creator also discusses the benefits of GPT-4's larger output limit for complex refactoring jobs. They demonstrate using these models to create folder structures and automate website updates, showcasing practical applications and workflow improvements.

Takeaways

• 😀 The video explores the use of OpenAI's GPT-3.5 and GPT-4 models, focusing on the 'mini' version for coding within the Cursor platform.
• 🔧 The creator has been testing Cursor's AI rules to enhance productivity and is experimenting with XML tags to structure prompts more effectively.
• 📊 A Reddit post is discussed, comparing GPT-3.5 to GPT-4, with the conclusion that GPT-3.5 is currently superior for daily tasks due to its speed and reliability.
• 📝 The video highlights the 64k output token limit of GPT-4 as a significant advantage for large refactoring jobs, allowing more extensive code generation in fewer prompts.
• 💬 The necessity for precise prompts with GPT-4 is emphasized due to its longer 'thinking time', suggesting that getting the prompt right the first time is crucial.
• 🛠️ Cursor's implementation of GPT-4 is noted to have some bugs, such as occasional failures to output text along with code.
• 📁 The video demonstrates using structured prompts with XML tags to generate complex folder structures and code, leveraging GPT-4's capabilities.
• 🔄 The creator shares a workflow of switching between GPT-4 for initial setup and GPT-3.5 for debugging and fine-tuning.
• 🎥 A practical example is given where GPT-4 is used to automate the process of updating video content on the creator's website, showcasing a real-world application.
• 🔗 The video concludes with a call to action for viewers to experiment with the models, share their findings, and consider integrating these techniques into their own workflows.

Q & A

What is the main focus of the video?

The main focus of the video is to explore the use of OpenAI's GPT-3.5 and GPT-4 models, particularly the 'mini' version, for coding tasks using the Cursor platform. The video also discusses the effectiveness of different models for productivity and coding efficiency.

What is Cursor and how does it relate to the video's content?

Cursor is a code editor that the video uses to test different coding rules and to integrate with OpenAI models for coding assistance. The video explores using Cursor to rewrite prompts with XML tags for more precise instructions when using AI models.

Why does the video mention XML tags in the context of coding?

The video mentions XML tags as a method to structure and clarify prompts for AI models. By using XML tags, the video aims to provide more precise instructions to the AI, which can lead to better and more efficient code generation.

What are the advantages of using GPT-4 'mini' mentioned in the video?

The video highlights the 64k output tokens of GPT-4 'mini' as an advantage, allowing for larger refactoring jobs to be done in fewer prompts due to the increased output limit compared to GPT-3.5.

What is the 'Chain of Thought' mentioned in the video in relation to AI models?

The 'Chain of Thought' refers to the process where AI models think through steps to solve a problem, which can be time-consuming. The video suggests that with GPT-4 'mini', users need to provide more specific prompts to minimize waiting time for the AI's thought process.

What is the verdict on GPT-3.5 vs. GPT-4 'mini' in the video?

The video concludes that for day-to-day coding tasks, GPT-3.5 is still considered better due to its speed and reliability, while GPT-4 'mini' is more suitable for large refactoring jobs that can benefit from its 64k output token limit.

How does the video suggest improving prompts for AI models?

The video suggests improving prompts by using more structured language with XML tags to provide clear and detailed instructions to the AI, which can help in getting more accurate and efficient code generation.

What is the significance of the 64k output tokens in GPT-4 'mini' as discussed in the video?

The 64k output tokens in GPT-4 'mini' allow for the generation of more extensive code in a single prompt, which is beneficial for creating large-scale project structures or refactoring without needing to split the task into multiple prompts.

How does the video demonstrate the use of GPT-4 'mini' for creating folder structures?

The video demonstrates using GPT-4 'mini' to create complex folder structures by providing a detailed prompt wrapped in XML tags. It then uses the large output token capacity to generate all the necessary files and folders in one go.

What is the practical application shown in the video for automating website updates?

The video shows a practical application where GPT-4 'mini' is used to create a script that can update a website's video section by generating a description based on a video title and URL, streamlining the process of adding new content to the site.

Outlines

00:00

🤖 Exploring OpenAI Models for Productivity

The speaker begins by discussing their exploration of OpenAI models, focusing on the 'mini' version. They mention testing different cursor rules to enhance productivity and experimenting with structured prompts using XML tags. The speaker also shares their weekend endeavor of seeking opinions on using GPT-3.5 for coding and reflects on the findings from a Reddit post, comparing GPT-3.5's speed and reliability with the newer models. They conclude that GPT-3.5 is still superior for daily tasks due to its consistency but remain open to exploring the 'mini' version's potential.

05:00

💻 Leveraging Cursor and XML Tags for Coding Efficiency

In this segment, the speaker dives into their experience with using Cursor, an AI assistant, to improve coding efficiency. They discuss setting up rules within Cursor to suggest XML tags for clearer prompts. The speaker demonstrates how they've been using Cursor to rewrite prompts with XML tags for better clarity. They also show a practical example of creating a terminal app to fetch and display Hacker News posts, explaining the process of using structured prompts with OpenAI's 'mini' model and then switching to GPT-3.5 for debugging.

10:01

📁 Automating Folder Structure Creation with OpenAI

The speaker illustrates how they've been using OpenAI's 'mini' model to automate the creation of complex folder structures. They provide an example of a large project setup and demonstrate how to use the 'mini' model with its 64k output token limit to generate an entire project structure in one go. The speaker emphasizes the time-saving benefits of this approach and how it streamlines the initial setup of projects.

15:03

🛠️ Building a Video Update Pipeline for a Personal Website

In this part, the speaker shares their project of creating a pipeline to update their website with new videos. They outline the steps to automate the process of adding a video URL and title to generate a description and update their website's video section. The speaker uses a combination of OpenAI's 'mini' model for the initial setup and GPT-3.5 for fine-tuning. They successfully demonstrate the pipeline's efficiency and express their satisfaction with the time saved.

20:04

🔧 Final Thoughts on Integrating OpenAI Models into Workflow

The speaker concludes by reflecting on the integration of OpenAI models into their workflow. They express excitement about the potential of the 'mini' model for large tasks and the continued preference for GPT-3.5 for daily tasks. The speaker invites viewers to share their experiences and discoveries with OpenAI models and encourages experimentation. They also mention plans to continue exploring and possibly share their Cursor rules and prompts with the community.

Keywords

💡OpenAI

OpenAI refers to a company that specializes in creating artificial intelligence technologies. In the context of the video, the presenter is experimenting with OpenAI's AI models, specifically the 'mini' version, to explore their capabilities in coding and productivity. The video discusses the use of these AI models within the 'cursor' environment, highlighting their potential for improving coding efficiency.

💡Cursor

Cursor is a code editor that the presenter is using in the video. It is designed to work with AI models to assist in coding tasks. The video discusses how the presenter has been testing different 'cursor rules' to enhance productivity and has been leveraging Cursor to rewrite prompts into XML tags for more precise instructions from the AI.

💡XML tags

XML tags are used in the video to describe a structured format for writing prompts. The presenter is experimenting with using XML tags to provide clearer instructions to the AI model, which is intended to improve the precision of the AI's responses. This method is showcased as a way to 'up the prompting game' and to leverage the AI's capabilities more effectively.

💡Productivity

Productivity is a central theme in the video, as the presenter is exploring ways to enhance their coding efficiency through the use of AI models. The video discusses how the presenter has been testing different AI models and techniques, such as using XML tags and Cursor rules, to streamline coding processes and improve overall productivity.

💡Prompting game

The 'prompting game' refers to the strategy of crafting effective prompts for AI models to elicit the most accurate and useful responses. The video focuses on improving this skill by using structured prompts with XML tags, aiming to get more precise instructions from the AI and thus improve the AI's performance in coding tasks.

💡Output tokens

Output tokens refer to the maximum number of tokens an AI model can generate in a single response. The video mentions the 64k output tokens of the 'mini' version of OpenAI's model, which is significantly higher than the 8K output tokens of the 3.5 version. This larger capacity allows for more extensive output, such as large refactoring jobs, in a single go.

💡Refactoring

Refactoring is a software development technique that involves restructuring existing code without changing its external behavior. In the video, the presenter discusses using the AI model to perform large refactoring jobs, taking advantage of the 64k output tokens to restructure codebases in fewer interactions.

💡Composer feature

The 'composer feature' is mentioned in the context of creating and managing files and folders within the Cursor environment. The video demonstrates how the presenter uses the AI model's large output capacity to generate entire folder structures and files, streamlining the initial setup process for coding projects.

💡Folder structure

Folder structure refers to the organization of files and directories in a project. The video shows how the presenter uses the AI model to create a specific folder structure with placeholder content, leveraging the 64k output tokens to automate the setup of complex project environments.

💡Debugging

Debugging is the process of identifying and fixing errors in code. The video discusses switching between different AI models for different tasks, such as using the 'mini' version for initial setup and then switching to the 3.5 version for debugging, to take advantage of each model's strengths in different aspects of coding.

Highlights

Exploring the use of OpenAI's mini version with a focus on productivity and process improvement.

Utilizing XML tags and structure in prompts to enhance precision with AI models.

Testing different Cursor rules to optimize productivity.

Comparing GPT-3.5 with the new GPT-4 models for coding in Cursor.

GPT-3.5 is still considered superior for day-to-day tasks due to speed and reliability.

GPT-4's 64k output tokens allow for large refactoring jobs in fewer prompts.

The necessity for specific prompts with GPT-4 to avoid long thinking times.

Limitations of GPT-4 include the need for one-shot tasks and potential bugs in Cursor implementation.

Strategies for using GPT-4 for massive refactoring jobs in one go.

Using GPT-3.5 for smaller tasks and GPT-4 for larger, one-shot tasks.

Implementing Cursor rules to suggest XML tags for clearer prompts.

Demonstration of using Cursor to rewrite prompts with XML tags for clarity.

Creating a terminal app to fetch and display Hacker News posts using GPT-4.

Efficiently generating folder structures for projects using GPT-4's 64k output tokens.

Developing a pipeline to automate website video updates using GPT-4 and GPT-3.5.

Integrating the new pipeline into a personal website to streamline video updates.

Transcripts

today's video is going to be a bit of a

mixed bag we're going to continue trying

out open

ai1 uh focusing mostly on the mini

version uh today because we are playing

here over on cursor I've been testing

out different cursor rules to see if

that has any effect on my productivity

my processes and I've been kind of

trying to up my prompting game by using

more XML tags more structure and to do

that I've been trying to leverage cursor

here to actually help me rewrite my

prompts into this yeah you can see here

XML tags to hopefully get a more yeah

precise instruction so yeah I think

we're just going to dive in we have some

examples we're going to do so yeah let's

just get started so over the weekend

I've been also trying to look out for

what other people think of using let's

say 01 mini for coding uh maybe on

cursor so I found this Reddit posted

that I thought was pretty good and kind

of fits my view of 01 mini to after

trying it out over this weekend so I

thought we can just go through a few of

the points this uh who's afraid of 138

has made here I thought it had some good

points here so you can see the fast few

days I've been testing 01 mini uh in

cursor compared to 3.5 yeah which has

been a Workhorse model that has been

insanely consistent and useful that is

kind of my IDE too so the verdict is

claw 3.5 sonets is still a better

day-to-day model and that is kind of my

takeaway too just because of speed and

reliability and yeah that is my idea

right but I'm open to change right and

I've been trying out 01 mini over this

weekend so I just wanted to go through

some of the pros he found and some of

the cons and see if I agree here so the

64k output tokens uh that is big right

so if you compare I think a CLA 3.5

maybe has 8K output tokens so that's

eight times more output you can print

right in cursor so that is nice when you

do these composer features and you won't

like create bunch of files that we're

going to look at later uh so you can see

uh if your prompt is good it generally

can do a large refactor re architecture

job in two three shots yeah I think

that's good uh I'm not going to read

example so in general this was quite a

large of reflecting job and it do well

large output context is a big part of

facilitating this I think that's pretty

cool uh so here he has listed some cons

that I kind of agree with so you have to

be very specific with with your prompt

and that is kind of what I dived into

these XML tags this weekend to try to be

even more uh

instructive uh than I've been with clo

3.5 son to see if that can improve

something on when using 01 mini in

cursor right and you can see due to Long

thinking time you pretty much need a

perfect prompt because it's annoying

right if you don't have good enough

instructions and you waste time by

waiting for these 01 models to do their

Chain of Thought and and H there was

something wrong

right uh so he is working with 01 you

have to do everything one shot uh I have

not been doing that but let's see uh

limited chats yeah that's a huge limit

there of course but this is early 64

outcut that has to be that is not a con

right uh I wish son 3.5 has uh much more

output tokens yeah if Sonet had 64k that

would be pretty cool uh verbos yeah

cursor implementation is buggy this is

important so sometimes there's no text

output only code I've seen this too and

I wonder this if this is something to do

with um cursor

backend uh prompting I'm not quite sure

uh so I've been trying to do kind of my

own let me show you here I did this uh

let me see here yeah so I try this 01

rule so ignore all Chain of Thought step

by step prompting when using cursor

ignore chain of thought step by step

prompting me using cursor chat but I

don't know we might try it but I don't

think it's going to work to be honest so

we can see he has 01 mini CLA five son

conclusions so if you're doing a massive

refactoring job or gree and filling a

massive project use 01 mini combination

of deeper thinking and massive output

token limits means you can do things one

shot I haven't tried that too much so I

can't really reflect on that so if you

have a collection of smaller task CL

Sonet is still the king so be very

specific and overl Bose in your prompt

to1 mini describe as much as your task

in detail as possible it will save you

time because this this is not a model to

have conversations or fix small bugs

it's a Ferrari to the Honda at is Sonet

so yeah I've been switching models right

so I maybe I do like one shot with 01

mini and then I kind of switch to 3.5

Sonet so that's the way I've been

solving this but I just thought this

post was pretty interesting and it kind

of confirms

my uh comparisons with 3.5 Sonet 2 so I

just wanted to go through this because

it's ni to get like a confirmation of

what you've been feeling too right so I

think we're just going to dive over to

cursor now test some rules do some

prompting with XML text so let's just

head over there so if you see here on

cursor in the settings you see we have

something called rules for AI right and

here you can kind of set these general

rules that will apply for all your

sessions right but we can also include a

DOT cursor rules files if you didn't

know that and in this uh project we kind

of have these uh different rules so

always assist the user and suggest XML

rapper tags to improve the

prompt uh the user rights right and

we're going to the suggestions must help

must help the user make it as clear as

possible for an llm to understand the

scope of the prompt so these are the

rules I have been testing out and I just

wanted to show you kind of how I've been

playing around with this and how I've

been using cursor to kind of write my

prompts instead of doing it like yeah

just in the chat box right so if you're

planning to try this for yourself uh you

got to kind of look at down in the right

corner here there's something called uh

cursor tab so here we have a hacker

news. mark MD that's kind of a markdown

file so in this cursor tab here you can

see sometimes it says uh disable for

markdown uh so if this is kind of

checked off you can see we have this

cursor tab that is kind of not available

here so uncheck this so you can see we

can use this tab so you can see when I

start writing now right we get some Auto

suggestions right if you wanted to use

that if not just go back and just start

typing so let me just come up with a

prompt here and we can try to show you

kind of the way I've been playing around

with this okay so here you can see my

prompt now I want to create a terminal

app that fetch the top 10 posts on

Hacker News and display them in a retro

terminal style uh I want to scrape The

Hacker News website for this to post

using bs4 I want to use the rich library

to style it the terminal app must

include the links to the post and the

title of the post the code should be in

Python make the code modular easy to

understand and of course be effective

and secure as possible this I kind of

wrap in Project the action for llm is to

execute the project above okay so that

is a prompt I used to use right so what

I've been testing out here I just uh

select all contrl K so here I just go

wrap the prompt in XML tags to make the

instructions as clear as possible for

the llm I just do submit uh edit right

and we are on CL 3.5 Sonet here that's

fine and now this is going to rewrite

this it's going to add all the XML tag

it thinks is the best possible

instructions here to kind of get the

most out of that so you can see here

right so it right so let's just accept

this and take a look now so you can see

we have description we have description

requirements and we have some

requirements here kind of wrapped in

this XML tags we have action step by

step here right okay and yeah I think

this is fine so let's go ahead and try

this out now so let me just open our new

cursor here and let's test this prompt

using open ai1 mini I see I forgot to

add something I've been also focusing on

lately let me uh select all this and

let's do include our instruction to the

llm to include the folder structure of

the project with old folders and files

so this is also something I've been

playing around with lately because uh

using o1 we have this big uh 64k output

token so it's super easy to kind of

recreate folder structure using the

composer feature so you can see here now

uh we added a folder

structure I don't think I want it like

this so let me rephrase this prompt here

so let's do include a step to print a pH

structor with XML tags just the

instruction so I just wanted to add this

into the steps here right for Action

let's see if we can do that okay good

now we have it here so just accept that

let me save that and now let's try to

run this so I just open a new directory

here let's paste in our prompt right uh

now I want to select 01 mini uh because

these are very precise instructions

right so let's just try to run uh in the

chat here first if we get the Fage

structure we're going to take that and

we're going to go to the composer and

try to create all the files before we

add the code right okay that was pretty

quick so now you can see we have our

project structure so I'm just going to

copy this control I right paste in this

and I've kind of played around with uh

so this is kind of my instructions for

generating the folder structure so so

you're an L liit access to generating uh

folds and filers in the current working

directory generate all files and folders

so yeah we have some action here so I'm

just going to call copy this to head

back to our composer and just paste in

this so let's try this now so we are on

the 01 mini

right okay so you can see we have some

placeholder here we have the

requirements so let's just accept all

this and if you go back here now you can

see we have all our files right and then

we can just start adding here so we can

do requirements let's just add this

right we have our scraper dop good we

can apply the code here accept save we

have uh the display so now we have all

files ready right should be pretty easy

we have

main let add

this install the dependencies okay

install

requirements good and then we can run

it okay uh that was a bit strange you

can see it's running but we missing the

posts here uh so let's try to do like a

quick followup here and see if we can

fix this so I'm just going to go the app

is running but we don't see any hacking

News Post in our terminal but now I'm

going to select CLA 3.5 control enter to

kind of use the code base so this is

what I've been doing I've been switching

between 01 mini o1 preview and Claw 3.5

depending on the use case right so we

want to go to our main we want to add

some new code here right okay uh scraper

let's add some new code

here uh let's see now so let's clear

this let's run

it and boom we got it so here is our app

right top 10 Hacker News Post uh we have

this link we can follow right okay boom

we got it so that was pretty cool so

yeah seems to be working pretty nice uh

our F structure was good our initial

prompt from 01 mini was pretty good good

and then we switch to claw 3.5 for some

debugging and boom we got it so yeah so

yeah that was pretty good I'm happy with

this I just wanted to do one more

example to show you kind of how I've

been using 01 mini to create these

folder structures because that's been

pretty interesting right so here you can

see we have a huge folder structure

right and you can see here I kind of

took my prompt in uh in the end here so

you can see the action is create a

specific five Direct and files in the

current work directory for each file

include a basic placeholder content that

represents it purpose or structure so if

we grab this full prompt here so this is

a pretty big folder structure right so

if we grab all this uh let's head over

to yeah we can do just the same folder

here so let's head control shift I into

the composer let's remove this old one

and now you can see we paste in like the

full structure here and here is where

the kind of the big output window comes

uh 64k output token window comes into

play so you can see in the bottom left

here I guess you can't see because of my

face but we are selecting o1 mini and

when we run this now hopefully we can

create this big folder structure uh with

files and folders so let's see

now so you can see now it's writing all

the files right so this is pretty big

it's that's a lot of files to create

right and it was pretty fast when it

first got going here so if we accept all

this now right accept all we go back

here boom we got it so we have our back

end right uh with all the files

middleware chat controller Roots

Services open I service our front end

our app page layout Styles Global CSS

components right uh so what we had to do

now is just fill in this because we

already have created all the files we

could just since you can see they have

this placeholder code so if we had the

code ready now we can just apply this to

all the files so that means that we

didn't manually have to create all the

file Series this is a big Advantage I

have found with using 01 mini uh using

this composer just because of the big uh

output token window right or output

tokens so this is a bunch of files I'm

sure we can do even more if we try to so

I think this is a very good use case so

now if we want to chat we can just

switch to CL 3.5 and start adding code

here right I just think this is very

interesting and this is definitely

something I will be implementing into my

workflow so the final thing I thought we

can do today is actually do something

that is impacting me because this is a

feature I've been wanting for my own

website so let me just show you now so

this is the setup I have for my website

if we head over here now I have a

section called videos so here I kind of

upload all my latest videos right uh but

the way it's set up now if we we go here

I have to go into the code here right I

have to go to website I have to find my

Json structure down here

somewhere where is it yeah you can see

here uh I have to fill in this small Jon

here that has the title description and

iframe that is kind of the embedded

video you can see here and it's very

annoying to do this manually so I kind

of want to create a script that can just

maybe just link the URL and the title

and justun the script in the terminal

and this will generate this kind of

structure uh and I kind of want open a i

API to write the description based on

the the title right so let's see if we

can actually use cursor open a i uh1

mini I think and maybe some 3.5 to

actually create this uh pipeline for me

and this is kind of my raw prompt before

I add the XML tag so I want to create a

pipeline to update my react website it's

easier to use than the current method

now I have to go into the code in the

website to find a code where latest

videos are update the section uh I want

to add a nice pipeline I can use to

update the latest video section on my

website I want to be able to add a

YouTube video by copying the URL pasting

into a script that adds the video Title

by the

video into a script that adds the video

to the website the script should be able

to run with the following command so I

just want to add video URL and title to

write the description for the YouTube

video I want to use llm to generate a

short description based on the title uh

used open a i API the generated Json

structure must be fed into website JS to

update the website I pasted in some

documentation so that's the gp40 mini

implemented features above into my re

website project I will add website JS as

context so now I'm just going to select

all contrl K and let's wrap this into

XML tags so let's just wrap my prompt in

XML tags to make the task as clear as

possible so let's just try this and see

what structure we get now okay so let's

accept this and take a look here so now

we kind of have an objective we have a

current method okay we have the script

we want the process and the description

generation okay that's pretty good and

we have the implementation request so an

action and some context I think we just

going to try this and see if this

actually works so let me just copy this

head

over yeah to our uh directory here that

we have all the files so what I want to

do now is open up our composer right uh

I want to add the website as context I

want to add maybe like the folder so

that's going to

be Source I think and let's paste in our

prompt here now so I think we just going

to try this again I want to select 01

mini here for the first iteration so

let's run this and see where this takes

us okay okay so now you can see we have

our EnV file we have our videos Json so

this is kind of our where we're going to

store our examples if we look at the new

video section now we're going to load

from our Json file I think and the add

video script uh if we open up this I

think we're going to run this

command uh yeah we're going to include

our video and our title okay that's

interesting we need to install some

dependencies yeah I think this looks

pretty interesting and it looks pretty

good so what I'm going to do now is I'm

going to accept all this I'm going to

implement all the code I'm going to set

it up and then I'm just going to try it

and see how this actually work uh yeah

let me do that and I'll come back to you

when we are ready okay so now I kind of

want to show you how this works so if we

head to my website now uh you can see we

have this video here in latest videos

right so I just did a quick test looks

superb right so if we go here now you

can see uh we are using open AI gp40

mini to generate a concise and engaging

description from based on its title

right so we have some examples here on

kind of the ey framing right and yeah

I'm not going to go too much into the

code but this is going to be you can see

new videos will be inserted here by the

ad. video.js script perfect so how this

works now is we can do so what we can do

now node uh at video at video.js right

we can do D- URL and then I can head

over to let's say my YouTube channel uh

let's just copy this URL here I can go

back to cursor paste in this URL I can

do d-h title uh let's do a string and I

can go back here let's just what was it

copy this

title and paste it in here okay that

kind of missed so let me go back and fix

that and we need a string here uh okay

so I'm going to add this so generating

description using open AI boom okay so

you can see this got slotted in here it

got saved if we go to our website now

boom we got a second one so that was

super nice pipeline for me let me just

go grab another video here so AI app of

the week 2 I can just do node ad videojs

URL uh okay so I have the title boom

generate description add it here go back

to my we site boom we have another video

that saves me a lot of time so this is a

superb use case so I'm super happy with

this this is going to save me a lot of

time so yeah combining uh 01 mini with

kind of the The Prompt we created with

all the XML tags and we use some CL 3.5

for some simple

debugging how much time did I spend on

this that's maximum 10 minutes so that's

a superb integration into my website by

adding like a a new feature that saves

me a lot of time when updating new

videos so this is something I'm going to

just Implement and deploy right away so

that's pretty cool right uh yeah super

happy with this and the way it turned

out and combining all of these things

I've been playing around with this

weekend I think this yeah kind of helped

me with my productivity so I hope you

found some of these things interesting

uh if you want to access all of these

prompts I have here uh just follow the

link in the description I have kind of

open public repo where I will be putting

out all of these yeah the small prompts

here I've been using so just go check it

out if you want to copy maybe my folder

structure instructions or something like

that also if you want to shout out

something in my YouTube video some

GitHub project you have you can just go

to my website just go to Services I have

this YouTube video shout out I'm not

sure about the price yet so you can kind

of buy if you want to do some shout out

maybe on your GitHub repo other things

you want to shout out some small startup

you're working on just send me a request

here and maybe we can do something so or

I I can put out the cursor rules too if

you want to copy this but uh just go

play around try out different things

with 01 if you have access if you have

cursor you should have access to at

least a few uh requests right uh but I

think still there's a lot of things we

need to explore here when it comes to

using the new models uh but uh daily

driving is going to be CLA 3.5 maybe

some GPT 40 but I'm going to keep

experimenting using uh using the 01 pre

uh not not so much the o1 preview mostly

I'm going to be using 01 mini I think

but I'm going to try something with the

preview too and it was pretty cool to

see uh what other people have been using

on Min I've been watching some videos

this weekend there's a lot of cool stuff

out there uh but yeah I kind of agree

with this CLA 3.5 son it is still uh the

better day to-day model for me for

now uh but the 64k output tokens in 01

mini makes it super easy when we want to

do this uh big folder structure so that

is something I'm going to using it for

so I'm going to keep experimenting let

me know in the comments if you have any

cool things you have found out about

using o1 yeah I hope you took something

away from this video maybe you learned

something new maybe something you want

to try let me know in the comments give

it a like if you found it interesting

and yeah have a good day and we speak

soon