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
What about developers who do enjoy writing for example high quality documentation? Do you expect that the status quo will be that most of the documentation will be AI slop and AI itself will just bruteforce itself through the issues? How close are we to the point where the AI could handle "tricky dependency updates", but not being able to handle "most interesting and complex problems"? Who writes the tests that are required for the "well tested" codebases for GitHub Copilot Coding Agent to work properly?

What is the job for the developer now? Writing tickets and reviewing low quality PRs? Isn't that the most boring and mundane job in the world?

>>petetn+aB
I'd argue the only way to ensure that is to make sure developers read high quality documentation - and report issues if it's not high quality.

I expect though that most people don't read in that much detail, and AI generated stuff will be 80-90% "good enough", at least the same if not better than someone who doesn't actually like writing documentation.

> What is the job for the developer now? Writing tickets and reviewing low quality PRs? Isn't that the most boring and mundane job in the world?

Isn't that already the case for a lot of software development? If it's boring and mundane, an AI can do it too so you can focus on more difficult or higher level issues.

Of course, the danger is that, just like with other automated PRs like dependency updates, people trust the systems and become flippant about it.

>>Cthulh+YS1
I think just having devs attempt to feed an agent openapi docs as context to create api calls would do enough. Simply adding tags and useful descriptions about endpoints makes a world of difference in the accuracy of agent's output. It means getting 95% accuracy with the cheapest models vs. 75% accuracy with the most expensive models.