Excitement is in the air as we gear up for our most community-driven gathering of the year! With just weeks to go until our biggest developer event, the anticipation is building. ✨ As one attendee from last year said, “This is actually my first time at Universe and it has been awesome. Not only the event itself and how it looks and feels, but the people and the energy. Everyone is really excited to do really nerdy things, and that’s been my favorite.”

We’re thrilled to welcome you to the Fort Mason Center in San Francisco on October 29-30 for a milestone celebration—10 years of GitHub Universe. This is not just a look back at how far the software development industry has come, but a chance to shape what comes next, together. Plus, be the first to hear about GitHub’s newest innovations and biggest ships of the year, straight from our CEO at the day 1 keynote.

In this blog, you’ll get a sneak peek at some of the exciting activities, networking opportunities, and interactive learning experiences we have lined up for you over two incredible days. And trust us: we have more surprises in store! If you haven’t already secured your tickets, now is the time—don’t miss out on connecting with fellow developers, gaining fresh insights, and immersing yourself in the vibrant GitHub community. Tickets are selling fast, so grab yours today for an experience you won’t forget.

Let’s dive right in.

🗺️ Map out your sessions

With over 100 sessions to choose from, it’s a good idea to explore the agenda and pick out the talks, workshops, and events that excite you the most. Once you’re logged into your attendee portal, use our schedule builder tool to “favorite” your must-attend sessions and create your own customized schedule.

If sorting through all the sessions feels overwhelming, don’t worry—we’ve got you covered! Take a look at our curated agendas tailored to your experience level, industry, and topics of interest. We even have agendas from influential community leaders like Brian Douglas, founder of OpenSauced, for you to follow along with.

🕹️ Fuel your curiosity with innovation and play

In between sessions, Universe ‘24 will have interactive zones to help inspire your creativity and challenge your thinking. Here are four activations you definitely don’t want to miss:

Open source zone

Visit the open source zone and explore live demos from rising stars in the GitHub Accelerator program, and connect with leaders from the Maintainer Community and passionate opensource creators from around the world. With projects like Home Assistant, Kubernetes, and Node featured across the two days, this is your chance to witness firsthand the transformative power of open source.

GitHub Copilot Extensions demo

For an interactive tech experience, head to the GitHub Copilot Extensions demo! Explore the endless possibilities of extensions—whether you’re browsing the GitHub Marketplace for ready-made extensions or learning how to build your own. The Copilot Extensions team will be on hand to help you unlock the full potential of what you can create. Don’t miss this chance to power up your coding experience!

Logitech activation

Curious about what it means to get into your flow state? Then visit the Logitech activation! Using an EEG headset and biometric data, the Logitech team will analyze your brain activity and create a personalized digital visualization of your unique brainwave patterns when you’re in peak productivity. It will be a mind-bending experience that combines science and art to show you what your flow looks like.

Hack your badge station

Unleash your creativity at the hack your badge station, where you can personalize your GitHub Universe badge with hands-on tutorials and guidance. Every in-person Universe ticket comes with a hackable badge, and here’s your chance to make it uniquely yours. Whether you’re a seasoned badge hacker or a first-timer, dive into tutorials and guidance to personalize your badge with flair. It’s a fun way to show off your skills and take home a one-of-a-kind keepsake from GitHub Universe!

♥️ Explore the heart of Universe

Next, find GitHub Central—your hub for discovering the latest GitHub features and networking with your peers. Here, you’ll see interactive product demos and zones for each content track—AI, the developer experience, and security—all with access to engage directly with GitHub experts.

While you’re there, get competitive in our fast-paced Bug Bash game or pick up a one-of-a-kind postcard generated from your contributions over the years. Plus, take advantage of networking moments at the “meet the Universe speakers” area and the Stars Lounge—connecting with GitHub’s top thought leaders and influential community members.

You also won’t want to miss going to The GitHub Shop. Whether you’re after the latest Universe apparel, unique GitHub Copilot collectibles, or our beloved invertocat hoodies, the shop has something for every GitHub fan—including an all-new collection. (Plus, the famous ugly holiday sweater is making a comeback with a brand new design!)

If you’re one of the first 1,000 visitors, you’ll receive a special gift with your purchase. So, come early and snag your favorite items before they’re gone! Get a sneak peek of the new collection below:

🤝 Meet the minds behind GitHub

Connecting with experts is easier than ever with key areas designed to spark conversations, share insights, and offer personalized guidance. Whether you’re looking for technical support, future-forward ideas, or startup wisdom, we’ve got you covered.

• Start at the GitHub Expert Center, where specialists are on hand to help you master every corner of GitHub’s offerings. From optimizing workflows with GitHub Actions to scaling Copilot or enhancing security practices with GitHub Advanced Security, this is the go-to place for tailored advice. This year, you can even schedule a slot ahead of time, making it easy to get one-on-one time with the pros in the areas that matter most to you.
• For a glimpse into the future of software development, visit GitHub Next, where the team behind Copilot and GitHub Copilot Workspace will share their latest prototypes. Engage with cutting-edge tools and get a sneak peek at how GitHub is pushing the boundaries of development.
• If you’re an entrepreneur or startup enthusiast, the Startup Lounge is where you’ll want to be. Network with founders, share ideas, and collaborate with fellow builders as you explore ways to grow and scale your startup. It’s the perfect space to gain insights and make connections that could shape the future of your business.

↗️ Level up your career

Take your career to the next level at Universe with a range of opportunities designed to help you grow and stand out! Start by visiting the Career Corner, where you can engage in private consultations with talent experts. Whether you need help refining your resume, optimizing your LinkedIn and GitHub profiles, or getting tips for your next big interview, these one-on-one sessions offer personalized guidance to boost your career search and set you up for success.

Looking to sharpen your technical skills? Don’t miss our workshops, led by industry experts, where you can gain hands-on experience and enhance your knowledge in key areas of development. And for those ready to validate their expertise, our on-site certification testing allows you to become GitHub-certified in areas like GitHub Foundations, GitHub Actions, GitHub Advanced Security, and even GitHub Copilot.

You can still add a workshop or on-site certification testing to your Universe ticket. Don’t miss your chance to showcase your skills and stand out from the crowd!

🌍 Your Universe experience awaits

As we look forward to celebrating 10 years of GitHub Universe, prepare for a wealth of knowledge, connections, and inspiration. This event will mark the continuation of your journey—the skills, insights, and relationships you build here will benefit your career and projects well beyond the two days you spend with us. We’re so excited to see you forge new paths and innovate beyond your limits!

If you haven’t secured your place yet, it’s not too late to get tickets now and join us in person at San Francisco’s Fort Mason Center for our biggest event yet! Be sure to follow along on the blog and our social media channels in the coming weeks for post-event recaps, including highlights and key takeaways from Universe ’24.

The post Career growth, learning, and fun, oh my! Your guide to GitHub Universe 2024 appeared first on The GitHub Blog.

In today’s rapidly evolving landscape, where AI is reshaping industries and transforming workflows, the role of developers has never been more critical. As business leaders, fostering an environment where developers feel valued, motivated, and empowered is essential to harnessing their full potential and keeping your business profitable and innovative.

In this blog post, we’ll explore actionable tips and strategies to supercharge developer happiness, ensuring your team remains productive, engaged, and ahead of the AI curve. We’ll walk you through ways to secure your code with AI, how to increase productivity with a strong developer experience, and, of course, invite you to join us at GitHub Universe 2024 to see the very best of the latest AI tooling in action.

Boost productivity with a great developer experience

Developer experience is more than just a buzzword—it’s a critical factor in driving productivity and collaboration within software development teams. A seamless developer experience allows developers to get into the flow state more easily, where their productivity and creativity can peak. This flow state—characterized by uninterrupted concentration and a deep sense of involvement in the task—is crucial for tackling complex coding challenges.

This work environment needs to be built intentionally, and the research backs it up. Developers who carve out time for deep work enjoy 50% more productivity, while those that get work they find engaging are 30% more productive.

How does this impact businesses? Well, because a developer that can significantly reduce their context-switching and mental load can also produce code faster and at a higher quality.

When developers understand their code, they’re 42% more productive. When developers are able to get faster turnaround times, they are 20% more innovative. These are tangible, individual benefits that in turn directly impact the output of developer teams.

Now is the time for leaders to invest in creating a great developer experience. By prioritizing the developer experience, you’re setting your team up to harness the full potential of the latest AI and platform engineering advances, ensuring your business stays ahead of the curve. Curious to learn more? Then dive into how a great developer experience fuels productivity with our latest research.

Use AI to secure your code

Historically, developers and security teams have found themselves at odds due to competing business goals. Shifting security left incorporates security earlier in the software development lifecycle, but in practice it has primarily shifted responsibility to developers without necessarily giving them the required expertise.

This, combined with the context switching inherent in development work, makes addressing security concerns particularly challenging. With AI, developers now have powerful tools at their disposal to enhance code security. AI can:

• Improve detection rates
• Provide near-instant fixes with context
• Enable application security (AppSec) at scale

These three improvements make it easier for developers to integrate robust security measures without sacrificing productivity, and transform the relationship between developers and security teams into a collaborative partnership.

Introducing a new security tool doesn’t have to be a daunting task either. By following a few simple steps, organizations can ensure a smooth transition and broad adoption.

• Document the tool’s features and usage to set the foundation and set realistic expectations to help align goals across teams.
• Recognize and celebrate successes to showcase the value of the new tool.
• Adopt a go-with-the-flow approach and organize hackathons to further drive engagement and interest.
• Listen to developer feedback continuously improve and refine security practices.

AI-powered security tools not only enhance the efficiency and effectiveness of AppSec, but also empower developers to take a proactive role in securing their code. This shift not only improves overall security posture, but also fosters a culture of shared responsibility and continuous learning, ultimately leading to more secure and resilient applications.

See exactly why security should be built into the developer workflow. 👇

Customize your LLMs

Organizations that take AI a step further and customize their AI tools are poised to lead the pack.

Large language models (LLMs) are trained on vast amounts of text data and can perform a variety of natural language processing tasks like translation, summarization, question-answering, and text generation. Customizing a pre-trained LLM goes beyond mere training—it involves adapting the model to perform specific tasks relevant to the organization’s needs. This level of customization helps developers maintain their flow state and significantly boost productivity and efficiency.

Customization techniques like retrieval-augmented generation (RAG), in-context learning, and fine-tuning enable LLMs to deliver more accurate and contextually appropriate responses:

• RAG combines retrieval-based and generation-based approaches in natural language processing. It enhances LLMs by integrating information retrieval techniques, where relevant documents or snippets are retrieved from a vector database to assist in generating more accurate and contextually appropriate responses. This approach allows the model to access and utilize external knowledge, making the generated output more informed and relevant to the user’s query.
• In-context learning refers to a model’s ability to adapt and respond to new tasks or inputs based on the context provided in the input prompt without requiring additional training. The model leverages its pre-trained knowledge and the context given in the input to perform tasks effectively.
• Fine-tuning, on the other hand, is a process in which an LLM is further trained on a specific dataset to adapt it to a particular task or domain. During fine-tuning, the model’s parameters are adjusted based on the new dataset, which typically involves supervised learning with labeled data. This process allows the model to specialize and improve its performance on specific tasks, (such as text classification, question answering, or machine translation), by leveraging the general knowledge acquired during its initial pre-training phase.

By implementing these customization strategies, businesses can unlock the full potential of their AI tools. Customized LLMs not only improve developer productivity—they also enhance the quality and relevance of AI-generated content.

Prepare your repository for teamwork

Fostering collaboration doesn’t just make software development faster, it also helps teams build better products and boost job satisfaction. By making your repository as collaborative as possible, you’ll optimize success. This includes focusing on:

• Repository settings: properly configuring repository settings to control visibility, access, and contribution workflows lays the foundation for collaboration.
• Repository contents: including essential files like README.md, LICENSE.md, CONTRIBUTING.md, CODEOWNERS, and CODE_OF_CONDUCT.md helps collaborators understand the project, its purpose, and how to contribute.
• Automation and checks: implementing automation tools such as linters, continuous integration (CI), and continuous deployment (CD) pipelines streamlines the development process, ensures code quality, and enables immediate feedback.
• Security practices: enforcing role-based access control, managing secrets securely, and scanning code for vulnerabilities can foster trust and protect the project from vulnerabilities.
• Issue templates: providing structured issue templates guides contributors in providing necessary information and context when reporting bugs.
• Community engagement: engaging with the project’s community through meetups, project blogs, discussions, and other channels fosters belonging and builds relationships.

Invest in your team’s learning opportunities

When you signal to your team that you value their career growth and exposure to learning opportunities, it can boost happiness and job satisfaction, leading to increased productivity, collaboration, and better problem solving.

Encouraging your developer teams to attend conferences like GitHub Universe 2024 is a strategic investment in their professional growth and your business’ success. Our global developer event provides an unparalleled platform for the best in software development to gather and expand their knowledge, stay updated on the latest AI-powered tools, and bring fresh ideas back to their teams.

Here are a few highlights of what you and your team can expect:

• Help your developers get in the flow and stay there with sessions, demos, panels, and more on the powerful tools and techniques that enhance productivity and satisfaction.
• Connect with other technical leaders to share experiences, challenges, and best practices. Expand your network with valuable industry contacts.
• Get a first look at GitHub’s product roadmap and see how upcoming features and enhancements can help you stay ahead in a competitive landscape.
• Gain technical skills with GitHub certifications and workshops designed to enhance your expertise in a rapidly evolving industry.
• Learn the latest on GitHub Copilot and stay ahead with the latest coding practices and techniques.

Get your tickets today. You can take advantage of our group discount and get four tickets for the price of three. (That’s a 25% savings!)

If you’re flying solo, you can also use our Early Bird discount and save 20% off one in-person ticket, only until September 3.

Reach new levels of creativity and efficiency

Incorporating these five business strategies can transform your development process and increase developer happiness. By investing in these areas, you empower your team, foster a culture of continuous learning, and position your organization for success in the rapidly evolving tech landscape.

• Get tickets to GitHub Universe 2024
• Read more about how DevEx boosts productivity and innovation
• Customize GitHub Copilot for your business

The post The ultimate guide to developer happiness appeared first on The GitHub Blog.

The world of software development is constantly evolving. That means whether you’re a seasoned developer or just starting out on your coding journey, there’s always something new to learn.

Below, we’ll explore five actionable tips to take your career to the next level. From mastering prompt engineering to harnessing the power of AI for code security, these tips will help you learn the skills and uncover the knowledge you need to excel in today’s competitive job market.

Tip #1: Become a pro at prompt engineering

In the age of AI, you can use AI tools like GitHub Copilot to code up to 55% faster. But like any other tool or skill, our AI pair programmer has a learning curve, and there are certain techniques you can use that will make your work with AI even more effective. Enter prompt engineering. With prompt engineering, you provide GitHub Copilot with more context about your project—which yields better, more accurate results. Below are three best practices for crafting prompts for GitHub Copilot:

Open related files in VS Code while using GitHub Copilot

While you can begin using GitHub Copilot with a blank file, one easy way to introduce more context is to open related files in VS Code. Known as neighboring tabs, this technique enables Copilot to gain a deeper understanding of your code by processing all open files in your IDE.

This broader scope allows Copilot to identify matching code segments across your project, enhancing its suggestions and code completion capabilities.

Provide a top-level comment in your code file

Imagine being assigned a task with little to no context—that would make accomplishing it much more difficult, right? The same can be said for GitHub Copilot. When you add a brief, top-level comment in your code file, it helps Copilot understand the overarching objective before getting into the how.

Once you’ve broken down the ask and your goal, you can articulate the logic and steps required to achieve it. Then, allow Copilot to generate code incrementally, rather than all at once. This approach enhances Copilot’s understanding and improves the quality of the generated code.

Input sample code

Offer GitHub Copilot a snippet of code that closely resembles what you need. Even a brief example can further help Copilot craft suggestions tailored to your language and objectives!

Tip #2: Learn shortcuts and hacks

GitHub is full of shortcuts and hacks that make your work life easier and help you stay in the flow. Gain momentum in your projects and increase your productivity with these popular shortcuts:

Search for any file in your repositories

When you’re searching through repositories, type the letter “t” on your keyboard to activate the file finder and do away with hours of wasted time! See how in the video below:

Link your pull requests to your issues

Did you know that GitHub also has project management tools? One of them is a handy interlinking feature that allows you to link pull requests and Git commits to relevant issues in a project. This facilitates better organization, collaboration, and project management, not just for you, but for anyone looking for more context in your issue. Gone are the days of hunting down old issues every time you create a new pull request!

Create custom actions

Creating custom actions on GitHub enables you to enhance code reuse, bypass repetition, and simplify maintenance across multiple workflows. All you have to do is outline the necessary steps for a particular task and package them into an action using any supported programming or scripting language, and you’re all set!

Incorporate feedback in pull requests

Ever wish there was an easier way to review code? Well, it’s possible! Add comments directly to the pull request, propose changes, and even accept and add those suggestions seamlessly to make code reviews easier than ever. You can also save your replies by heading over to the comment box in an open pull request and selecting “create new saved reply,” and then “add saved reply,” to make it official.

Tip #3: Brush up on your soft skills

AI has introduced a host of hard skills that developers need to master in order to keep up with the latest tooling. Soft skills complement your new technical expertise and can contribute to your overall success by enhancing communication, collaboration, and problem-solving. Here are a few important ones to practice:

Communication

As you know, developer work rarely happens in a vacuum. Strong communication skills can facilitate clear understanding and efficient collaboration for both humans and AI tools, whether you’re collaborating with stakeholders, communicating complex technical concepts to non-technical audiences, or working on your prompt engineering.

Problem-solving

Critical thinking enables developers to approach complex challenges creatively, break them down into manageable tasks, and find innovative solutions with the help of AI coding tools.

Adaptability

AI coding tools are evolving rapidly, with new technologies, methodologies, and tools emerging regularly. Being adaptable allows developers to stay current, learn new skills quickly, and stay nimble as things change. To cultivate resilience and embrace discomfort (in and outside of the workplace), engage in activities that challenge you to anticipate and respond to the unexpected.

Ethics

Being aware of the ethical implications associated with these tools is essential. Developers should understand both the capabilities and limitations of AI coding tools and exercise critical thinking when interpreting responses from them. By remaining conscious of ethical considerations and actively working toward ethical practices, developers can ensure that these tools are used responsibly.

Empathy

Empathy is crucial for understanding the needs, preferences, and challenges of end-users. Empathy also fosters better collaboration within teams by promoting understanding and respect for colleagues’ perspectives and experiences.

Tip #4: Use AI to secure your code

Developers can leverage AI to enhance code security in several ways. First, AI can help prevent vulnerabilities by providing context and secure code suggestions right from the start. Traditionally, “shift left” meant getting security feedback after coding (but before deployment). By utilizing AI as a pair programmer, developers can “shift left” by addressing security concerns right where they bring their ideas to code.

A common pain point for developers is sifting through lengthy pages of alerts, many of which turn out to be false positives—wasting valuable time and resources. With features like code scanning autofix, AI and automation can step in to provide AI-generated code fixes alongside vulnerability alerts, streamlining remediation directly into the developer workflow. Similarly, secret scanning alerts developers to potential secrets detected in the code.

AI also presents an opportunity to improve the modeling of a vast array of open-source frameworks and libraries. Traditionally, security teams manually model numerous packages and APIs. This is a challenging task given the volume and diversity of these components, along with frequent updates and replacements. By infusing AI in modeling efforts, developers can increase the detection of vulnerabilities.

Tip #5: Attend GitHub Universe 2024

Attending conferences is a valuable investment in a developer’s career, providing opportunities for learning, networking, skill development, and professional growth all at the same time. GitHub Universe is our flagship, global event that brings together developers, leaders, and companies for two days of exploring the latest technologies and industry trends with fun, food, and networking in between. Here are some of the highlights:

100+ sessions on AI, DevEx, and security

Learn about frameworks and best practices directly from 150+ experts in the field through keynotes, breakout sessions, product demos, and more.

Gain and practice new skills

Git official by signing up for an interactive workshop or getting GitHub certified in GitHub Actions, GitHub Advanced Security, GitHub Foundations, or GitHub Administration. It’ll certainly look great on your resume and LinkedIn. 😉

Visibility

Sharing insights, presenting research findings, or showcasing projects can help developers establish themselves as thought leaders and experts in their field. The Universe call for sessions is open from now until May 10. Submit a session proposal today!

Professional development

Show your commitment to your career and continuous learning by visiting the dedicated Career Corner for professional development.

Community engagement

Build your network and find opportunities for collaboration and mentorship by engaging with peers and participating in the Discussions Lounge.

Learn more about our content tracks and what we have in store for the 10th anniversary of our global developer event.

Navigate your career with confidence

By implementing the strategies outlined above, you’ll be well-equipped to unlock your dream career in 2024 and beyond. And remember: you can take your skills to the next level, network with industry leaders, and learn how to use the latest AI tools at GitHub Universe 2024.

The post 5 tips to supercharge your developer career in 2024 appeared first on The GitHub Blog.

September 5, 2024 update: the full GitHub Universe agenda is now live.

Imagine arriving at a conference and immediately feeling inspired: your agenda is packed with must-see GitHub Copilot sessions, booths are filled with experts from top tech companies, and you’re surrounded by thousands of fellow developers and leaders eager to connect.

That is the experience we’re curating to celebrate 10 years of our global developer event. This year, we’re going bigger and better with a stunning new venue as the foundation. We hope you’ll join us at the Fort Mason Center for Arts & Culture on the San Francisco Bay, from October 29-30, or virtually from anywhere in the world.

As the world’s fair of software, GitHub Universe 2024 will be an unparalleled gathering of the brightest minds, companies, and innovators in the industry. With sessions diving into AI, the developer experience (DevEx), and security, attendees will have an opportunity to explore the latest products, best practices, and insights shaping the future of software development.

Ready to be a part of this milestone event with us? In-person tickets are currently 20% off with our Early Bird discount, only from now until September 3.

Universe 2024: Where innovation meets fun, food, and connection 🎨

We take your experience as a Universe attendee very seriously. From the moment you step through the colorful gates right down to the beverages we serve, our event will blow your expectations out of the water.

Spread across a sprawling 13-acre waterfront compound, Universe will unfold across seven buildings and various outdoor areas.

With five stages hosting more than 100 sessions and 150 speakers, alongside a record-breaking 3,500 attendees (that’s over 50% more in-person attendees than last year!), this will be our biggest Universe yet.

During breakfast and lunch, you’ll indulge in food trucks, snacks, and beverages—all included in the price of your in-person ticket. And don’t forget to explore the GitHub Shop for the latest Universe swag and join us for lively happy hours sponsored by our partners.

We’re also excited to offer students a chance to connect with GitHub employees through virtual micro-mentoring sessions ahead of GitHub Universe 2024. As a part of the GitHub Social Impact programming, join us for 30-minute 1:1 sessions from October 21 to November 1 where industry experts will provide personalized resume feedback, career advice, or skill development tips. Make sure to apply by September 18 for a chance to participate in this unique opportunity and get mentorship from the best in the business!

Everything you’ll learn at our global developer event 🧠

Attending Universe is an investment in your business and your career. It’s easier than ever to be in charge of your growth with our beginner, intermediate, and advanced session topics curated to what developers and enterprises care about most.

As an in-person attendee, you’ll also be able to take advantage of two ticket add-ons: GitHub Certification testing and workshops, available onsite! Take what you learn during your sessions and practice them IRL alongside your industry peers.

You can secure your spot for workshops and certifications when you purchase your in-person ticket. Don’t miss out—these opportunities will go fast!

Here’s a sneak peek of some can’t-miss sessions to get you started.

AI content track 🤖

DevEx content track ⚙️

Security content track 🔐

Will you celebrate 10 years of GitHub Universe with us? 🤗

Whether you’re a leader interested in connecting with and learning from other industry executives, a manager hoping to propel your team’s productivity to new heights, or a developer looking to acquire new skills and further your career, Universe has something for you.

The post The world’s fair of software: Join us at GitHub Universe 2024 appeared first on The GitHub Blog.

Generative AI coding tools are changing software production for enterprises. Not just for their code generation abilities—from vulnerability detection and facilitating comprehension of unfamiliar codebases, to streamlining documentation and pull request descriptions, they’re fundamentally reshaping how developers approach application infrastructure, deployment, and their own work experience.

We’re now witnessing a significant turning point. As AI models get better, refusing adoption would be like “asking an office worker to use a typewriter instead of a computer,” says Albert Ziegler, principal researcher and member of the GitHub Next research and development team.

In this post, we’ll dive into the inner workings of AI code generation, exploring how it functions, its capabilities and benefits, and how developers can use it to enhance their development experience while propelling your enterprise forward in today’s competitive landscape.

How to use AI to generate code

AI code generation refers to full or partial lines of code that are generated by machines instead of human developers. This emerging technology leverages advanced machine learning models, particularly large language models (LLMs), to understand and replicate the syntax, patterns, and paradigms found in human-generated code.

The AI models powering these tools, like ChatGPT and GitHub Copilot, are trained on natural language text and source code from publicly available sources that include a diverse range of code examples. This training enables them to understand the nuances of various programming languages, coding styles, and common practices. As a result, the AI can generate code suggestions that are syntactically correct and contextually relevant based on input from developers.

Favored by 55% of developers, our AI-powered pair programmer, GitHub Copilot, provides contextualized coding assistance based on your organization’s codebase across dozens of programming languages, and targets developers of all experience levels. With GitHub Copilot, developers can use AI to generate code in three ways:

1. Type code and AI can autocomplete the code

Autocompletions are the earliest version of AI code generation. John Berryman, a senior researcher of ML on the GitHub Copilot team, explains the user experience: “I’ll be writing code and taking a pause to think. While I’m doing that, the agent itself is also thinking, looking at surrounding code and content in neighboring tabs. Then it pops up on the screen as gray ‘ghost text’ that I can reject, partially accept, or fully accept and then, if necessary, modify.”

While every developer can reap the benefits of using AI coding tools, experienced programmers can often feel these gains even more so. “In many cases, especially for experienced programmers in a familiar environment, this suggestion speeds us up. I would have written the same thing. It’s just faster to hit ‘tab’ (thus accepting the suggestion) than it is to write out those 20 characters by myself,” says Johan Rosenkilde, principal researcher for GitHub Next.

Whether developers are new or highly skilled, they’ll often have to work in less familiar languages, and code completion suggestions using GitHub Copilot can lend a helping hand. “Using GitHub Copilot for code completion has really helped speed up my learning experience,” says Berryman. “I will often accept the suggestion because it’s something I wouldn’t have written on my own since I don’t know the syntax.”

Using an AI coding tool has become an invaluable skill in itself. Why? Because the more developers practice coding with these tools, the faster they’ll get at using them.

2. Explicit code comments codes using natural language to receive even better AI-generated code suggestions

For experienced developers in unfamiliar environments, tools like GitHub Copilot can even help jog their memories.

Let’s say a developer imports a new type of library they haven’t used before, or that they don’t remember. Maybe they’re looking to figure out the standard library function or the order of the argument. In these cases, it can be helpful to make GitHub Copilot more explicitly aware of where the developer wants to go by writing a comment.

“It’s quite likely that the developer might not remember the formula, but they can recognize the formula, and GitHub Copilot can remember it by being prompted,” says Rosenkilde. This is where natural language commentary comes into play: it can be a shortcut for explaining intent when the developer is struggling with the first few characters of code that they need.

If developers give specific names to their functions and variables, and write documentation, they can get better suggestions, too. That’s because GitHub Copilot can read the variable names and use them as an indicator for what that function should do.

Suddenly that changes how developers write code for the better, because code with good variable and function names are more maintainable. And oftentimes the main job of a programmer is to maintain code, not write it from scratch.

“When you push that code, someone is going to review it, and they will likely have a better time reviewing that code if it’s well named, if there’s even a hint of documentation in it, and so on,” says Rosenkilde. In this sense, the symbiotic relationship between the developer and the AI coding tool is not just beneficial for the developer, but for the entire team.

3. Chat directly with AI

With AI chatbots, code generation can be more interactive. GitHub Copilot Chat, for example, allows developers to interact with code by asking it to explain code, improve syntax, provide ideas, generate tests, and modify existing code—making it a versatile ally in managing coding tasks.

Rosenkilde uses the different functionalities of GitHub Copilot:

“When I want to do something and I can’t remember how to do it, I type the first few letters of it, and then I wait to see if Copilot can guess what I’m doing,” he says. “If that doesn’t work, maybe I delete those characters and I write a one liner in commentary and see whether Copilot can guess the next line. If that doesn’t work, then I go to Copilot Chat and explain in more detail what I want done.”

Typically, Copilot Chat returns with something much more verbose and complete than what you get from GitHub Copilot code completion. “Namely, it describes back to you what it is you want done and how it can be accomplished. It gives you code examples, and you can respond and say, oh, I see where you’re going. But actually I meant it like this instead,” says Rosenkilde.

But using AI chatbots doesn’t mean developers should be hands off. Mistakes in reasoning could lead the AI down a path of further mistakes if left unchecked. Berryman recommends that users should interact with the chat assistant in much the same way that you would when pair programming with a human. “Go back and forth with it. Tell the assistant about the task you are working on, ask it for ideas, have it help you write code, and critique and redirect the assistant’s work in order to keep it on the right track.”

The importance of code reviews

GitHub Copilot is designed to empower developers to execute their ideas. As long as there is some context for it to draw on, it will likely generate the type of code the developer wants. But this doesn’t replace code reviews between developers.

Code reviews play an important role in maintaining code quality and reliability in software projects, regardless of whether AI coding tools are involved. In fact, the earlier developers can spot bugs in the code development process, the cheaper it is by orders of magnitude.

Ordinary verification would be: does the code parse? Do the tests work? With AI code generation, Ziegler explains that developers should, “Scrutinize it in enough detail so that you can be sure the generated code is correct and bug-free. Because if you use tools like that in the wrong way and just accept everything, then the bugs that you introduce are going to cost you more time than you save.”

Rosenkilde adds, “A review with another human being is not the same as that, right? It’s a conversation between two developers about whether this change fits into the kind of software they’re building in this organization. GitHub Copilot doesn’t replace that.”

The advantages of using AI to generate code

When developer teams use AI coding tools across the software development cycle, they experience a host of benefits, including:

Faster development, more productivity

AI code generation can significantly speed up the development process by automating repetitive and time-consuming tasks. This means that developers can focus on high-level architecture and problem-solving. In fact, 88% of developers reported feeling more productive when using GitHub Copilot.

Rosenkilde reflects on his own experience with GitHub’s AI pair programmer: “95% of the time, Copilot brings me joy and makes my day a little bit easier. And this doesn’t change the code I would have written. It doesn’t change the way I would have written it. It doesn’t change the design of my code. All it does is it makes me faster at writing that same code.” And Rosenkilde isn’t alone: 60% of developers feel more fulfilled with their jobs when using GitHub Copilot.

Mental load alleviated

The benefits of faster development aren’t just about speed: they’re also about alleviating the mental effort that comes with completing tedious tasks. For example, when it comes to debugging, developers have to reverse engineer what went wrong. Detecting a bug can involve digging through an endless list of potential hiding places where it might be lurking, making it repetitive and tedious work.

Rosenkilde explains, “Sometimes when you’re debugging, you just have to resort to creating print statements that you can’t get around. Thankfully, Copilot is brilliant at print statements.”

A whopping 87% of developers reported spending less mental effort on repetitive tasks with the help of GitHub Copilot.

Less context switching

In software development, context switching is when developers move between different tasks, projects, or environments, which can disrupt their workflow and decrease productivity. They also often deal with the stress of juggling multiple tasks, remembering syntax details, and managing complex code structures.

With GitHub Copilot developers can bypass several levels of context switching, staying in their IDE instead of searching on Google or jumping into external documentation.

“When I’m writing natural language commentary,” says Rosenkilde, “GitHub Copilot code completion can help me. Or if I use Copilot Chat, it’s a conversation in the context that I’m in, and I don’t have to explain quite as much.”

Generating code with AI helps developers offload the responsibility of recalling every detail, allowing them to focus on higher-level thinking, problem-solving, and strategic planning.

Berryman adds, “With GitHub Copilot Chat, I don’t have to restate the problem because the code never leaves my trusted environment. And I get an answer immediately. If there is a misunderstanding or follow-up questions, they are easy to communicate with.”

What to look for in enterprise-ready AI code generation tools

Before you implement any AI into your workflow, you should always review and test tools thoroughly to make sure they’re a good fit for your organization. Here are a few considerations to keep in mind.

Compliance

• Regulatory compliance. Does the tool comply with relevant regulations in your industry?
• Compliance certifications. Are there attestations that demonstrate the tool’s compliance with regulations?

Security

• Encryption. Is the data transmission and storage encrypted to protect sensitive information?
• Access controls. Are you able to implement strong authentication measures and access controls to prevent unauthorized access?
• Compliance with security standards. Is the tool compliant with industry standards?
• Security audits. Does the tool undergo regular security audits and updates to address vulnerabilities?

Privacy

• Data handling. Are there clear policies for handling user data and does it adhere to privacy regulations like GDPR, CCPA, etc.?
• Data anonymization. Does the tool support anonymization techniques to protect user privacy?

Permissioning

• Role-based access control. Are you able to manage permissions based on user roles and responsibilities?
• Granular permissions. Can you control access to different features and functionalities within the tool?
• Opt-in/Opt-out mechanisms. Can users control the use of their data and opt out if needed?

Pricing

• Understand the pricing model. is it based on usage, number of users, features, or other metrics?
• Look for transparency. Is the pricing structure clear with no hidden costs?
• Scalability. Does the pricing scale with your usage and business growth?

Additionally, consider factors such as customer support, ease of integration with existing systems, performance, and user experience when evaluating AI coding tools. Lastly, it’s important to thoroughly assess how well the tool aligns with your organization’s specific requirements and priorities in each of these areas.

Visit the GitHub Copilot Trust Center to learn more around security, privacy, and other topics.

Can AI code generation be detected?

The short answer here is: maybe.

Let’s first give some context to the question. It’s never really the case that a whole code base is generated with AI, because large chunks of AI-generated code are very likely to be wrong. The standard code review process is a good way to avoid this, since large swaths of completely auto-generated code would stand out to a human developer as simply not working.

For smaller amounts of AI-generated code, there is no way at the moment to detect traces of AI in code with true confidence. There are offerings that purport to classify whether content has AI-generated text, but there are limited equivalents for code, since you’d need a dedicated model to do it. Ziegler explains, “Computer generated code is good enough that it doesn’t leave any particular traces and normally has no clear tells.”

At GitHub, the Copilot team makes use of a duplicate detection filter that detects exact duplicates in code. So, if you’re writing code and it’s an exact copy of something that exists elsewhere, then it’ll flag it.

Is AI code generation secure?

AI code generation is not any more insecure than human generated code. A combination of testing, manual code reviews, scanning, monitoring, and feedback loops can produce the same quality of code as your human-generated code.

When it comes to code generated by GitHub Copilot, developers can use tools like code scanning, which actively reviews your code for potential security issues in real-time and seamlessly integrates the findings into the developer workflow.

Ultimately, AI code generation will have vulnerabilities—but so does code written by human developers. As Ziegler explains, “It’s unclear whether computer generated code does particularly worse. So, the answer is not if you have GitHub Copilot, use a vulnerability checker. The answer is always use a vulnerability checker.”

Watch this video for more tips and words of advice around secure coding best practices with AI.

Empower your enterprise with AI code generation

While the benefits to using AI code generation tools can be significant, it’s important to note that human oversight remains crucial to ensure that the generated code aligns with project goals, coding standards, and business needs.

Tech leaders should embrace the use of AI code generation—not only to streamline development, but also to empower developer teams to collaborate, drive meaningful business outcomes, and deliver exceptional value to customers.

Learn more or get started with the world’s most widely adopted AI developer tool.

The post How AI code generation works appeared first on The GitHub Blog.

While this technology is exciting, many are also concerned about how LLMs can generate false, outdated, or problematic information, and how they sometimes even hallucinate (generating information that doesn’t exist) so convincingly. Thankfully, we can immediately put one rumor to rest. According to Alireza Goudarzi, senior researcher of machine learning (ML) for GitHub Copilot: “LLMs are not trained to reason. They’re not trying to understand science, literature, code, or anything else. They’re simply trained to predict the next token in the text.”

Let’s dive into how LLMs come to do the unexpected, and why. This blog post will provide comprehensive insights into LLMs, including their training methods and ethical considerations. Our goal is to help you gain a better understanding of LLM capabilities and how they’ve learned to master language, seemingly, without reasoning.

What are large language models?

LLMs are AI systems that are trained on massive amounts of text data, allowing them to generate human-like responses and understand natural language in a way that traditional ML models can’t.

“These models use advanced techniques from the field of deep learning, which involves training deep neural networks with many layers to learn complex patterns and relationships,” explains John Berryman, a senior researcher of ML on the GitHub Copilot team.

What sets LLMs apart is their proficiency at generalizing and understanding context. They’re not limited to pre-defined rules or patterns, but instead learn from large amounts of data to develop their own understanding of language. This allows them to generate coherent and contextually appropriate responses to a wide range of prompts and queries.

And while LLMs can be incredibly powerful and flexible tools because of this, the ML methods used to train them, and the quality—or limitations—of their training data, can also lead to occasional lapses in generating accurate, useful, and trustworthy information.

Deep learning

The advent of modern ML practices, such as deep learning, has been a game-changer when it comes to unlocking the potential of LLMs. Unlike the earliest language models that relied on predefined rules and patterns, deep learning allows these models to create natural language outputs in a more human-like way.

“The entire discipline of deep learning and neural networks—which underlies all of this—is ‘how simple can we make the rule and get as close to the behavior of a human brain as possible?’” says Goudarzi.

By using neural networks with many layers, deep learning enables LLMs to analyze and learn complex patterns and relationships in language data. This means that these models can generate coherent and contextually appropriate responses, even in the face of complex sentence structures, idiomatic expressions, and subtle nuances in language.

While the initial pre-training equips LLMs with a broad language understanding, fine-tuning is where they become versatile and adaptable. “When developers want these models to perform specific tasks, they provide task descriptions and examples (few-shot learning) or task descriptions alone (zero-shot learning). The model then fine-tunes its pre-trained weights based on this information,” says Goudarzi. This process helps it adapt to the specific task while retaining the knowledge it gained from its extensive pre-training.

But even with deep learning’s multiple layers and attention mechanisms enabling LLMs to generate human-like text, it can also lead to overgeneralization, where the model produces responses that may not be contextually accurate or up to date.

Why LLMs aren’t always right

There are several factors that shed light on why tools built on LLMs may be inaccurate at times, even while sounding quite convincing.

Limited knowledge and outdated information

LLMs often lack an understanding of the external world or real-time context. They rely solely on the text they’ve been trained on, and they don’t possess an inherent awareness of the world’s current state. “Typically this whole training process takes a long time, and it’s not uncommon for the training data to be two years out of date for any given LLM,” says Albert Ziegler, principal researcher and member of the GitHub Next research and development team.

This limitation means they may generate inaccurate information based on outdated assumptions, since they can’t verify facts or events in real-time. If there have been developments or changes in a particular field or topic after they have been trained, LLMs may not be aware of them and may provide outdated information. This is why it’s still important to fact check any responses you receive from an LLM, regardless of how fact-based it may seem.

Lack of context

One of the primary reasons LLMs sometimes provide incorrect information is the lack of context. These models rely heavily on the information given in the input text, and if the input is ambiguous or lacks detail, the model may make assumptions that can lead to inaccurate responses.

Training data biases and limitations

LLMs are exposed to massive unlabelled data sets of text during pre-training that are diverse and representative of the language the model should understand. Common sources of data include books, articles, websites—even social media posts!

Because of this, they may inadvertently produce responses that reflect these biases or incorrect information present in their training data. This is especially concerning when it comes to sensitive or controversial topics.

“Their biases tend to be worse. And that holds true for machine learning in general, not just for LLMs. What machine learning does is identify patterns, and things like stereotypes can turn into extremely convenient shorthands. They might be patterns that really exist, or in the case of LLMs, patterns that are based on human prejudices that are talked about or implicitly used,” says Ziegler.

If a model is trained on a dataset that contains biased or discriminatory language, it may generate responses that are also biased or discriminatory. This can have real-world implications, such as reinforcing harmful stereotypes or discriminatory practices.

Overconfidence

LLMs don’t have the ability to assess the correctness of the information they generate. Given their deep learning, they often provide responses with a high degree of confidence, prioritizing generating text that appears sensible and flows smoothly—even when the information is incorrect!

Hallucinations

LLMs can sometimes “hallucinate” information due to the way they generate text (via patterns and associations). Sometimes, when they’re faced with incomplete or ambiguous queries, they try to complete them by drawing on these patterns, sometimes generating information that isn’t accurate or factual. Ultimately, hallucinations are not supported by evidence or real-world data.

For example, imagine that you ask ChatGPT about a historical issue in the 20th century. Instead, it describes a meeting between two famous historical figures who never actually met!

In the context of GitHub Copilot, Ziegler explains that “the typical hallucinations we encounter are when GitHub Copilot starts talking about code that’s not even there. Our mitigation is to make it give enough context to every piece of code it talks about that we can check and verify that it actually exists.”

But the GitHub Copilot team is already thinking about how to use hallucinations to their advantage in a “top-down” approach to coding. Imagine that you’re tackling a backlog issue, and you’re looking for GitHub Copilot to give you suggestions. As Johan Rosenkilde, principal researcher for GitHub Next, explains, “ideally, you’d want it to come up with a sub-division of your complex problem delegated to nicely delineated helper functions, and come up with good names for those helpers. And after suggesting code that calls the (still non-existent) helpers, you’d want it to suggest the implementation of them too!”

This approach to hallucination would be like getting the blueprint and the building blocks to solve your coding challenges.

Ethical use and responsible advocacy of LLMs

It’s important to be aware of the ethical considerations that come along with using LLMs. That being said, while LLMs have the potential to generate false information, they’re not intentionally fabricating or deceiving. Instead, these arise from the model’s attempts to generate coherent and contextually relevant text based on the patterns and information it has learned from its training data.

The GitHub Copilot team has developed a few tools to help detect harmful content. Goudarzi says “First, we have a duplicate detection filter, which helps us detect matches between generated code and all open source code that we have access to, filtering such suggestions out. Another tool we use is called Responsible AI (RAI), and it’s a classifier that can filter out abusive words. Finally, we also separately filter out known unsafe patterns.”

Understanding the deep learning processes behind LLMs can help users grasp their limitations—as well as their positive impact. To navigate these effectively, it’s crucial to verify information from reliable sources, provide clear and specific input, and exercise critical thinking when interpreting LLM-generated responses.

As Berryman reminds us, “the engines themselves are amoral. Users can do whatever they want with them and that can run the gamut of moral to immoral, for sure. But by being conscious of these issues and actively working towards ethical practices, we can ensure that LLMs are used in a responsible and beneficial manner.”

Developers, researchers, and scientists continuously work to improve the accuracy and reliability of these models, making them increasingly valuable tools for the future. All of us can advocate for the responsible and ethical use of LLMs. That includes promoting transparency and accountability in the development and deployment of these models, as well as taking steps to mitigate biases and stereotypes in our own corners of the internet.

The post Demystifying LLMs: How they can do things they weren’t trained to do appeared first on The GitHub Blog.

You can still sign up to join us on November 8-9 right here. If you register to attend IRL at the Yerba Buena Center for the Arts in San Francisco before October 6, you can take advantage of our Early Bird discount! And as always, virtual registration is free if you’re not able to join us IRL.

Day 1, November 8

Need a little help convincing your boss to let you attend? Use our handy email template!

Day 2, November 9

Once you’ve registered to attend, you’ll be able to build your personalized agenda using the schedule builder! Simply navigate to the session catalog and click “Add to schedule” to add your favorites.

Be part of an unforgettable IRL experience

In addition to the incredible sessions above, the IRL Universe ‘23 experience offers even more learning and networking opportunities than ever before.

Workshops and GitHub certifications

For even more hands-on learning, in-person attendees can sign up for one of the nine workshops starting on November 7 and running through November 9. And if you do, you’ll receive a free voucher to get GitHub certified in Foundations, Actions, Security, or Administration!

If you’re only interested in becoming GitHub certified, you can purchase a standalone voucher to take the exam on-site at GitHub Universe. Why get certified? Earning a GitHub certification is an industry-endorsed approach to demonstrating proficiency with GitHub technologies. In other words, it’s good for your resume.

Space is limited for both, so make sure to act fast!

Save your spot for GitHub Universe ‘23

Universe is more than just a tech conference: it’s a chance to immerse yourself in the future of software development, connect with like-minded individuals, and gain the skills and knowledge you need to excel in your career. Grab your tickets, customize your learning journey by building your agenda, and get ready to transform tomorrow at GitHub Universe ‘23!

The post Your ultimate guide to the GitHub Universe ‘23 agenda appeared first on The GitHub Blog.

Since we launched GitHub Copilot, we’ve been at the forefront of generative AI, laser-focused on what these tools can help teams accomplish. (Did you know that 92% of U.S.-based developers are already using AI coding tools both in and outside of work?) If you aren’t already entrenched in generative AI, GitHub Universe will be a great place to get started and dive deeper.

With AI tools enabling teams to move faster than ever to achieve and exceed their goals, natively-embedded security testing needs to match that pace. If you’re going to build and innovate, your code needs to be secure. At GitHub Universe, we’ll show you how.

The final frontier of productivity is DevEx: making sure developers are empowered to do their best work—and how leaders (and the tools they choose) can make all the difference.

GitHub Universe is coming up on November 8-9 and Super-Early Bird tickets are now available. (So, you should fly on over and pick some up!). Below we’re going to dive into what you can expect at our annual event for developers and enterprises across the globe.

Are you ready for it?

The new 2023 experience

With new content formats, stages, interactive sessions, and more, 2023 attendees can take their Universe experience to the next level.

Amplify your learning

When it comes to learning, we understand that it’s not just about the type of content shared—but about how it’s shared and who’s doing the teaching. That’s why this year, we’ve expanded our campus at the Yerba Buena Center for the Arts (YBCA) in San Francisco to include five stages running concurrently throughout the day, and more than 100+ industry speakers with a wide spectrum of diverse voices to help you grow your career and your business.

Choose from the Main Stage, SF MoMA theater, Screening Room, and Demos & Donuts Stage (you read that right ) for general sessions, fireside chats, customer panels, and more. Take a behind-the-scenes look at the Broadcast Studio where our MCs will be live streaming throughout Universe. We’ll also have the newly minted Discussions Lounge for more peer-to-peer conversations!

Virtual attendees will have access to all the live content on the Main Stage and Broadcast Studio, with access to recordings from the SF MoMA theater and the Screening Room once they’re available on-demand.

Level-up your skill set

We’re excited to announce that in-person attendees will have access to the exclusive beta launch of GitHub certification testing! Attendees will be able to take on-site exams to get certified in GitHub Foundations, GitHub Actions, GitHub Advanced Security, or GitHub Administration.

And back for the second year in a row, attendees will also be able to register for nine unique workshops running from November 7-9.

Sign up for a workshop and get a complimentary voucher for a certification exam, or simply purchase a standalone voucher. You can register for workshops and certifications when you buy your in-person ticket.

Network with industry professionals

We expanded our campus presence at YBCA to curate an even better networking experience for our attendees, customers, and partners.

Walk through The Hub, where you can explore new technology and interact with top partners across the industry. Learn from GitHub product leaders and Universe partners on the Demos & Donuts stage. And make sure to ask all of your burning product questions at the Ask GitHub booth, no pre-booking required!

Eat, drink, shop, stroll!

Of course, it wouldn’t be GitHub Universe without a touch of magic. If you (or anyone you know, we’re not picky), need some new Octocat gear, visit The Shop for the latest merch.

Past attendees loved our delicious food and beverages. From fresh juices and gourmet coffee to all-day snacks and delightful treats, you’ll have more than enough choices to keep your energy levels up.

You can also stop by The Galleries to immerse yourself in both art and technology. And don’t forget to stroll by The Garden with your snack of choice and watch a live stream of the on-site broadcast studio!

So, what are you waiting for?

It’s easier than ever to customize your experience at GitHub Universe. Need some help convincing your boss to let you attend? Check out our handy template. Just copy and paste it into an email, fill in your relevant information, and it’s ready for send off.

Whether you join us IRL in San Francisco or virtually from your couch, we want to see you! Virtual tickets are free, and you can always upgrade to an in-person ticket later. And remember, your in-person ticket could be 30% off if you take advantage of our Super-Early Bird discount while supplies last.

The post Build for today, transform for tomorrow at GitHub Universe 2023 appeared first on The GitHub Blog.

How does developer productivity impact businesses at large? Well, let’s take a quick look at Telus, a communications and information technology provider in Canada. When their team switched to GitHub, they were able to streamline the developer workflow—and save $16.9M in the process.

That’s just one example of how innovative tools and developer-friendly features can create real, lasting change. And GitHub Galaxy is where you can get a glimpse of the latest and greatest tools (like GitHub Copilot) that are enabling devs to work smarter and helping business leaders achieve their goals, too.

Sign up for the event right here. (Registration is free!). And keep reading below for a sneak peek at some of the sessions and speakers.

What can you expect to learn at GitHub Galaxy?

Each day of GitHub Galaxy will focus on a specific business challenge for growing enterprises, so you can build the schedule that works best for you. The virtual sessions are taking place in three regions across multiple time zones, so you’ll be able to tune into a keynote broadcast, an interview-style fireside chat with a GitHub customer, and an interactive forum session where you can work through questions with other attendees in real time.

The schedule

Kick-off: Global keynote broadcast with GitHub leadership

March 28, 9:30am PT

Galaxy will kick off with a special keynote broadcast from GitHub leadership. You’ll hear from GitHub CEO, Thomas Dohmke, GitHub Chief Product Officer, Inbal Shani, and other product leaders to learn how GitHub is building an AI-powered, secure, and fully integrated developer experience for businesses, teams, startups, and individuals alike to drive innovation at scale.

Add it to your calendar and bring your friends—you won’t want to miss it!

Day 1: Flexibility and productivity

March 29

On Day 1, we’ll be looking at how you can help individuals and teams be more flexible and productive. You’ll come away with an understanding of how to accelerate your high-quality software development and delivery by prioritizing the developer experience (DevX).

Day 2: Security and reliability

March 30

On Day 2, you’ll hear from industry experts on how the most successful businesses are optimizing their software development cycles and preventing vulnerabilities by embedding security into the developer workflow. The goal is for you and your teams to leave these sessions with an understanding of how to scale your DevOps quickly using security tools that won’t sacrifice productivity or innovation.

Day 3: Efficiency and collaboration

March 31

On Day 3, we’ll cover how enterprises can increase efficiency, drive collaboration, and deploy secure and compliant code using enterprise-ready tools. By investing in these tools, you can cut costs, simplify your tech stack, and reduce onboarding time. You’ll also improve the developer experience, and by extension, retention and recruitment. Learn how choosing the right tools can have an outsized impact on your business’ ability to accelerate growth and efficiency.

Together, we’ll think creatively, experiment with new technologies, and push the boundaries of what’s possible. So what are you waiting for?

The post GitHub Galaxy 2023: your guide to building a more flexible and productive software development cycle appeared first on The GitHub Blog.

These are common struggles developer teams face and were some of the challenges Canadian communications and IT provider TELUS experienced with their fragmented tech stack and engineering teams. They constantly had to define, maintain, and provision development environments, making it so that developers spent too much time context switching between different tools and not enough time writing code. But when they decided to use GitHub Enterprise as their one, centralized and integrated platform for end to end software development, magic happened:

• Onboarding happens in minutes now for new developers
• Two hours of time saved per developer each week
• One minute of time saved per GitHub Actions executed

We hear these stories from GitHub customers all the time. And to dig further into just how much GitHub impacts developers and businesses, we commissioned a Total Economic Impact (TEI) study from Forrester Consulting to examine the potential return on investment (ROI) businesses can gain by using GitHub Enterprise Cloud (GHEC) and GitHub Advanced Security (GHAS).

To understand the benefits, costs, and risks associated with investing in GitHub’s platform, they interviewed and surveyed individuals with experience using GHEC and GHAS, and aggregated these experiences into a composite organization¹.

So, what did Forrester find? Over three years, using GitHub drove 433% ROI for the organization simply with the combined power of all GitHub’s enterprise products.

Let’s take a look at other ways GitHub helps developers be more productive, onboard faster, and remediate security vulnerabilities all while increasing efficiency.

Better code, faster

Developer productivity is an important metric that’s incredibly difficult to quantify and define. Is it influenced by developer happiness? Is it defined by how much time a developer spends writing code? Or, is it the speed at which developers complete tasks?

With more and more businesses grappling with increasingly complicated tech stacks, we know that simplifying the tools developers use on a daily basis positively impacts developer productivity.

Key challenges

Before using GitHub, interviewees’ organizations faced many tech stack challenges, including having to “write homegrown solutions to address the context switching between one open source tool to another,” and “discovering security flaws late in the SDLC or after the code was deployed into production.” In addition, team productivity was impacted by the decentralized management of maintaining legacy open source tools and infrastructure.

But after using GHEC and GHAS for three years, the composite organization saw:

• Developer productivity gains of 22%
• Tool maintenance productivity gains of 75% by phasing out legacy tools

Ultimately, GitHub helps enterprises produce better and more secure code—and more quickly—with faster code drops into production.

Improved security

Adopting the right approach to DevSecOps reduces risk and allows teams to deploy quickly and securely. But many businesses find this easier said than done when using legacy tools that aren’t optimized for modern development practices.

Key challenges

The organization faced numerous security challenges before moving to GitHub. For one, “interviewees reported that without automation in their organization’s CI/CD environment, software defects and security vulnerabilities made it far into the SDLC before being detected.” DevOps and DevSecOps teams spent large amounts of time writing custom code to detect code defects and security vulnerabilities across all platforms. And in order to comply with IT audit requirements, respondents said that they had teams of auditors—many who worked full time—gathering documentation and performing internal and external audits from decentralized environments.

When they implemented GHEC, GHAS, GitHub Pages, GitHub Actions, and Codespaces, the organization saw the following benefits over three years:

• Time savings of $2.7 million USD in IT auditors preparing for audits
• Improved DevOps, DevSecOps, and site reliability engineer efficiency by 15%
• Reduction in time spent on code remediation, resulting in savings of $5.2 million USD

With fewer vulnerabilities in code production and more granular controls, GitHub is a secure solution for enterprises combating both internal and external threats.

Recruit and retain talent

The world of work looks vastly different than it did a couple of years ago. With that, businesses have to think about what it takes to recruit and retain employees for the long term. And when it comes to software development specifically, intentionally placing the developer experience at the core of your efforts can attract new employees, improve retention, and increase productivity.

Key challenges

The onboarding process for a new developer is crucial to ensuring their short-term success and ability to make an immediate impact on the business. Before switching to GitHub, onboarding new developers took 10 days according to interviewees, forcing the organization to pay for unproductive work since “…new developers need to be trained on the organization’s methods of coding, but developers also had to learn how to use the various tools that they would need during the SDLC.”

After implementing GitHub, organizations drastically reduced the time spent training new developers by 80%.

Ultimately, developers want to use the tools they know and love at work. When businesses use GitHub’s entire platform, tenured developers are happier and new developers can make an impact from day one.

Reduced costs

In an increasingly unpredictable macroeconomic environment, business leaders have to be even more strategic when it comes to reducing operational costs and driving efficiency in their organizations.

Key challenges

When Forrester interviewed the DevOps and DevSecOps teams in their research, they found that using and maintaining infrastructure for legacy tools incurred annual infrastructure, software, and labor costs to maintain. But by standardizing their development on GHEC and using the power of GHAS and GitHub Actions to perform centralized tasks, many of the legacy servers could be deprecated over time.

After deploying GHEC, the organization retired its use of legacy tools and realized a total cost of ownership savings of $811,900 USD over three years.

In short, moving to GitHub eliminates redundant costs and improves site reliability.

Experience the power of GitHub yourself

As the home for all developers, we build tools that help developers do the best work of their lives. How to make developers more productive, collaborative, happy, and innovative are our bread and butter. And by dedicating ourselves to these goals, we help the companies that hire them meet their goals, too.

After all, GitHub is the #1 ranked DevOps platform on G2 for a reason! With automated workflows via GitHub Actions, supercharged collaboration tools in the cloud with Codespaces, and native security throughout the developer workflow with GHAS, GitHub improves your time-to-market, increases your revenue growth, and reduces your hardware costs and time spent managing platforms.

Notes

The post Increase developer productivity, save time on developer onboarding, and drive ROI in 2023 appeared first on The GitHub Blog.