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GitHub
@github
The AI-powered developer platform to build, scale, and deliver secure software.
San Francisco, CAgithub.comJoined February 2008

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"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 realize the 10X developer." -Thomas
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"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 – beginning in the morning gradually zaps my creative energy." -Thomas
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Generative AI tools like GitHub Copilot for Business are changing the game in software by enabling engineering teams to focus on important (more enjoyable) work. See how it works in our upcoming webinar on May 11!
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You can only accept one type of contribution to your open source project from now on—which is it?
  • Code (Features/Bug Fixes)
    56.5%
  • Docs
    17.1%
  • Testing
    14.8%
  • Design (Graphics & UI/UX)
    11.6%
1,864 votesFinal results
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Developers lose hours with framework issues, looking up syntax and writing boilerplate code 😨 These tasks break flow, reduce satisfaction, slow velocity and increase churn 📉 Fortunately, GitHub Copilot can help! 😎
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This month on The ReadME Podcast, hosts and dive into shifts in technology that force us to adapt and inspire us to dream big. From nuclear fusion research to taking your next career leap, check it out on The ReadME Project.
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Developers are using generative AI coding tools like GitHub Copilot to: 🏃‍♀️Get a head start on complex code translation tasks 🧑‍💻Code more efficiently Tackle new problems and get creative 🔍Find answers without leaving their IDEs Build better test coverage
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🔗Generative AI coding tools are powered by large language models (LLMs). Today’s state-of-the-art LLMs are transformers, which makes them adept at connecting tokens, big-picture thinking, and scaling. The results are coding and content suggestions that are contextually relevant.
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🌐Generative AI coding tools are trained on large amounts of code across programming languages. They’re trained to process data like humans do—by recognizing patterns, making connections, and drawing inferences with limited guidance.
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