And so while we want to help generate code for tasks (e.g. from issue->PR), we also find that it's just super helpful to take an idea and make it more tangible/concrete. And then use that Workspace session to drive a conversation amongst the team, or spark the implementation. Especially since that might only take a couple clicks.
Within the GitHub Next team, I'll often file issues on one of the team's project repos, and then pause for a moment, before realizing I'm actually curious how it might be accomplished. So I'll open it in CW, iterate a bit on the plan, and then either 1) realize it's simple enough to just fix it, or 2) understand more about my intentions and use a shared session to drive a discussion with the team. But in either case, it's pretty nice to give my curiosity the space to progress forward, and also, capitalize on serendipitous learning opportunities.
So while AI-powered code generation is clearly compelling, I agree with you that there are other, more broadly interesting benefits to the idea->code environment that CW is trying to explore. We have a LOT of work to do, but I'm excited about the potential :)
What AI can really do well is take an already competent engineer and suddenly get rid of a lot of the annoying tedium they had to deal with. Whether it's writing boilerplate, doing basic project management, organizing brain dumps/brainstorming, etc.
This is certainly a long game though. I think GitHub with MS money can continue to lose money on Copilot for the next 5 years to gather data. For other VC ventures, I don't think they can wait that long.
Looking at the sad fate of Deep Mind (R.I.P), I feel that the shortermism generated by LLMs is going to be really painful
It takes an experienced eye to fix the code after, but overall it makes you a bit faster at those kinds of tasks.
But if you’re in thinking and exploration mode then turn it off. It’s a massive distraction then.