zlacker

>>net01+(OP)
> Copilot excels at low-to-medium complexity tasks in well-tested codebases, from adding features and fixing bugs to extending tests, refactoring, and improving documentation.

Bounds bounds bounds bounds. The important part for humans seems to be maintaining boundaries for AI. If your well-tested codebase has the tests built thru AI, its probably not going to work.

I think its somewhat telling that they can't share numbers for how they're using it internally. I want to know that Microsoft, the company famous for dog-fooding is using this day in and day out, with success. There's real stuff in there, and my brain has an insanely hard time separating the trillion dollars of hype from the usefulness.

>>taurat+O6
We've been using Copilot coding agent internally at GitHub, and more widely across Microsoft, for nearly three months. That dogfooding has been hugely valuable, with tonnes of valuable feedback (and bug bashing!) that has helped us get the agent ready to launch today.

So far, the agent has been used by about 400 GitHub employees in more than 300 our our repositories, and we've merged almost 1,000 pull requests contributed by Copilot.

In the repo where we're building the agent, the agent itself is actually the #5 contributor - so we really are using Copilot coding agent to build Copilot coding agent ;)

(Source: I'm the product lead at GitHub for Copilot coding agent.)

>>timrog+Oj
So I need to ask: what is the overall goal of your project? What will you do in, say, 5 years from now?

>>binary+Ml
What I'm most excited about is allowing developers to spend more of their time working on the work they enjoy, and less of their time working on mundane, boring or annoying tasks.

Most developers don't love writing tests, or updating documentation, or working on tricky dependency updates - and I really think we're heading to a world where AI can take the load of that and free me up to work on the most interesting and complex problems.

>>timrog+ln
But isn't writing tests and updating documentation also the areas where automated quality control is the hardest? Existing high quality tests can work as guardrails for writing business logic, but what guardrails could AI use to to evaluate if its generated docs and tests are any good?

I would not be surprised if things end up the other way around – humans doing the boring and annoying tasks that are too hard for AI, and AI doing the fun easy stuff ;-)