Key insights

• ⌨️ GitHub introduced co-pilot workspace, a tool that can write, test, and execute code using natural language.
• 🤖 GPT-4 enables coding using natural language instructions, while co-pilot workspace offers enhanced control for developers.
• 🔧 An AI tool in a workspace helps with issue resolution by specifying the current state of files, proposing edits, and allowing for user modifications.
• 🔄 Co-pilot assists in reviewing and modifying code before merging, and can be run in VS Code or GitHub code space.
• 🚀 AI and LLMS are increasing programming productivity without significantly affecting jobs, but pose a threat to the need for new software.
• 📈 Programming tools like co-pilot workspace are getting easier, historically resulting in more programmers without threatening experienced developers.
• 🔍 Co-pilot workspace represents a major advancement in AI programming, raising questions about the impact on human jobs in software development.
• 🏢 Businesses are integrating AI to stay relevant and avoid obsolescence amidst the threat of cheating and automation in software development.

Q&A

• Are programming tools like co-pilot workspace a threat to experienced developers?

  Tools like co-pilot workspace require knowledge of code to be useful. Easier programming tools historically result in more programmers, and experienced developers are not threatened by these advancements.

• What is the impact of AI and LLMS on programming productivity and job displacement?

  AI and LLMS are increasing productivity in programming without significantly affecting jobs. However, they pose a threat to the need for new software due to cheating and automation, leading businesses to integrate AI to avoid becoming obsolete.

• How does co-pilot assist with code review and merging?

  Co-pilot can help review and modify code before merging. It also assists with creating pull request descriptions. It is a middle ground between AI autocomplete and taking over control, impacting programmers in a nuanced way.

• What is the process for issue resolution using the AI tool in the workspace?

  The AI tool in the workspace helps with issue resolution by specifying the current state of files, proposing edits to fix the issue, allowing for user modifications, listing affected files and code changes, and adding them to a queue for the AI to write the code.

• How can co-pilot workspace be triggered?

  Co-pilot workspace can be triggered by clicking the code button or starting with a GitHub issue. It can be run in VS Code or GitHub code space.

• What is GitHub's co-pilot workspace?

  GitHub's co-pilot workspace is a new tool that can write, test, and execute code using natural language. It is seen as a major advancement in AI programming and provides enhanced control for developers.

• 00:00 GitHub has unveiled a new tool called co-pilot workspace that can write and execute code from natural language, raising questions about the future of coding and the role of humans in software development.
• 00:41 GPT-4 released, allowing natural language coding, Co-pilot workspace provides more control for developers, can be triggered via code button or GitHub issue.
• 01:18 An AI tool in a workspace helps with issue resolution by first specifying the current state of files, proposing edits to fix the issue, and allowing for user modifications. It then lists the affected files and code changes before adding them to a queue for the AI to write the code.
• 01:54 Co-pilot can help review and modify code before merging. It can be run in VS Code or GitHub code space. Co-pilot assists with creating pull request descriptions. It's a middle ground between AI autocomplete and taking over control. The impact of Co-pilot on programmers is more nuanced than expected.
• 02:30 AI and LLMS are increasing productivity in programming without significantly affecting jobs, but are posing a threat to the need for new software as they enable cheating and automation, leading businesses to integrate AI to avoid becoming obsolete.
• 03:15 The tools for writing code are getting easier, but it's not a threat to experienced developers. History shows that easier programming leads to more programmers.