Tag Archives: GitHub Copilot

How I used GitHub Copilot to build a browser extension

Post Syndicated from Rizel Scarlett original https://github.blog/2023-05-12-how-i-used-github-copilot-to-build-a-browser-extension/

For the first time ever, I built a browser extension and did it with the help of GitHub Copilot. Here’s how.

I’ve built a rock, paper, scissors game with GitHub Copilot but never a browser extension. As a developer advocate at GitHub, I decided to put GitHub Copilot to the test, including its upcoming chat feature, and see if it could help me write an extension for Google Chrome to clear my cache.

I’m going to be honest: it wasn’t as straightforward as I expected it to be. I had a lot of questions throughout the build and had to learn new information.

But at the end of the day, I gained experience with learning an entirely new skill with a generative AI coding tool and pair programming with GitHub Copilot—and with other developers on Twitch 👥.

I wanted to create steps that anyone—even those without developer experience—could easily replicate when building this extension, or any other extension. But I also wanted to share my new takeaways after a night of pair programming with GitHub Copilot and human developers.

So, below you’ll find two sections:

Let’s jump in.

How to build a Chrome extension with GitHub Copilot

To get started, you’ll need to have GitHub Copilot installed and open in your IDE. I also have access to an early preview of GitHub Copilot chat, which is what I used when I had a question. If you don’t have GitHub Copilot chat, sign up for the waitlist, and pair GitHub Copilot with ChatGPT for now.

1. 🧑🏾‍💻 Using the chat window, I asked GitHub Copilot, “How do I create a Chrome extension? What should the file structure look like?”

💻 GitHub Copilot gave me general steps for creating an extension—from designing the folder structure to running the project locally in Chrome.

Screenshot of the char window where the user asked GitHub Copilot "How do I build a browser extension? What should the file structure look like?" GitHub Copilot provided some instructions in response."

Then, it shared an example of a Chrome extension file structure.

Copilot response showing an example structure for a simple Chrome extension.

To save you some time, here’s a chart that briefly defines the purpose of these files:

manifest.json 🧾 Metadata about your extension, like the name and version, and permissions. Manifest as a proper noun is the name of the Google Chrome API. The latest is V3.
popup.js 🖼 When users click on your extension icon in their Chrome toolbar, a pop-up window will appear. This file is what determines the behavior of that pop-up and contains code for handling user interactions with the pop-up window.
popup.html and style.css 🎨 These files make up the visual of your pop-up window. popup.html is the interface, including layout, structure, and content. style.css determines the way the HTML file should be displayed in the browser, including font, text color, background, etc.

2. Create the manifest.json 🧾

🧑🏾‍💻 Inside a folder in my IDE, I created a file called manifest.json. In manifest.json,

I described my desired file:

Manifest for Chrome extension that clears browser cache.
manifest_version: 3
Permissions for the extension are: storage, tabs, browsingData

I pressed enter and invoked suggestions from GitHub Copilot by typing a curly brace.

💻 Inside the curly brace, GitHub Copilot suggested the manifest. I deleted the lines describing my desired manifest.json, and the final file looked like this:

{
   "name": "Clear Cache",
   "version": "1.0",
   "manifest_version": 3,
   "description": "Clears browser cache",
   "permissions": [
       "storage",
       "tabs",
       "browsingData"
   ],
   "action": {
       "default_popup": "popup.html"
   },
   "background": {
       "service_worker": "background.js"
   }
}

3. Create a service worker, which is a file called background.js 🔧

This wasn’t a file that was recommended from my chat with GitHub Copilot. I learned that it was a necessary file from a developer who tuned into my livestream 👥. The background.js is what gives your extension the ability to run in the background, perform tasks, and respond to user events outside of the extension’s pop-up window (like network requests and data storage).

🧑🏾‍💻 In my background.js file, I wrote a comment describing my desired service worker:

Service Worker for Google Chrome Extension 
Handles when extension is installed
Handles when message is received

Then, I wrote a comment for the first function, which prompted a suggestion from GitHub Copilot, and then wrote another comment to describe the second function.

💻 The final file looked like this:

/*
Service Worker for Google Chrome Extension
Handles when extension is installed
Handles when message is received
*/

// console.log when extension is installed
chrome.runtime.onInstalled.addListener(function() {
   console.log("Extension installed");
});

// send response when message is received and console.log when message is received
chrome.runtime.onMessage.addListener(function(request, sender, sendResponse) {
   console.log("Message received");
   sendResponse("Message received");
});

4. Create the popup.html file 🖼

🧑🏾‍💻 In the popup.html file, I wrote a comment describing how I wanted my pop-up window to appear. This window is the one users will see when they click on the extension icon.

<!--
   HTML for Chrome extension that clears browser cache.
   Connect to javascript file called popup.js and CSS file called style.css
   Will render the following buttons with id's:
   - "All History"
   - "Past Month"
   - "Past Week"
   - "Past Day"
   - "Past Hour"
   - "Past Minute"


   Will render an empty paragraph with id "lastCleared"
-->

💻 GitHub Copilot suggested the following:

<!DOCTYPE html>
<html>
   <head>
       <meta charset="utf-8">
       <title>Clear Cache</title>
       <link rel="stylesheet" href="style.css">
   </head>
   <body>
       <h1>Clear Cache</h1>
       <button id="allHistory">All History</button>
       <button id="pastMonth">Past Month</button>
       <button id="pastWeek">Past Week</button>
       <button id="pastDay">Past Day</button>
       <button id="pastHour">Past Hour</button>
       <button id="pastMinute">Past Minute</button>
       <p id="lastCleared"></p>
       <script src="popup.js"></script>
   </body>
</html>

5. Test the browser extension

I decided to test the modifications before incorporating additional styles or interactivity. I prefer making incremental changes and conducting manual tests in between because it simplifies the debugging process.

🧑🏾‍💻 Navigate to chrome://extensions/ in your Chrome browser. Turn on developer mode. Press “Load unpacked.” Upload the folder that holds your Chrome extension. Once loaded, you should be able to test your extension. Here’s what mine looked like:

Screenshot of the Chrome extension offering options to "Clear Cache."

6. Create a popup.js file to add interactivity

🧑🏾‍💻 Inside this file, write a comment that will act as pseudocode for you but as a prompt for GitHub Copilot.

Pseudocode isn’t code, but a method of conceptualizing and communicating ideas to plan and design programs before writing the actual code. Programmers can concentrate on algorithmic logic without worrying about syntactic details of a particular programming language—and communicate ideas to other developers regardless of coding experience.

Here’s the pseudocode I used:

/*
This program is a Chrome Extension that clears browser cache.
Handle on button click:
- button with id "allHistory" that clears all cache history
- button with id "pastMonth" that clears cache history from the past month
- button with id "pastWeek" that clears cache history from the past week
- button with id "pastDay" that clears cache history from the past day
- button with id "pastHour" that clears cache history from the past hour
- button with id "pastMinute" that clears cache history from the past minute


Create function that
- converts dates and times into human-readable format
- adds "Successfully cleared cache" with date and time in a paragraph with id "lastCleared"
*/

Then, write the series of comments below, but write them one at a time to allow GitHub Copilot to generate a code suggestion after each one. The final popup.js looked like this:

/*
This program is a Chrome Extension that clears browser cache.
Handle on button click:
- button with id "allHistory" that clears all cache history
- button with id "pastMonth" that clears cache history from the past month
- button with id "pastWeek" that clears cache history from the past week
- button with id "pastDay" that clears cache history from the past day
- button with id "pastHour" that clears cache history from the past hour
- button with id "pastMinute" that clears cache history from the past minute

Create function that
- converts dates and times into human-readable format
- adds "Successfully cleared cache" with date and time in a paragraph with id "lastCleared"
*/

// convert date and time into human-readable format
function convertDate(date) {
   let date = new Date(date);
   var options = { weekday: 'long', year: 'numeric', month: 'long', day: 'numeric' };
   return date.toLocaleDateString("en-US", options);
}

// add successfully cleared cache into paragraph with id "lastCleared"
function addCleared() {
   var p = document.getElementById("lastCleared");
   let date = new Date();
   p.innerHTML = "Successfully cleared cache " + convertDate(date);
}

// clear all cache history
document.getElementById("allHistory").addEventListener("click", function() {
   chrome.browsingData.removeCache({ "since": 0 }, function() {
       addCleared();
   });
});

// clear cache history from the past month
document.getElementById("pastMonth").addEventListener("click", function() {
   let date = new Date();
   date.setMonth(date.getMonth() - 1);
   chrome.browsingData.removeCache({ "since": date.getTime() }, function() {
       addCleared();
   });
});

// clear cache history from the past week
document.getElementById("pastWeek").addEventListener("click", function() {
   let date = new Date();
   date.setDate(date.getDate() - 7);
   chrome.browsingData.removeCache({ "since": date.getTime() }, function() {
       addCleared();
   });
});

// clear cache history from the past day
document.getElementById("pastDay").addEventListener("click", function() {
   let date = new Date();
   date.setDate(date.getDate() - 1);
   chrome.browsingData.removeCache({ "since": date.getTime() }, function() {
       addCleared();
   });
});

// clear cache history from the past hour
document.getElementById("pastHour").addEventListener("click", function() {
  let date = new Date();
   date.setHours(date.getHours() - 1);
   chrome.browsingData.removeCache({ "since": date.getTime() }, function() {
       addCleared();
   });
});

// clear cache history from the past minute
document.getElementById("pastMinute").addEventListener("click", function() {
  let date = new Date();
   date.setMinutes(date.getMinutes() - 1);
   chrome.browsingData.removeCache({ "since": date.getTime() }, function() {
       addCleared();
   });
});

🧑🏾‍💻 GitHub Copilot actually generated the var keyword, which is outdated. So I changed that keyword to let.

7. Create the last file in your folder: style.css

🧑🏾‍💻 Write a comment that describes the style you want for your extension. Then, type “body” and continue tabbing until GitHub Copilot suggests all the styles.

My final style.css looked like this:

/* Style the Chrome extension's popup to be wider and taller
Use accessible friendly colors and fonts
Make h1 elements legible
Highlight when buttons are hovered over
Highlight when buttons are clicked
Align buttons in a column and center them but space them out evenly
Make paragraph bold and legible
*/

body {
   background-color: #f1f1f1;
   font-family: Arial, Helvetica, sans-serif;
   font-size: 16px;
   color: #333;
   width: 400px;
   height: 400px;
}

h1 {
   font-size: 24px;
   color: #333;
   text-align: center;
}

button {
   background-color: #4CAF50;
   color: white;
   padding: 15px 32px;
   text-align: center;
   text-decoration: none;
   display: inline-block;
   font-size: 16px;
   margin: 4px 2px;
   cursor: pointer;
   border-radius: 8px;
}

button:hover {
   background-color: #45a049;
}

button:active {
   background-color: #3e8e41;
}

p {
   font-weight: bold;
   font-size: 18px;
   color: #333;
}
For detailed, step-by-step instructions, check out my Chrome extension with GitHub Copilot repo.

Three important lessons about learning and pair programming in the age of AI

  1. Generative AI reduces the fear of making mistakes. It can be daunting to learn a new language or framework, or start a new project. The fear of not knowing where to start—or making a mistake that could take hours to debug—can be a significant barrier to getting started. I’ve been a developer for over three years, but streaming while coding makes me nervous. I sometimes focus more on people watching me code and forget the actual logic. When I conversed with GitHub Copilot, I gained reassurance that I was going in the right direction and that helped me to stay motivated and confident during the stream.

  2. Generative AI makes it easier to learn about new subjects, but it doesn’t replace the work of learning. GitHub Copilot didn’t magically write an entire Chrome extension for me. I had to experiment with different prompts, and ask questions to GitHub Copilot, ChatGPT, Google, and developers on my livestream. To put it in perspective, it took me about 1.5 hours to do steps 1 to 5 while streaming.

    But if I hadn’t used GitHub Copilot, I would’ve had to write all this code by scratch or look it up in piecemeal searches. With the AI-generated code suggestions, I was able to jump right into review and troubleshooting, so a lot of my time and energy was focused on understanding how the code worked. I still had to put in the effort to learn an entirely new skill, but I was analyzing and evaluating code more often than I was trying to learn and then remember it.

  3. Generative AI coding tools made it easier for me to collaborate with other developers. Developers who tuned into the livestream could understand my thought process because I had to tell GitHub Copilot what I wanted it to do. By clearly communicating my intentions with the AI pair programmer, I ended up communicating them more clearly with developers on my livestream, too. That made it easy for people tuning in to become my virtual pair programmers during my livestream.

Overall, working with GitHub Copilot made my thought process and workflow more transparent. Like I said earlier, it was actually a developer on my livestream who recommended a service worker file after noticing that GitHub Copilot didn’t include it in its suggested file structure. Once I confirmed in a chat conversation with GitHub Copilot and a Google search that I needed a service worker, I used GitHub Copilot to help me write one.

Take this with you

GitHub Copilot made me more confident with learning something new and collaborating with other developers. As I said before, live coding can be nerve-wracking. (I even get nervous even when I’m just pair programming with a coworker!) But GitHub Copilot’s real-time code suggestions and corrections created a safety net, allowing me to code more confidently—and quickly— in front of a live audience. Also, because I had to clearly communicate my intentions with the AI pair programmer, I was also communicating clearly with the developers who tuned into my livestream. This made it easy to virtually collaborate with them.

The real-time interaction with GitHub Copilot and the other developers helped with catching errors, learning coding suggestions, and reinforcing my own understanding and knowledge. The result was a high-quality codebase for a browser extension.

This project is a testament to the collaborative power of human-AI interaction. The experience underscored how GitHub Copilot can be a powerful tool in boosting confidence, facilitating learning, and fostering collaboration among developers.


More resources

How companies are boosting productivity with generative AI

Post Syndicated from Chris Reddington original https://github.blog/2023-05-09-how-companies-are-boosting-productivity-with-generative-ai/

Is your company using generative AI yet?

While it’s still in its infancy, generative AI coding tools are already changing the way developers and companies build software. Generative AI can boost developer and business productivity by automating tasks, improving communication and collaboration, and providing insights that can inform better decision-making.

In this post, we’ll explore the full story of how companies are adopting generative AI to ship software faster, including:

Want to explore the world of generative AI for developers? 🌎

Check out our generative AI guide to learn what it is, how it works, and what it means for developers everywhere.

Get the guide >

What is generative AI?

Generative AI refers to a class of artificial intelligence (AI) systems designed to create new content similar to what humans produce. These systems are trained on large datasets of content that include text, images, audio, music, or code.

Generative AI is an extension of traditional machine learning, which trains models to predict or classify data based on existing patterns. But instead of simply predicting the outcome, generative AI models are designed to identify underlying patterns and structures of the data, and then use that knowledge to quickly generate new content. However, the main difference between the two is one of magnitude and the size of the prediction or generation. Machine learning typically predicts the next word. Generative AI can generate the next paragraph.

AI-generated image from Shutterstack of a developer using a generative AI tool to code faster.
AI-generated image from Shutterstack of a developer using a generative AI tool to code faster.

Generative AI tools have attracted particular interest in the business world. From marketing to software development, organizational leaders are increasingly curious about the benefits of the new generative AI applications and products.

“I do think that all companies will adopt generative AI tools in the near future, at least indirectly,” said Albert Ziegler, principal machine learning engineer at GitHub. “The bakery around the corner might have a logo that the designer made using a generative transformer. The neighbor selling knitted socks might have asked Bing where to buy a certain kind of wool. My taxi driver might do their taxes with a certain Excel plugin. This adoption will only increase over time.”

What are some business uses of generative AI tools? 💡

  • Software development: generative AI tools can assist engineers with building, editing, and testing code.
  • Content creation: writers can use generative AI tools to help personalize product descriptions and write ad copy.
  • Design creation: from generating layouts to assisting with graphics, generative AI design tools can help designers create entirely new designs.
  • Video creation: generative AI tools can help videographers with building, editing, or enhancing videos and images.
  • Language translation: translators can use generative AI tools to create communications in different languages.
  • Personalization: generative AI tools can assist businesses with personalizing products and services to meet the needs of individual customers.
  • Operations: from supply chain management to pricing, generative AI tools can help operations professionals drive efficiency.

How generative AI coding tools are changing the developer experience

Generative AI has big implications for developers, as the tools can enable them to code and ship software faster.

How is generative AI affecting software development?⚡

Check out our guide to learn what generative AI coding tools are, what developers are using them for, and how they’re impacting the future of development.

Get the guide >

Similar to how spell check and other automation tools can help writers build content more efficiently, generative AI coding tools can help developers produce cleaner work—and the models powering these tools are getting better by the month. Tools such as GitHub Copilot, for instance, can be used in many parts of the software development lifecycle, including in IDEs, code reviews, and testing.

The science backs this up. In 2022, we conducted research into how our generative AI tool, GitHub Copilot, helps developers. Here’s what we found:

Source: Research: quantifying GitHub Copilot’s impact on developer productivity and happiness

GitHub Copilot is only continuing to improve. When the tool was first launched for individuals in June 2022, more than 27% of developers’ code was generated by GitHub Copilot, on average. Today, that number is 46% across all programming languages—and in Java, that jumps to 61%.

How can generative AI tools help you build software? 🚀

These tools can help:

  • Write boilerplate code for various programming languages and frameworks.
  • Find information in documentation to understand what the code does.
  • Identify security vulnerabilities and implement fixes.
  • Streamline code reviews before merging new or edited code.

Explore GitHub’s vision for embedding generative AI into every aspect of the developer workflow.

Using generative AI responsibly 🙏

Like all technologies, responsibility and ethics are important with generative AI.

In February 2023, a group of 10 companies including OpenAI, Adobe, the BBC, and others agreed upon a new set of recommendations on how to use generative AI content in a responsible way.

The recommendations were put together by the Partnership on AI (PAI), an AI research nonprofit, in consultation with more than 50 organizations. The guidelines call for creators and distributors of generative AI to be transparent about what the technology can and can’t do and disclose when users might be interacting with this type of content (by using watermarks, disclaimers, or traceable elements in an AI model’s training data).

Is generative AI accurate? 🔑

Businesses should be aware that while generative AI tools can speed up the creation of content, they should not be solely relied upon as a source of truth. A recent study suggests that people can identify whether AI-generated content is real or fake only 50% of the time. Here at GitHub, we named our generative AI tool “GitHub Copilot” to signify just this—the tool can help, but at the end of the day, it’s just a copilot. The developer needs to take responsibility for ensuring that the finished code is accurate and complete.

How companies are using generative AI

Even as generative AI models and tools continue to rapidly advance, businesses are already exploring how to incorporate these into their day-to-day operations.

This is particularly true for software development teams.

“Going forward, tech companies that don’t adopt generative AI tools will have a significant productivity disadvantage,” Ziegler said. “Given how much faster this technology can help developers build, organizations that don’t adopt these tools or create their own will have a harder time in the marketplace.”

3 primary generative AI business models for organizations 📈

Enterprises all over the world are using generative AI tools to transform how work gets done. Three of the business models organizations use include:

  • Model as a Service (MaaS): Companies access generative AI models through the cloud and use them to create new content. OpenAI employs this model, which licenses its GPT-3 AI model, the platform behind ChatGPT. This option offers low-risk, low-cost access to generative AI, with limited upfront investment and high flexibility.
  • Built-in apps: Companies build new—or existing—apps on top of generative AI models to create new experiences. GitHub Copilot uses this model, which relies on Codex to analyze the context of the code to provide intelligent suggestions on how to complete it. This option offers high customization and specialized solutions with scalability.
  • Vertical integration: Vertical integration leverages existing systems to enhance the offerings. For instance, companies may use generative AI models to analyze large amounts of data and make predictions about prices or improve the accuracy of their services.

Duolingo, one of the largest language-learning apps in the world, is one company that recently adopted generative AI capabilities. They chose GitHub’s generative AI tool, GitHub Copilot, to help their developers write and ship code faster, while improving test coverage. Duolingo’s CTO Severin Hacker said GitHub Copilot delivered immediate benefits to the team, enabling them to code quickly and deliver their best work.

”[The tool] stops you from getting distracted when you’re doing deep work that requires a lot of your brain power,” Hacker noted. “You spend less time on routine work and more time on the hard stuff. With GitHub Copilot, our developers stay in the flow state and keep momentum instead of clawing through code libraries or documentation.”

After adopting GitHub Copilot and the GitHub platform, Duolingo saw a:

  • 25% increase in developer speed for those who are new to working with a specific repository
  • 10% increase in developer speed for those who are familiar with the respective codebase
  • 67% decrease in median code review turnaround time

“I don’t know of anything available today that’s remotely close to what we can get with GitHub Copilot,” Hacker said.

Looking forward

Generative AI is changing the world of software development. And it’s just getting started. The technology is quickly improving and more use cases are being identified across the software development lifecycle. With the announcement of GitHub Copilot X, our vision for the future of AI-powered software development, we’re committed to installing AI capabilities into every step of the developer workflow. There’s no better time to get started with generative AI at your company.

Web Summit Rio 2023: Building an app in 18 minutes with GitHub Copilot X

Post Syndicated from Thomas Dohmke original https://github.blog/2023-05-05-web-summit-rio-2023-building-an-app-in-18-minutes-with-github-copilot-x/

Missed GitHub CEO Thomas Dohmke’s Web Summit Rio 2023 talk? Read his remarks and get caught up on everything GitHub Copilot X.

Hello, Rio! I’m Thomas, and I’m a developer. I’ve been looking forward to this for some time, about a year. When I first planned this talk, I wanted to talk about the future of artificial intelligence and applications. But then I thought, why not show you the full power live on stage. Build something with code.

I’ve been a developer my entire adult life. I’ve been coding since 1989. But as CEO, I haven’t been able to light up my contribution graph in a while. Like all of you, like every developer, my energy, my creativity every day is finite. And all the distractions of life—from the personal to the professional—from morning to night, gradually zaps my creative energy.

When I wake up, I’m at my most creative and sometimes I have a big, light bulb idea. But as soon as the coffee is poured, the distractions start. Slack messages and emails flood in. I have to review a document before a customer presentation. And then I hear my kids yell as their football flew over the fence. And, of course, they want me to go over to our neighbors’ and get it.

Then…I sit back down, I am in wall-to-wall meetings. And by the time the sun comes down, that idea I had in the morning—it’s gone! It’s midnight and I can’t even keep my eyes open. To be honest, in all this, I usually don’t even want to get started because the perception of having to do all the mundane tasks, all the boilerplate work that comes with coding, stops me in my tracks. And I know this is true for so many developers.

The point is: boilerplate sucks! All busywork in life, but especially repetitive work is a barrier between us and what we want to achieve. Every day, building endless boilerplate prevents developers from creating a new idea that will change the world. But with AI, these barriers are about to be shattered.

You’ve likely seen the memes about the 10x developer. It’s a common internet joke—people have claimed the 10x developer many times. With GitHub Copilot and now Copilot X, it’s time for us to redefine this! It isn’t that developers need to strive to be 10x, it’s that every developer deserves to be made 10 times more productive. With AI at every step, we will truly create without captivity. With AI at every step, we will realize the 10x developer.

Imagine: 10 days of work, done in one day. 10 hours of work, done in one hour. 10 minutes of work, done with a single prompt command. This will allow us to amplify our truest self-expression. It will help a new generation of developers learn and build as fast as their minds. And because of this, we’ll emerge into a new spring of digital creativity, where every light bulb idea we have when we open our eyes for the day can be fully realized no matter what life throws at us.

During my presentation, I built a snake game in 15 minutes.

This morning I woke up, and I wanted to build something. I wanted to build my own snake game, originally created back in 1976. It’s a classic. So, 15 minutes are left on my timer. And I’m going to build this snake game. Let’s bring up Copilot X and get going!

See how you can 10x your developer superpowers. Discover GitHub Copilot X.

CLI tricks every developer should know

Post Syndicated from Kedasha Kerr original https://github.blog/2023-04-26-cli-tricks-every-developer-should-know/

The CLI is a critical component of a developer’s toolkit—it’s a trusty sidekick that offers flexibility and control. You can tell it what to do by simply typing in a specific command, and it will execute those commands like moving files, running programs, or even starting up a server, immediately. That being said, the CLI can be daunting to beginners, especially when you’re not sure which commands to run (and who hasn’t turned to Google to find a command they need?).

In this blog, we’ve compiled some important tricks and commands that every developer should know from GitHub’s own engineers. By mastering these basic techniques, developers can become more efficient at working with the command line and gain a deeper understanding of how the underlying operating system and programs work.

Components of the CLI

The CLI has two main components: the shell and the command. The shell is the interface that allows the user to enter commands and commands are the instructions that tell the computer what to do. Shells also provide a way to customize and extend the behavior of the CLI. With a shell, users can create their own scripts and aliases to automate tasks or simplify complex commands, and they can customize the behavior of the shell itself using configuration files. For this blog post, all of the examples are for Bash since it’s the most widely used shell. And if you’re using Windows, Windows Subsystem for Linux (WSL) is available if you’d like to use a Bash terminal.

To learn more about shells, you can check out our shell documentation here.

Keyboard shortcuts in the CLI

One of the easiest ways to improve your productivity on the command line is to learn some keyboard shortcuts. Below, you’ll find popular shortcuts that can save you time and effort on navigating and executing demands.

CTRL + C: Cancel the current command

CTRL + A: Move the cursor to the beginning of the line

CTRL + E: Move the cursor to the end of the line

CTRL + L: Clear the terminal screen

CTRL + _: Undo the last edit

CTRL-D: Exit shell session

TAB:Auto-complete commands, file names, and paths

CLI command history shortcuts

The command history allows you to quickly access and reuse previously executed commands rather than retype or search for the whole command. Here are a few to try out for yourself:

history n: Type this in the terminal to access the history

!!: Execute the last command

CTRL + R: Reverse search for a previously executed command

CTRL + P: Move to the previous command in the history

CTRL + N: Move to the next command in the history

CTRL + G: Abort search

Perform operations on multiple files with wildcards

Wildcards are characters that take the place of one or more other characters, and they’re used to increase the efficiency and flexibility of searches. They come in handy when you want to perform an operation on multiple files or directories that have similar names or patterns, and can save you a lot of time and effort by allowing you to specify patterns rather than list each individual file. There are three types of command wildcards:

?: Matches a single character. For example, if you type d?g, it will match anything that begins with a “d” and ends with an “g.”

*: Matches any number of characters. If you search s*n, it will match anything between “s” and “n” no matter how many characters are between the first and last letter.

[]: Matches only the characters enclosed within the square brackets.

Combine commands with pipes

A pipe (represented by the symbol |) connects the output of the command on the left side of the symbol to the input of the command on the right side. This allows users to create more complex and powerful commands by combining simpler commands together.

Here’s an example of how it’s used in a command:

ls -la | grep test | sort | uniq | wc -l: The final output of this command would be the number of files and directories in the current directory whose name contains the string “test.”

When you pipe all of these commands together, you are essentially:

  • Listing all files and directories in the current directory in long format
  • Searching for all lines that contain the string “test”
  • Sorting those lines alphabetically
  • Removing any duplicate lines
  • Counting the number of remaining lines

Note: grep is a useful CLI tool to search for matching patterns in a file. We’ll explore some more helpful CLI tools later on in this article.

Command substitution

Command substitution is a feature that allows you to execute a command with a different command. This helps you create more complex and dynamic commands by using the output of one command as an argument for another.

There are two different syntaxes for command substitution:

$(command) or `command`

Here’s a simple example:

$ echo "the date is $(date)": This will display the current date and time in a sentence.

Learning the CLI commands and tricks that work for you takes time and practice. But you’ll soon find that using the command line becomes second nature, and you’ll be able to accomplish more complex tasks with ease.

Now that we’ve covered some basics, you can begin to experiment with different options and parameters for your commands to see what they do. And stay curious! There are tons of great resources out there, like tldr or Explainshell, to help you learn new commands or shortcuts. And speaking of resources, let’s take a look at some helpful CLI tools to help you optimize the command line—and have some fun with it.

CLI tools to know

Command line tools are scripts, programs, or libraries created to solve problems that developers might encounter while coding. While these tools are largely focused on improving productivity, there are also CLI tools that can entertain you while you’re in the terminal (check out football-cli or spotify-tui for reference).

Here are a few CLI tools that we recommend.

grep or ack

grep is a command-line utility that allows users to search for specific patterns in text files. Users can search for a specific word, phrase, or regular expression, and output the matching lines. ack is designed to be a faster alternative to the traditional grep command. Some prefer ack over grep because it can automatically exclude files that are not typically used for programming, such as binary files or backup files.

jq

jq is a lightweight and powerful command-line JSON processor that allows developers to parse, filter, and transform JSON data using a range of query and manipulation functions. With jq, developers can easily extract specific data from large JSON files, format JSON output, and even convert between JSON and other data formats.

ImageMagick

Though ImageMagick has been around since the ‘90s, this tool remains the gold standard for all things image-related. It supports a wide range of image formats and can help simplify resizing, cropping, rotating, and applying filters and effects. One of the most powerful features of ImageMagick is its ability to automate batch processing of images, which is particularly useful for web developers and designers who need to resize or convert large numbers of images at once.

howdoi

howdoi provides answers to your programming questions directly in the terminal. This command-line tool is particularly useful since you don’t have to leave the CLI or open your web browser to get immediate access to programming knowledge.

Taskwarrior

Taskwarrior manages your to-do list right from the command line. It also includes a variety of reports and filters that allow you to view your tasks in different ways, like by project, due date, or priority.

GitHub CLI

GitHub CLI provides a streamlined and efficient way to manage GitHub projects and workflows directly from the command line. You can perform a range of tasks, like managing pull requests, viewing and responding to issues and comments, and reviewing code changes. You can also use it to access GitHub Actions and other features on GitHub without exiting your terminal.

The bottom line: ​​The CLI is a powerful tool for developers—especially if you prefer the keyboard over GUI interfaces. Learning how to use it and incorporating it into your daily workflow will help you become more productive and efficient, and getting familiar with these tips and tricks is a great way to start your CLI journey.

Additional resources for CLI

As you move further along in your developing journey, you’ll encounter different projects and pain points, which means that the commands and CLI tools you find useful might change and shift. There are so many diverse and useful CLI applications out there, and the repository awesome-cli-apps lists a bunch of excellent tools that are used and loved by developers around the world.

What’s next for CLI

We’ll be launching a technical preview of GitHub Copilot for CLI, which translates natural language prompts into terminal commands to help you find the exact command you need for the task at hand. Click here to get on the waitlist and try it out for yourself.

3 benefits of migrating and consolidating your source code

Post Syndicated from Mark Paulsen original https://github.blog/2023-04-14-3-benefits-of-migrating-and-consolidating-your-source-code/

In a previous blog on consolidating your toolkit, we shared strategies to help you simplify your tech stack, which ultimately helps developers be more productive. In fact, developers are almost 60% more likely to feel equipped to do their job when they can easily find what they need.

But there are other benefits of consolidating and simplifying your toolkit that may be surprising–especially when migrating your source code and collaboration history to GitHub.

Today, we’ll explore three benefits that will support enterprises in a business climate where everyone is being asked to do more with less, as well as some resources to help get started on the migration journey.

1. Enable developer self-service culture

Some of the benefits enterprises can achieve with DevOps are improved productivity, security, and collaboration. Silos should be broken down and traditionally separated teams should be working in a cohesive and cloud native way.

Another benefit that DevOps enables, which is a key part of the Platform Engineering technology approach, is the ability for development teams to self-service workflows, processes, and controls which traditionally have either been manual, or tightly-coupled with other teams. A great example of this was covered in a previous blog where we described how to build consistent and shared IaC workflows. IaC workflows can be created by operations teams, if your enterprise separation of duties governance policies require this, but self-serviced when needed by development teams.

But this type of consistent, managed, and governable, self-service culture would not be possible if you have multiple source code management tools in your enterprise. If development teams have to spend time figuring out which tool has the source of truth for the workflow they need to execute, the benefits of DevOps and Platform Engineering quickly deteriorate.

There is no better place to migrate the core of your self-service culture to than GitHub–which is the home to 100 million developers and counting. Your source code management tool should be an enabler for developer productivity and happiness or else they will be reluctant to use it. And if they don’t use it, you won’t have a self-service culture within your enterprise.

2. Save time and money during audits

The latest Forrester report on the economic impact of GitHub Enterprise Cloud and GitHub Advanced Security, determined a 75% improvement in time spent managing tools and code infrastructure. But one of the potentially surprising benefits is related to implementing DevOps and cloud native processes that would both help developers and auditors save time and money.

If your tech stack includes multiple source code tools, and other development tools which may not be integrated our have overlapping capabilities, each time your security, compliance, and audit teams need to review the source of truth for your delivery artifacts, you will need to gather artifacts and setup walkthroughs for each of the tools. This can lead to days and even weeks of lost time and money on simply preparing and executing audits–taking your delivery teams away from creating business value.

Working with GitHub customers, Forrester identified and quantified key benefits of investing in GitHub Enterprise Cloud and GitHub Advanced Security. The corresponding GitHub Ent ROI Estimate Calculator includes factors for time saving on IT Audit preparations related to the number of non-development security or audit staff involved in software development. This itself can lead to hundreds of thousands if not millions of dollars of time savings.

What is not factored into the calculator is the potential time savings for development teams who have a single source of truth for their code and collaboration history. A simplified and centrally auditable tech stack with a single developer-friendly core source code management platform will enable consistent productivity even during traditionally time-consuming audit and compliance reviews–for both developers and non-developers.

3. Keep up with innovation

If you are using another source code platform besides GitHub, or if GitHub is one of several tools that are providing the overlapping functionality, some of your teams may be missing out on the amazing innovations that have been happening lately.

Generative AI is enabling some amazing capabilities and GitHub is at the forefront with our AI pair-programmer, GitHub Copilot. The improvements to developer productivity are truly amazing and continue to improve.

A graphic showing how many developers and companies have already used GitHub Copilot and how it's helping improve productivity and happiness.
A graphic showing how many developers and companies have already used GitHub Copilot and how it’s helping improve productivity and happiness.

GitHub continues to innovate with the news about GitHub Copilot X, which is not only adopting OpenAI’s new GPT-4 model, but introducing chat and voice for GitHub Copilot, and bringing GitHub Copilot to pull requests, the command line, and docs to answer questions on your projects.

Innovations like this need to be rolled-out in a controlled and governable manner within many enterprises. But if your techstack is overly complex and you have several source code management tools, the roll-out may take a long time or may be stalled while security and compliance reviews take place.

However, if your development core is GitHub, security and compliance reviews can happen once, on a centrally managed platform that is well understood and secure. And you’ll be front row for all of the amazing new innovations that GitHub will be releasing down the road.

Get started today

If you are planning on migrating your source code and collaboration history to GitHub and have questions, thankfully, many other enterprises have done this already with great success and there are resources to help you through the process.

Visit our GitHub Enterprise Importer documentation for details on supported migration paths, guides for different migration sources, and more.

If you want to learn more about how GitHub can benefit your business, while increasing developer velocity and collaboration, see how GitHub Enterprise can help.

Generative AI-enabled compliance for software development

Post Syndicated from Mark Paulsen original https://github.blog/2023-04-11-generative-ai-enabled-compliance-for-software-development/

In our recent blog post announcing GitHub Copilot X, we mentioned that generative AI represents the future of software development. This amazing technology will enable developers to stay in the flow while helping enterprises meet their business goals.

But as we have also mentioned in our blog series on compliance, generative AI may soon act as an enabler for developer-focused compliance programs that will drive optimization and keep your development, compliance and audit teams productive and happy.

Today, we’ll explore the potential for generative AI to help enable teams to optimize and automate some of the foundational compliance components of separation of duties that many enterprises still often manage and review manually.

Generative AI has been dominating the news lately—but what exactly is it? Here’s what you need to know, and what it means for developers.

Separation of duties

The concept of “separation of duties,” long used in the accounting world as a check and balance approach, is also adopted in other scenarios, including technology architecture and workflows. While helpful to address compliance, it can lead to additional manual steps that can slow down delivery and innovation.

Fortunately, the PCI-DSS requirements guide provides a more DevOps, cloud native, and AI-enabled approach to separation of duties by focusing on functions and accounts, as opposed to people:

“The purpose of this requirement is to separate the development and test functions from the production functions. For example, a developer can use an administrator-level account with elevated privileges in the development environment and have a separate account with user-level access to the production environment.”

There are many parts of a software delivery workflow that need to have separation of duties in place—but one of the core components that is key for any compliance program is the code review. Having a separate set of objective eyes reviewing your code, whether it’s human or AI-powered, helps to ensure risks, tech debt, and security vulnerabilities are found and mitigated as early as possible.

Code reviews also help enable the concept of separation of duties since it prohibits a single person or a single function, account, or process from moving code to the next part of your delivery workflow. Additionally, code reviews help enable separation of duties for Infrastructure as Code (IaC) workflows, Policy-as-Code configurations, and even Kubernetes declarative deployments.

As we mentioned in our previous blog, GitHub makes code review easy, since pull requests are part of the existing workflow that millions of developers use daily. Having a foundational piece of compliance built-in to the platform that developers know and love keeps them in the flow, while keeping compliance and audit teams happy as well.

Generative AI and pull requests

Wouldn’t it be cool if one-day generative AI could be leveraged to enable more developer-friendly compliance programs which have traditionally been very labor and time intensive? Imagine if generative AI could help enable DevOps and cloud native approaches for separation of duties by automating tedious tasks and allowing humans to focus on key value-added tasks.

Bringing this back to compliance and separation of duties, wouldn’t it be great if a generative AI helper was available to provide an objective set of eyes on your pull requests? This is what the GitHub Next team has been working towards with GitHub Copilot for Pull Requests.

  • Suggestions for your pull request descriptions. AI-powered tags are embedded into a pull request description and automatically filled out by GitHub Copilot based on the code which the developers changed. Going one step further, the GitHub Next team is also looking at the creation of descriptive sentences and paragraphs as developers create pull requests.
  • Code reviews with AI. Taking pull requests and code reviews one step further, the GitHub Next team is looking at AI to help review the code and provide suggestions for changes. This will help enable human interactions and optimize existing processes. The AI would automate the creation of the descriptions, based on the code changes, as well as suggestions for improvements. The code reviewer will have everything they need to quickly review the change and decide to either move forward or send the change back.

When these capabilities are production ready, development teams and compliance programs will appreciate these features for a few reasons. First, the pull request and code review process would be driven by a conversation based on a neutral and independent description. Second, the description will be based on the actual code that was changed. Third, both development and compliance workflows will be optimized and allow humans to focus on value-added work.

While these capabilities are still a work in progress, there are features available now that may help enable compliance, audit, and security teams with GitHub Copilot for Business. The ability for developers to complete tasks faster and stay in the flow are truly amazing. But the ability for GitHub Copilot to provide AI-based security vulnerability filtering nowis a great place for compliance and audit teams within enterprises to get started on their journey to embracing generative AI into their day-to-day practices.

Next steps

Generative AI will enable developers and enterprises to achieve success by reducing manual tasks and enabling developers to focus their creativity on business value–all while staying in the flow.

I hope this blog will help drive positive discussions regarding this topic and has provided a forward looking view into what will be possible in the future. The future ability of generative AI to help enable teams by automating tedious tasks will help humans focus on more value-added work and could eventually be an important part of a robust and risk-based compliance posture.

 

Explore GitHub Copilot X >

What developers need to know about generative AI

Post Syndicated from Damian Brady original https://github.blog/2023-04-07-what-developers-need-to-know-about-generative-ai/

By now, you’ve heard of generative artificial intelligence (AI) tools like ChatGPT, DALL-E, and GitHub Copilot, among others. They’re gaining widespread interest thanks to the fact that they allow anyone to create content from email subject lines to code functions to artwork in a matter of moments.

This potential to revolutionize content creation across various industries makes it important to understand what generative AI is, how it’s being used, and who it’s being used by. In this article, we’ll explore what generative AI is, how it works, some real-world applications, and how it’s already changing the way people (and developers) work.

What is generative AI used for?

You may have heard the buzz around new generative AI tools like ChatGPT or the new Bing, but there’s a lot more to generative AI than any one single framework, project, or application.

Traditional AI systems are trained on large amounts of data to identify patterns, and they’re capable of performing specific tasks that can help people and organizations. But generative AI goes one step further by using complex systems and models to generate new, or novel, outputs in the form of an image, text, or audio based on natural language prompts.

Generative AI models and applications can, for example, be used for:

  • Text generation. Text generation, as a field, with AI tools has been in development since the 1970s—but more recently, AI researchers have been able to train generative adversarial networks (GANs) to produce text that models human-like speech. A prime example is OpenAI’s application ChatGPT, which has been trained on thousands of texts, books, articles, and code repositories, and can respond with full answers to natural language prompts and questions.
An example of text generation in ChatGPT
An example of text generation in ChatGPT
  • Image generation. Generative AI models can be used to create new images with natural language prompts, which is one of the most popular techniques with current tools and applications. The goal with text-to-image generation is to create an image that accurately represents the content of a given prompt. For example, when we give the text prompt, “impressionist style oil painting of a Shiba Inu dog giving a tarot card reading,” to the popular AI image generator DALL-E 2 we get something that looks like this (and yes, it’s a gem):
An AI-generated image from DALL-E 2 of a Shiba Inu dog giving a tarot card reading
An AI-generated image from DALL-E 2 of a Shiba Inu dog giving a tarot card reading

An example of a video created with a text prompt using diffusion models from [Imagen Video](https://imagen.research.google/).

  • Programming code generation. Rather than scouring the internet or developer community groups for help with code examples, generative AI models can be used to help generate new programming code with natural language prompts, complete partially written code with suggestions, or even translate code from one programming language to another. This is how, at a simple level, GitHub Copilot works: it uses OpenAI’sCodex model to offer code suggestions right from a developer’s editor. However, as you would with any software development tool, we encourage you to review generated code before merging into production.

  • Data generation. Creating new data—which is called synthetic data—and augmenting existing data sets is another common use case for generative AI. This involves generating new samples from an existing dataset to increase the dataset’s size and improve machine learning models trained on it, all while providing a layer of privacy since real user data is not being utilized to power models. Synthetic data generation provides a way to create useful, meaningful data for more than just ML training though—a number of self-driving car companies like Cruise and Waymo utilize AI-generated synthetic data for training perception systems to prepare vehicles for real-world situations while in operation.

  • Language translation. Natural-language understanding (NLU) models combined with generative AI have become increasingly popular to provide language translations on-the-fly. These types of tools help companies break language barriers and increase their scope of accessibility for customer bases by being able to provide things like support or documentation in their native language. Through complex, deep learning algorithms, generative AI is able to understand the context of a source text and linguistically construct those sentences in another language. This practice can also apply to coding languages, for example, translating a desired function from Python to Java.

The bottom line: Even though generative AI is a relatively new technology, it’s already being used in consumer and business applications. The use cases, as well as the quantity of applications created with it, will continue evolving to meet more distinct and specific needs.

How does generative AI work?

Generative AI models work by using neural networks to identify patterns from large sets of data, then generate new and original data or content.

But what are neural networks? In simple terms, they use interconnected nodes that are inspired by neurons in the human brain. These networks are the foundation of machine learning and deep learning models, which use a complex structure of algorithms to process large amounts of data such as text, code, or images. Training these neural networks involves adjusting the weights or parameters of the connections between neurons to minimize the difference between predicted and desired outputs, which allows the network to learn from mistakes and make more accurate predictions based on the data.

Algorithms are a key component of machine learning and generative AI models. But beyond helping machines learn from data, algorithms are also used to optimize accuracy of outputs and make decisions, or recommendations, based on input data.

While algorithms help automate these processes, building a generative AI model is incredibly complex due to the massive amounts of data and compute resources they require. People and organizations need large datasets to train these models, and generating high-quality data can be time-consuming and expensive.

To restate the obvious, these models are complicated. Need proof? Here are some common generative AI models and how they work:

  • Large language models (LLM): LLMs are a type of machine learning model that process and generate natural language text. One of the most significant advancements in the development of large language models has been the availability of vast amounts of text data, such as books, websites, and social media posts. This data can be used to train models that are capable of predicting and generating natural language responses in a variety of contexts. As a result, large language models have multiple practical applications, such as virtual assistants, chatbots, or text generators, like ChatGPT.

  • Generative adversarial networks (GAN): GANs are one of the most used models for generative AI, and they employ two different neural networks. GANs consist of two different types of neural networks: a generator and a discriminator. The generator network generates new data, such as images or audio, from a random noise signal while the discriminator is trained to distinguish between real data from the training set and the data produced by the generator.

During training, the generator tries to create data that can trick the discriminator network into thinking it’s real. This “adversarial” process will continue until the generator can produce data that is totally indistinguishable from real data in the training set. This process helps both networks improve at their respective tasks, which ultimately results in more realistic and higher-quality generated data.

A diagram illustrating how a generative adversarial network works. Image [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.en) האדם-החושב on wikipedia
A diagram illustrating how a generative adversarial network works. Image [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.en) האדם-החושב on wikipedia
  • Transformer-based models: A transformer-based model’s neural networks operate by learning context and meaning through tracking relationships of sequential data, which means these models are really good at natural language processing tasks like machine translation, language modeling, and answering questions. These models have been used in popular language models, such as GPT-4 (which stands for Generative Pre-trained Transformer 4), and have also been adapted for other such tasks that require the modeling of sequential data such as image recognition.
  • Variational autoencoder models (VAEs): These models are similar to GANs in that they work with two different neural networks: encoders and decoders. VAEs can take a large amount of data and compress it into a smaller representation, which can be used to create new data that is similar to the original data. VAEs are often used in image, video, and audio generation—and here’s a fun fact: you can train a VAE on datasets like CelebA, which contains over 200,000 images of celebrities, to create completely new portraits of people that don’t exist.
 The smile vector, a concept vector discovered by [Tom White](https://aiartists.org/tom-white) using VAEs trained on the CelebA dataset.
The smile vector, a concept vector discovered by Tom White using VAEs trained on the CelebA dataset.

The real-world applications of generative AI

The impact of generative AI is quickly becoming apparent—but it’s still in its early days. Despite this, we’re already seeing a proliferation of applications, products, and open source projects that are using generative AI models to achieve specific outcomes for people and organizations (and yes, developers, too).

Though generative AI is constantly evolving, it already has some solid real world applications. Here’s just a few of them:

Coding

New and seasoned developers alike can utilize generative AI to improve their coding processes. Generative AI coding tools can help automate some of the more repetitive tasks, like testing, as well as complete code or even generate brand new code. GitHub has its own AI-powered pair programmer, GitHub Copilot, which uses generative AI to provide developers with code suggestions. And GitHub also has announced GitHub Copilot X, which brings generative AI to more of the developer experience across the editor, pull requests, documentation, CLI, and more.

Accessibility

Generative AI has the potential to greatly impact and improve accessibility for folks with disabilities through a variety of modalities, such as speech-to-text transcription, text-to-speech audio generation, or assistive technologies. One of the most exciting facets of our GitHub Copilot tool is its voice-activated capabilities that allow developers with difficulties using a keyboard to code with their voice. By leveraging the power of generative AI, these types of tools are paving the way for a more inclusive and accessible future in technology.

Gaming

Generative AI can take gaming to the next level (get it? 😉) by generating new characters, storylines, design components, and more. Case in point: The developer behind the game, This Girl Does Not Exist, has said that every component of the game—from the storyline to the art and even the music—was generated entirely by AI. This use of generative AI can enable gaming studios to create new and exciting content for their users, all without increasing the developer workload, which frees them up to work on other aspects of the game, such as story development.

Web design

Designers can utilize generative AI tools to automate the design process and save significant time and resources, which allows for a more streamlined and efficient workflow. Additionally, incorporating these tools into the development process can lead to the creation of highly customized designs and logos, enhancing the overall user experience and engagement with the website or application. Generative AI tools can also be used to do some of the more tedious work, such as creating design layouts that are optimized and adaptable across devices. For example, designers can use tools like designs.ai to quickly generate logos, banners, or mockups for their websites.

Microsoft and other industry players are increasingly utilizing generative AI models in search to create more personalized experiences. This includes query expansion, which generates relevant keywords to reduce the number of searches. So, rather than the search engine returning a list of links, generative AI can help these new and improved models return search results in the form of natural language responses. Bing now includes AI-powered features in partnership with OpenAI that provide answers to complex questions and allow users to ask follow-up questions in a chatbox for more refined responses.

Healthcare

Interest has emerged around the potential applications of generative AI in the healthcare field to improve disease detection and diagnosis, advance medical research, and accelerate progress in the pharmaceutical space. Potentially, generative AI could be used to analyze large amounts of data to simulate chemical structures and predict new compounds will be the most effective for new drug discoveries. NVIDIA Clara is one example of a generative AI model specifically designed for medical imaging and healthcare research. (Plus, Gartner suggests more than 30 percent of new pharmaceutical drugs and materials will be discovered via generative AI models by 2025.)

Fun fact: Did you know that ChatGPT recently passed the US Medical Licensing exam without any intervention from clinicians?

Marketing and advertising

In marketing, content is king—and generative AI is making it easier than ever to quickly create large amounts of it. A number of companies, agencies, and creators are already turning to generative AI tools to create images for social posts or write captions, product descriptions, blog posts, email subject lines, and more. Generative AI can also help companies personalize ad experiences by creating custom, engaging content for individuals at speed. Writers, marketers, and creators can leverage tools like Jasper to generate copy, Surfer SEO to optimize organic search, or albert.ai to personalize digital advertising content.

Art and design

As we’ve seen above, the power of AI can be harnessed to create some incredible portraits in a matter of moments (re: the future-telling Shiba 🐕). Artists and designers alike are using these AI tools as a source of inspiration. For example, architects can quickly create 3D models of objects or environments and artists can breathe new life into their portraits by using AI to apply different styles, like adding a Cubist style to their original image. Need proof? Designers are already starting to use AI image generators, such as Midjourney and Microsoft Designer, to create high-quality images by simply typing out Discord commands.

Finance

In a recent discussion about tech trends and how they’ll affect the finance sector, Michael Schrage, a research fellow at the MIT Sloan School Initiative on the Digital Economy, said, “I think, increasingly, we’re going to be seeing generative AI used for financial forecasts and scenario generation.” This is a likely path forward—generative AI can be used to analyze large amounts of data to detect fraud, manage risk, and inform decision making. And that has obvious applications in the financial services industry.

Manufacturing

Manufacturers are starting to turn to generative AI solutions to help with product design, quality control, and predictive maintenance. Generative AI can be used to analyze historical data to improve machine failure predictions and help manufacturers with maintenance planning. According to research conducted by Capgemini, more than half of European manufacturers are implementing some AI solutions (although so far, these aren’t generative AI solutions). This is largely because the sheer amount of manufacturing data is easier for machines to analyze at speed than humans.

AI as a partner: Generative AI models and tools are narrow in focus, and work best at generating content, code, and images. In research at GitHub, we’ve found that GitHub Copilot helps developers code up to 55% faster, underscoring how generative AI models and tools can improve overall productivity and boost efficiency. Metrics like these show how generative AI tools are already changing how people and teams work—but they also underscore how these tools act as complement to human efforts.

Take this with you

Whether it’s creating visual assets for an ad campaign or augmenting medical images to help diagnose diseases, generative AI is helping us solve complex problems at speed. And the emergence of generative AI-based programming tools has revolutionized the way developers approach writing code.

We know that developers want to design and write software quickly, and tools like GitHub Copilot are enabling them to access large datasets to write more efficient code and boost productivity. In fact, 96% of developers surveyed reported spending less time on repetitive tasks using GitHub Copilot, which in turn allowed 74% of them to focus on more rewarding work.

While these models aren’t perfect yet, they’re getting better by the day—and that’s creating an exciting immediate future for developers and generative AI.

8 things you didn’t know you could do with GitHub Copilot

Post Syndicated from Rizel Scarlett original https://github.blog/2022-09-14-8-things-you-didnt-know-you-could-do-with-github-copilot/

Similar to other AI pair programming tools, GitHub Copilot is changing the game of software development. GitHub Copilot is an AI pair programmer that helps you write code faster with less work. We use the terms “AI pair programmer” and “Copilot” to imply that this tool cannot work without you–the developer! It’s not magic. It cannot read minds, although it sometimes feels like it can. However, by sharing code completion suggestions based on my project’s context and style conventions, GitHub Copilot increased my velocity and confidence as a programmer.

The best part is you can use GitHub Copilot to increase your velocity and confidence as you code, too! In June 2022, we made GitHub Copilot available to all individual developers. You can learn how to get started with GitHub Copilot here.

If it’s not reading minds and it’s not magic, then how does it work?

Open AI Codex, a machine learning model that translates natural language into code, powers GitHub Copilot to draw context from comments and code to suggest individual lines and whole functions as you type. Codex is a version of GPT-3 (Generative Pre-trained Transformer 3) fine-tuned for programming tasks. Some of your favorite applications, like Duolingo, use GPT-3 for grammar correction.

For more information on how GitHub Copilot works and its effectiveness, check out the following resources:

Unexpected yet valuable GitHub Copilot use cases

Once installed, the extension will suggest code as you type, but what next? How can you optimally benefit from the GitHub Copilot extension?

First, I recommend writing clear, understandable comments to help your AI pair programmer generate desired solutions, but if you’re interested in exploring how to use GitHub Copilot in ways you might not be thinking of, we compiled some fun and valuable use cases we’ve seen in talking with developers. I hope that the following examples inspire you!

1. Assisting non-native English speakers

GitHub Copilot can understand other languages beyond English! This is helpful for developers of all backgrounds because programming languages are based on American English. For example, the CSS property color is based on American English, so it is unfamiliar for native British-English or Canadian-English speakers who use the spelling ‘colour’. Forgetting the correct spelling and syntax can often result in typos, unexpected errors, and lost time.

In the GIF below, I wrote a comment in Spanish that says, “importar,” which translates to “import.” GitHub Copilot quickly completed my comment in Spanish and imported the necessary libraries as the comment described.

Demonstration of GitHub Copilot interpreting a comment in Spanish.

Additionally, GitHub Copilot helps translate words from English to other languages. MilMikDev on Twitter used GitHub Copilot to translate an array of words ‘answer’, ‘question,’ and ‘date’ to various languages.

Demonstration of using GitHub Copilot to translate an array of words ‘answer’, ‘question,’ and ‘date’ to various languages.

2. Creating dictionaries with lookup data

Martin Woodward, Vice President of Developer Relations at GitHub, shared this tip with us! GitHub Copilot is great at creating dictionaries of lookup data. Try it out by writing a comment instructing GitHub Copilot to create a dictionary of two-letter ISO country codes and their contributing country name. Writing a comment and the first few lines of code should help GitHub Copilot generated the desired results. See the GIF below for visual representation!

Demonstration of using GitHub Copilot to create a dictionary of two-letter ISO country codes and their contributing country name.

3. Testing your code

Writing tests is a vital yet sometimes tedious step in the software development lifecycle. Because GitHub Copilot excels in pattern recognition and pattern completion, it can speed up the process of writing unit tests, visual regression tests, and more.

Check out these resources to learn more about using GitHub Copilot for testing:

4. Matching patterns with regular expressions

With GitHub Copilot, you can spend less time fiddling in a Regex playground or combing through StackOverflow to match character combinations in strings. Instead, you can write a comment or a function name to trigger GitHub Copilot’s suggestions.

I used Copilot to help me validate a phone number!

Demonstration of using GitHub Copilot to validate a phone number.

GitHub Copilot can help you remove white space from a string!

Demonstration of using GitHub Copilot to remove white space from a string.

5. Preparing for technical interviews

While this may sound unorthodox, developers, including myself, use GitHub Copilot to study for interviews.

Here’s the strategy:

  • First, I try to solve the problem without GitHub Copilot.
  • If I’m feeling extremely stuck and disheartened while solving the problem, I’ll activate GitHub Copilot and use it to generate ideas on how to solve the problem better.
  • Subsequently, I’ll delete the code GitHub Copilot generated, deactivate GitHub Copilot, and make another attempt at finding a solution with the new information in mind.

By adopting this method, I maintain momentum when tempted to quit. Instead of giving up, I gain new perspectives even when I don’t have a mentor or peer to guide me. GitHub Copilot becomes my digital mentor. But, note, that I highly discourage activating GitHub Copilot during an interview (that’s cheating!).

Interestingly, chess players take a similar approach when preparing for matches. It’s common for chess players to practice against AI engines to advance their skills. In the publication, Towards Data Science, Bharath K writes, “Artificial Intelligence has influenced the way in which chess games are played at the top level. Most of the Grandmasters and Super Grandmasters (rated at a FIDE above 2700) utilize these modern AI chess engines to analyze their games and the games of their competitors.” Learn more about the influence of AI chess engines here.

If AI is helping chess players advance their skills, perhaps it can positively impact developers’ problem-solving skills by challenging them to think differently about solving a problem.

You can learn more about leveraging GitHub Copilot to solve algorithmic problems here. In the example below, I wrote a comment that says, “write a binary search algorithm,” and the first line of my function. GitHub Copilot correctly completed the function.

Screenshot of a code editor demonstrating that GitHub Copilot correctly completed a function based on the input of a comment and the first line of the function.

6. Sending tweets

Of course, you can simply use the Twitter application to send a Tweet, but it’s more fun to send a Tweet via your IDE! As a Developer Advocate, part of my job is to create demos. In a recent livestream, I had to demonstrate using Twitter API v2 with GitHub Copilot in Python, a language that I rarely use. GitHub Copilot saved the day and generated the code I needed after I wrote a few comments. Read my DEV post if you want to try sending a tweet with GitHub Copilot, too!

Screenshot of a tweet from Rizel Scarlett that reads, "I wrote this tweet with Copilot and I'm in KCDC right now."

7. Exiting Vim

Developers who are new to Vim frequently wonder how to exit the editor. Struggling to exit vim is so common that it’s a meme on the internet! Since GitHub Copilot is available in Visual Studio Code, JetBrains, and Neovim, a forked version of Vim with additional features, you can exit NeoVim using GitHub Copilot. In the video below, Brian Douglas uses GitHub Copilot to exit NeoVim, by writing a comment that says, “How do I exit vim?”

8. Navigating a new codebase with Copilot Labs

GitHub Copilot Labs is a complementary extension that comes with GitHub Copilot access. The GitHub Next team developed GitHub Copilot Labs, an experimental sidebar, to help developers translate code from one programming language to another and get a step-by-step explanation of code snippets.

There’s no easy method for building a mental model of a new codebase, but these two features within GitHub Copilot Labs can help. By translating code snippets to languages they’re more familiar with and using the ‘Explain’ feature to gain an understanding of the code, developers can better comprehend complex blocks of code.

Demonstration of using Copilot's ‘Explain’ feature to gain an understanding of the code, developers can better comprehend complex blocks of code.

Closeup look at the results of Copilot's 'Explain' feature being used.

Wrap-up

As you’ve seen in the examples above, GitHub Copilot can help you be more productive in so many ways day to day (a lot of which we’re still discovering!), and I want to kindly remind you that GitHub Copilot is an AI pair programmer, so just as you would with your coworkers’ code or even your own, review the generated code before pushing it to production! While GitHub Copilot is powerful, sometimes it makes mistakes or refers to an outdated version of an API. Nevertheless, the team at GitHub continues to work hard and learn from our users to develop a better experience and generate better results with GitHub Copilot. I’m excited to witness and experience the evolution of AI pair programming tools. If you’re just as eager as me, sign up for GitHub Copilot today!