Compare AI Coding Tools Github Copilot Vs Tabnine vs CodeWhisperer [ Hands on Lab]

hey guys what's up welcome back to another exciting videos so today we have a thrilling showdown between three of the AI power coding assistants that are revolutionizing the way developers write code so it will be a comparison video between AWS code whisper GitHub called pilot and our new tab 9 AI assistance so yeah in this video we'll find out the differences similarities and we'll see a quick comparison with respect to the real life problem solving and how each of the tools provides suggestions or code completions and also at the end I'll provide you with my verdict on which of the tool is best for you guys based on your need and based on your productivity so yeah without further Ado let's find out so before we dive into the comparison let's briefly introduce each of the AI assistants so first of all we have the AWS code whisper that is developed by Amazon web services in April 2023. so code whisper aims to enhance your coding productivity with smart code suggestions and optimizations and also it helps to leverage powerful machine learning algorithms for you guys as well so next we have the GitHub gold pilot that is of quite a famous and you can say well-known tool in the world of AI and it was developed in a collaboration between GitHub and open AI in October 2021 first so GitHub palette basically utilizes the gbt language model to generate entire lines of code and functions based on the natural language descriptions so the last one we have is the tab 9 AI assistance timeline is basically first developed in Fab 2023 for the assistance for the software developers that can basically anticipate code uh to be written for specific apps and files and tab 9 is known for its vast language support and context aware code completions making it a go-to tool for developers worldwide alright so now we'll see all of these tools with the help of a live demonstration by solving daily life problems or realized scenarios just to compare between these three and see which one is the most efficient that which one is the most convenient to use so yeah first of all we'll see the working of the AWS code whisper with the help of a real life example so yeah first of all I'm just gonna type your example to write a program that create the largest element in a list so yeah let's just go ahead and see the suggestion of this code all right yeah as you guys can see so it did provided all the suggestions of the code without a problem like a charm so let's just go ahead and see the output of that now so as you guys can see so it provided me the largest element as 10 which is absolutely right seeing our list over in here so if you can also see that with the help of one another example so this time I'll be prompting AWS code whisper to write me a program that generates a random password with a specific length so then in this code I'll be providing a specific length as an input and after that it will generate a strong password for me using different numbers fashion characters and including letters as well so let's go ahead and try to run this one now okay so seems like our code has been completed as well so if you can go ahead and try to run this so first of all it will ask me to enter the length of the password so over here I'll enter the length as four and yeah as you guys can see so it did provided a password for me using different numbers correctors and alphabets as well so yeah this was basically the working of the AWS code whisper so if you can go ahead and try to see the working GitHub for pilot now just to see how efficient and how good it is okay so in GitHub compiled first of all for the first example I'll be prompting it to create a multiplication table from 1 to 10 so let's just see the suggestion of this prompt now okay so since liger code has been completed so if you can go ahead and try to run this yeah as you guys can see provided all the multiplication table for me from one to ten so yeah it is working absolutely fine so if you can go ahead and do one more example just a little bit of a more complex one to see like whether it's working perfect for us and also this time I'll be prompting him to create me a project like a bank management project which will actually hold the account number account name and all the account information that it will provide me at the end in the form of the output so let's just go ahead and try to run that project for me so yeah seems like our code has been completed so if we can go ahead and try to run this suya is showing me the output as one two three four which is basically the account number Hidden over here then you have 1500 which is our account balance then we have 1300 which was basically the balance after we deposited 200 from our account and after that at the end we have the check balance prompt so that will be 1300 at the end so yeah as you guys can see by using GitHub called palette we can now not only do simple code but we can also use that to help us do like a bit of a more complex and intermediate projects for our use so yeah now let's just go ahead and see our last tool which was tab 9 AI assistance so like that would actually be uh creating a YouTube video downloader using the power of tab 9 AI system so tab 9 is basically a very intelligent coding assistant that can significantly speed up the development process and it can also offer you valuable suggestions as we write code so let's start in on building our YouTube video downloader so yeah first of all as you guys can see over here we'll create a new directory for our project and initialize a new python project we'll use Python for this example since it's a very versatile and widely used programming language so after that I will import the required libraries for our YouTube video downloader so to download a YouTube video obviously we need to get the URL from the user so we'll add some code to the prompt that user for the video URL and after that we'll use the pi tube library to download the video and after that I will simply put these certain prompts that have been showed to you on the screen so yeah now our YouTube video downloader is complete so now let's just go ahead and test it by downloading a YouTube video so yeah there we have it with the help of tab 9 AI system we have built a very efficient and a very simple but functional YouTube video downloader python so tab names intelligent suggestions are made the coding process smoother and it has made that more efficient so at the end tab 9 is undoubtedly uh the best tool in the market which is revolutionizing coding with its AI powered tool and completions and it has no doubt make the development faster and more enjoyable than ever before next one if you see some of the theoretical uh differences and similarities between two of these tools so yeah here is a feature comparison chart between AWS code Whisperer GitHub palette and tab 9 AI assistance so first of all if you're gonna see developer platform so obviously AWS code whisper is developed by AWS GitHub copilot was developed by GitHub and tab 9 is the stand alone development and if you talk about the core functionality of this tool so yeah AWS helps us mainly in code suggest GitHub provided helps us encode generation and tag 9 with the code completions and yet the AI model of these tools are ml model for the AWS code Whisperer GED 3.5 language model for the GitHub Pro pilot and ml model again for the tab 9 AI assistance and if you see about the language support so yeah the AWS is very limited if you talk about the language support and the GitHub copilot is quite extensive it supports quite a few languages that are available in software development world and then tab 9 is also quite a bit extensive so your learning capability is very limited again in AWS code whisper in GitHub and tab 9 it is quite a bit of constant Improvement and yes same goes for the integration as well and if you talk about the code contacts awareness so very moderate in AWS again and high in copilot and tab 9. so pricing is basically uh the AWS code whisper is subscription based same goes for the GitHub for pilot but if you talk about the tab 9 AI assistance it is free of course you don't don't need to pay anything and you can use tab man assistance without paying anything just right away alright so here was few of the comparison of the features of three of the tools that we're talking about today so yeah if you guys are limited to the languages of your code so this is quite a bit of a good chart that you can take help from so yeah if you talk about python so python is supported by all three tools you don't need to worry about going to any of this tools so if you talk about the JavaScript so it is also uh supported by all three tools and typescript it isn't supported by the AWS code whisper but you can use GitHub go about it and tab 9 assistant for that same goes for Ruby go see hash and C plus plus and then if you talk about Java so Java is supported by AWS code whisper and only tab 9 AI systems so yeah with the help of this chart like you can go for any of the tools depending upon the language you are using for your code and yeah if you move further so here are the code editor support that you need to know if you are opting for any of this tool so yeah if you want to use the vs code or jetbrains IDE so you can use any of the tool from this column and if you are using visual studio or neonim so you can only go for the GitHub palette and tab 9 and for the AWS Cloud9 and AWS lambdas obviously for that you are only limited to the AWS code whisper so yeah in conclusion all three AI assistants have very unique strands that can enhance your coding experience uh basically AWS code whisper excels in providing code suggestions while GitHub copilus code generation capabilities are very impressive as well and if you talk about tab 9 AI assistance its contacts aware completions are a huge Plus for developers as well so yeah in my verdict if I can tell you guys my opinion so if we talk about uh the tab 9 it is very fast very efficient and it basically works like a charm there was basically no such problems or errors that I found in my code while using tab 9 and it is also very easy to use in your IDE and by using tab 9 you can basically upgrade your development process to a whole new level so yeah let us know in the comments which AI assistant caught your attention to the most don't forget to like this video if you found it helpful and don't forget to subscribe to our channel for more videos like that and I'll see you guys the next one take care bye

Channel: SkillCurb

Views: 24,420

Keywords: ai tools, ai, github copilot, github copilot x, software developer tools, dev tools, developer tools, what tools do developers use, programming tools, programming software, coding tools, coding software, software engineer tools, what tools do coders use, computer science, software developer, machine learning, Artificial Intelligence, Deep Learning, GPT-4, gpt4, openai, chatgpt, open ai chat gpt, tabnine vs github copilot, aws codewhisperer

Id: StG92jxou_4

Length: 10min 16sec (616 seconds)

Published: Mon Jul 24 2023