How GitHub Next took Copilot Workspace from concept to code #142971
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No spoilers, but check out these five tips and tricks for using GitHub Copilot Workspace to get your wheels turning! |
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GitHub Next brought Copilot Workspace from concept to code by reimagining the developer workflow around natural language and AI assistance. They started with the idea that coding should begin with a task or intent (like a GitHub Issue), not just a blank file. Using GPT-4, they built a system that understands these natural language inputs, generates a step-by-step implementation plan, suggests relevant code changes, and enables in-place editing—all within a collaborative environment. |
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🛠️ How GitHub Next Turned an Idea into Copilot Workspace 📅 When: Wednesday, Oct 30, 11:30 AM – 12:10 PM PDT Chris Reddington (Senior Program Manager, DevRel) Cole Bemis (Research Engineer, GitHub Next) 🚀 What’s it about? This session will cover: How GitHub Next brought Copilot Workspace to life (launched April 2024) Behind-the-scenes of the technical preview What they’ve learned from early users Where it's headed next |
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GitHub Next’s Copilot Workspace is an experimental project aimed at taking natural language prompts (like "build a todo app") and turning them into full-fledged coding sessions, leveraging the power of Copilot, LLMs, and custom tooling. Here’s a breakdown of how GitHub Next took Copilot Workspace from concept to code: 🔍 1. Identifying the ProblemGitHub Next asked:
They envisioned a "start-to-finish" AI assistant that:
🧠 2. Designing the User ExperienceThe team focused on making AI a collaborative partner rather than an automated generator. Key UX decisions:
🧪 3. Building the SystemTo implement this vision, GitHub Next layered multiple AI technologies: 🏗️ Tech Stack:
🚀 4. Iterating with Internal FeedbackGitHub Next dogfooded the Workspace by:
🧩 5. Tackling ChallengesSome key challenges they had to overcome:
They used techniques like:
🔄 6. Testing in the WildOnce the prototype matured, GitHub Next began offering early access to developers to gather real-world usage data. Users explored building:
✨ Key Innovations
🧭 Final OutcomeCopilot Workspace is still experimental, but it represents a new direction in AI-assisted software engineering — from code completion to code collaboration.
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Copilot Workspace is different from traditional Copilot in your IDE because it’s not just giving inline code completions — it’s a whole environment where you describe what you want in natural language and then move through a workflow of tasks → specs → plans → code. IDE Copilot = mostly autocomplete and inline suggestions to speed up coding inside VS Code/JetBrains. Copilot Workspace = helps at a higher level, like when you’re starting a new feature, fixing an issue, or bootstrapping a project. You can write out the problem in plain English, let Copilot generate a plan, review or edit that plan, and then turn it into code. The big value is that you stay in control at every step — you can accept, tweak, or reject suggestions, and the Workspace organizes your tasks and context, rather than just throwing code snippets at you. So in short: Copilot in the IDE is about speeding up coding, while Copilot Workspace is about structuring and guiding the whole development workflow from idea to code. |
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Hi there 👋, Thanks for your interest in the “How GitHub Next took Copilot Workspace from concept to code” session at Universe! In this session, Chris Reddington (Senior PM, DevRel) and Cole Bemis (Research Engineer, GitHub Next) will walk through: The journey of Copilot Workspace — from initial concept at GitHub Next to the launch of the technical preview in April 2024. How Copilot Workspace enables you to go from natural language task descriptions to working code, with you in control at every step. Key learnings from early adopters of the preview, including how developers are using Workspace to address issues, iterate on PRs, and bootstrap projects faster. If you’re attending in person, we’d love for you to join us: You can also find more details in the Universe session catalog Looking forward to connecting with you! 🚀 |
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GitHub Next, the company's internal R&D group, took Copilot Workspace from concept to code by following a rigorous, iterative process focused on a core insight: developer needs more than just code generation. Their process can be broken down into four key phases:
3.Rigorous Internal Dogfooding: GitHub Next used the tool internally to build itself. They tested it on real, open-ended GitHub issues from actual projects. This "dogfooding" was crucial for refining the AI's ability to:
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Thanks for sharing the session details! I’d like to ask:
I’m really looking forward to learning more during the session! |
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How GitHub Next took Copilot Workspace from concept to code
Wednesday, Oct 30, 11:30 AM - 12:10 PM PDT -- Cowell Theater @ Gateway Pavilion
Link to the session catalog
Whether you’re addressing an issue, iterating on a pull request, or bootstrapping a project, GitHub Copilot Workspace helps jumpstart your tasks by describing what you want in natural language. You remain in control as you move between tasks, specs, plans, and code. Join GitHub's @chrisreddington, senior program manager of DevRel, and @colebemis research engineer on GitHub Next, for an introduction to Copilot Workspace, a Copilot-native dev environment launched in April 2024 by GitHub Next. Learn how Copilot Workspace works, how we got here, and what we've learned so far from the technical preview.
Please share your questions here, and we'll aim to respond as soon as possible! Or if you're in-person at Universe, why not drop by the GitHub Next booth in the Gateway Pavillion?
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