HyperTest的动态

We have another Accelerating Government Innovation with GitHub Copilot session coming up at 3:30pm ET tomorrow that you won't want to miss. Join us for answers to frequently asked questions around steps to get started, billing, product capability updates, demo's and more! Link below in the comments ??

A fascinating topic that I am reviewing right now is impact of code-generation tools (say Github Copilot) on productivity. Say if you can see these metrics # of prompts suggested # suggestions accepted # lines of code generated %age code accepted vs generated How would you as a DevEx leader map that to productivity gains? What other metrics would you look at for copilot like solutions? #devex #copilot #genai

I love GitHub Copilot because you can give it an error message and ask what’s the issue and how to fix it. It tells you and it’s a great way to learn new things along the way. This capability is a massive timesaver and the ROI is clear on the investment. Unfortunately this isn’t universal #Copilot behavior. For example, if I attempt to do the same in Power Automate Copilot, it says it doesn’t know anything about errors or product documentation. If I go to Bing Copilot, it can do it. This seems odd to me as it should be an obvious feature of every Copilot. Don’t make me leave the Copilot experience to look up product help. I’d much rather have the Copilot handle debugging or user assist questions as this is a huge time suck. Do you agree? What other core activities could stand to be supported with a Copilot?

Supercharge Your Workflow with #GenAI: How Copilot Boosts Productivity Exciting part - GitHub Copilot has revolutionized my recent coding experience, streamlining everything and making it faster and smoother. It's like having an AI-powered assistant by your side, enhancing code quality and problem-solving efficiency. ??Join us today with great speakers Eldrick and Divya at 2pm EST to uncover how Copilot can elevate your productivity. Explore its remarkable features and gain insights into maximizing this game-changing tool's potential. ?? Register here: https://lnkd.in/g-GWZTPx Don't miss out on this opportunity to enhance your development process! #Github #DeveloperProductivity Paul Laberge Adarsha Datta

What is _really_ GitHub Copilot, and how to start with it to enhance your productivity and foster innovation? This Microsoft Learn path will show you how to implement Copilot in your organization and use it in your own projects: https://lnkd.in/gJFNbVq5 #GitHubCopilot #MSFTadvocate

Understanding the impact of implementing AI-assisted tools on your software delivery lifecycle must be important. We at Solidify can help you realize that with your own data and dashboard. More on that platform on April 10th in Copenhagen, sign up below!

Welcome to a comprehensive exploration of GitHub Copilot! In this special episode, we dive into the GitHub Copilot family, dissecting its three distinct versions - Copilot Individual, Copilot Business, and Copilot Enterprise. #github #githubcopilot

????I'm on the list! GitHub Copilot is dropping more ?? We've introduced a major new Copilot Workspace capability that we're calling "brainstorming" #GitHubCopilot #TechNews #Brainstorming #Workspace #Innovation

My personal benchmark for AI dev products is "Demo Resilience". Having seen many demos for Copilot Workspace, it's a whole lot of fun to demo because the developers stay in control! Everything that GitHub Copilot Workspace proposes—from the plan to the code—is fully editable, allowing you to iterate until you’re confident in the path ahead. You retain all of the autonomy, while Copilot Workspace lifts your cognitive strain. https://lnkd.in/g4bJcBjR

Copilot Extensions are now in Public Beta, offering all GitHub Copilot users the ability to extend and customize their Copilot experience. In this video, we explore Image Insight, a powerful Copilot Extension that processes image files to enhance the development workflow. With Image Insight, developers can generate code snippets from visual data, analyze architecture diagrams, and transform diagram-based logic into code. We will dive into several exciting use cases of this extension, including: 1. Analyzing and interpreting architecture diagrams 2. Generating class files from class diagrams 3. Converting diagram-based logic into code 4. Generating test cases from visual flows 5. Image-based data modeling 6. Identifying target resource requirements 7. Parsing network topology diagrams to generate configuration scripts 8. Converting screen designs into code #github #githubcopilot #copilot