Florin Lungu’s Post

From the pages of our Insider newsletter: How to use GitHub Copilot Explore July’s Insider newsletter, featuring prompts, tips, and use cases for GitHub Copilot. The post From the pages of our Insider newsletter: How to use GitHub Copilot appeared first on The GitHub Blog. Read more on following #GitHub Blog post! #Product #Insider

Get better code suggestions from GitHub Copilot by following the advice in this article.

Unleashing GitHub Copilot Enterprise: A Game Changer for Developers ? You might want to enter Enter GitHub Copilot Enterprise - a paradigm shift designed to supercharge the development process through AI. Here’s how enterprises can leverage it to foster a culture of excellence and creativity within their teams. Empowering Teams at Scale GitHub Copilot Enterprise isn’t just another tool; it’s a catalyst for transformation. By enabling it, enterprises grant their teams access to advanced AI features directly on GitHub site, including chat assistance and context-aware coding suggestions. This isn’t about replacing human ingenuity but enhancing it, enabling developers to code smarter and faster. Tailored Access and Control Understanding the diverse needs of organizations, GitHub provides flexible management options. Enterprise owners can dictate access at both the enterprise and organization levels, ensuring that Copilot’s power is harnessed in alignment with the company’s policies and preferences. Whether it's enabling Copilot for specific teams or the entire organization, the control is nuanced and comprehensive. A Story of Implementation Imagine a scenario where a leading tech enterprise decides to adopt Copilot Enterprise. The decision comes from recognizing the need for faster iteration and innovation cycles. By enabling Copilot, they witness a transformation - developers leveraging AI to breeze through boilerplate code, dedicating more time to solve complex problems creatively. The result? Accelerated development timelines and enhanced product quality. Actionable Tips for Seamless Integration Index Your Repositories: Optimize Copilot’s performance by enabling semantic code indexing on your most vital repositories. It ensures Copilot understands your codebase better, offering more relevant suggestions. Curate Knowledge Bases: Aggregate Markdown documentation across repositories to create knowledge bases. It empowers Copilot to provide context-aware assistance, making it an indispensable part of your development workflow. Conclusion GitHub Copilot Enterprise is not just a feature; it's a strategic asset. By embracing it, enterprises can unlock their teams' potential, streamline development processes, and foster an environment where innovation flourishes. Whether you’re a CTO aiming for efficiency or a developer striving for excellence, Copilot Enterprise is your companion on the journey to redefine what’s possible in software development. #copilot #enterprise #AI more here : >>>>> https://lnkd.in/dNiGMKfS

🚀 Exciting News: GitHub Copilot General Availability in the CLI with commands! 🚀 🌟 The future of coding just got even more accessible! I'm thrilled to announce that #GitHub #Copilot is now available directly in the Command Line Interface (CLI) with commands, making coding smarter, faster, and more intuitive than ever before. 💡 GitHub Copilot, powered by the revolutionary OpenAI Codex, leverages the power of machine learning to assist developers in writing code effortlessly. And now, with its integration into the CLI, developers can harness Copilot's capabilities seamlessly within their command-line workflows. 🔑 Key Features: 1️⃣ **Effortless Code Suggestions:** GitHub Copilot in the CLI provides real-time code suggestions as you type, helping you write code faster and with fewer errors. 2️⃣ **Contextual Command Support:** Whether you're working on a simple script or a complex project, Copilot understands the context of your code and offers relevant suggestions tailored to your specific needs. 3️⃣ **Improved Productivity:** Say goodbye to tedious searches and manual code writing. With Copilot at your fingertips in the CLI, you can focus more on solving problems and less on writing boilerplate code. 4️⃣ **Language Agnostic:** Copilot supports multiple programming languages, empowering developers across various tech stacks to benefit from its intelligent code suggestions. 🌐 GitHub Copilot in the CLI represents a significant step forward in democratizing access to advanced coding assistance tools, making them available to developers wherever they work. 👩💻 Are you ready to supercharge your coding experience? Try out GitHub Copilot in the CLI today and unlock a whole new level of productivity and creativity! link: https://lnkd.in/guRQYaXx #GitHubCopilot #CLI #Coding #Developers #Productivity #Innovation #OpenAI #Codex #GitHub #Programming #CodeAssistance

Just getting started with GitHub Copilot? Check out these helpful tips, tricks, and best practices to make your Copilot experience even better! #GitHub #Copilot Link: https://lnkd.in/gU2XUMx4

✨ Looking to increase Productivity with GitHub Copilot - Try this github blog with tips and tricks ✨

GitHub Copilot truly revolutionizes the coding experience. As a seasoned engineer , I've found immense value in leveraging this. Watching YouTube videos related to programming, I've experienced firsthand the seamless integration of Copilot into my workflow. No longer do I need to painstakingly jot down notes; instead, Copilot effortlessly generates entire function codes,based on the logic it recommends. It's a game-changer, allowing me to focus more on problem-solving and innovation, while Copilot handles the heavy lifting of code generation. With Copilot by our side, the possibilities for innovation and productivity are truly limitless. #githubcopilot #copilot #programmingjourney #engineeringexcellence #highperformanceteams

This is a great post by the team at commercetools 🪄 It shows how a 150-person engineering org explored, evaluated, and implemented a new technology in a structured way. And then they wrote it all down and shared it with the world! Buying completely new tools (for example CDEs or AI coding assistants) is challenging for many engineering organizations. Discussions swing from one extreme ("This is going to change the world! Everyone will be using this!) to the other ("This cannot possibly pass our security requirements for a POC"). Neither perspective is helpful for good decisions. It's best to start with a simple Q&A session that should look like something like this: - What do we want to achieve with the new technology? - What is the primary use case? (Alternatively: what problem are we facing today that could be addressed by the new technology?) - What is one team that could test it this quarter? - How do we want to set it up? (multi-tenant vs. single-tenant, open-source vs. full-service vendor) - How are we going to measure if it is a success or not? (IMHO, with a quantitative ROI perspective and a qualitative user survey) - Who will make the decision by when? The world of developer tools would benefit a ton from more teams writing about how they evaluate new tools. Thanks Tobias Deekens 🙏🏼

Very interesting article which explains different usage of Github Copilot than our conventional usage. Thanks Lalit Kale for sharing. Some usages: 1.    Run terminal commands from GitHub Copilot Chat 2.    Write pull request summaries (Copilot Enterprise feature only) 3.    Generate commit messages 4.    Get help in the terminal with GitHub Copilot in the CLI 5.    Talk to your repositories on GitHub.com (Copilot Enterprise feature only) 6.    Fix code inline 7.     Bulk close 1000+ GitHub Issues 8.    Generate documentation for your code 9.    Get help with error messages in your terminal 10.  Debug your GitHub Actions workflow https://lnkd.in/eC3K-z9f #copilot #amdocs #makeitamazing #platformx #github

Some thoughts on the usage Github Copilot in a context of survivorship bias. When you first give someone a web search engine to use, they generally start by writing queries as if having a conversation with it. Over time such usage will transform into something in tune of keyword soup which would sound like caveman speech when uttered. But this was supposed to be about Copilot. The quality of the recommendations or completions differs widely based on context where at times it feels as if the model is reading one's mind and then in others you're super-frustrated of getting complete nonsense as recommendation. Generally we dismiss those recommendations (when poor) and keep on going (keep on adding more to our codebase from our own heads). Which kind of equals us throwing a bad query out there and then browsing through bad results, somewhat upset. Perhaps an alternative approach to that would be to take bad recommendation as a feedback that the code is unreadable. Or not readable enough to get a good contextual recommendation. One could go over their already written code and improve the wording of variables and method names and try again. Maybe even restructure the code and then try again. What I'm trying to get to here is that we have all these new technologies and they have standard narratives attached to them on how we're supposed to use them. Search for the implicit signal sent by every tool we use: Q: why are my results bad? (why are my completions bad?) 𝙰̶:̶ ̶𝚋̶𝚎̶𝚌̶𝚊̶𝚞̶𝚜̶𝚎̶ ̶𝚝̶𝚑̶𝚒̶𝚜̶ ̶𝚝̶𝚘̶𝚘̶𝚕̶ ̶𝚜̶𝚞̶𝚌̶𝚔̶𝚜̶.̶ A: because your query sucked. (because code is not readable) This is not always the case of course, but it's worth pondering the next time one brushes aside whatever was recommended. Same applies for tools like 𝘎𝘗𝘛 𝘌𝘯𝘨𝘪𝘯𝘦𝘦𝘳, 𝘎𝘗𝘛 𝘗𝘪𝘭𝘰𝘵 and the like which are promising, but are very easy to derail (in all honesty I still fail to know the exact best way to prompt either of these to get something useful/usable out from them).