Tim from the Copilot coding agent team here. We've now disabled these tips in pull requests created by or touched by Copilot, so you won't see this happen again for future PRs.
We've been including product tips in PRs created by Copilot coding agent. The goal was to help developers learn new ways to use the agent in their workflow. But hearing the feedback here, and on reflection, this was the wrong judgement call. We won't do something like this again.
> Reviewing AI output all day without the dopamine of creation is not a sustainable job description.
I agree that reducing engineers’ careers to code review will lead to burnout (amongst other problems).
But I think the reason we’re headed in this direction is precisely because creating with AI /can/ deliver “the dopamine of creation”.
It doesn’t deliver that hit for everyone - but it does for the half of engineers who are more excited about building new things than the act of coding.
Teams build more and ship faster because it’s so much easier to do that with AI - and it’s fun - and that leads to increased review load.
That would be the normal pattern. But you could certainly stop after the LLM picks the tool and provides the arguments, and not present the result back to the model.
The travel agency is the one that collects your personal information - but it (unsurprisingly) immediately passes just about everything to the airline: name, date of birthday, phone number, email etc.
In general, the airline won’t get your payment details though.
GitHub PM here. We have tried this, but we weren't able to get results that we were satisfied with. Of course, you have to revisit these things regularly, as the models and wider state of the art are evolving so quickly!
Tim from the GitHub Copilot coding agent product team here!
@artdigital is on the money here. Our quick tip for beginners is to use `copilot-instructions.md` (which we can now generate for you <3), but for more serious use, we'd strongly recommend adding `copilot-setup-steps.yml`.
That gets you a deterministic setup - and for many teams, it'll be easy, as you can just copy and paste from existing Actions workflows.
Our asynchronous coding agent can run Docker in its GitHub Actions-powered development environment - for example it could start a Dockerized web server.
After hearing feedback from the community, we’re planning to share more on the GitHub Blog about how we’re using Copilot coding agent at GitHub. Watch this space!
At the moment, we're using Claude 3.7 Sonnet - but we're keeping our options open to experiment with other models and potentially bring in a model picker.
(Source: I'm on the product team for Copilot coding agent.)
As we've built Copilot coding agent, we've put a lot of thought and work into our security story.
One of the things we've done here is to treat Copilot's commits like commits from a first-time contributor to an open source project.
When Copilot pushes changes, your GitHub Actions workflows won't run by default, and you'll have to click the "Approve and run workflows" button in the merge box.
That gives you the chance to run Copilot's code before it runs in Actions and has access to your secrets.
(Source: I'm on the product team for Copilot coding agent.)
At the moment, we're using Claude 3.7 Sonnet, but we're keeping our options open to change the model down the line, and potentially even to introduce a model picker like we have for Copilot Chat and Agent Mode.
But would you rather get paid to spend your time doing the interesting and enjoying work, or the mundane and boring stuff? ;) My hope is that agents like Copilot can help us burn down the tedious stuff and make more time for the big value adds.
Nice idea! We're going to try to get together a blog post in the next couple of weeks on how we're using Copilot coding agent at GitHub - including to build Copilot coding agent ;) - and having some live stats would be pretty sweet too.