Yeah I don't really mind using AI coding in work because it's boring as hell. And getting things done quicker is almost a virtue in the business world.
I should have clarified that my original comment is about side projects or serious software engineering.
Hey there, this looks interesting. What's the scope of this project? Is it meant to be closer to developers or do you see this being used in production?
How is progress of builds, observability, etc being tackled?
> What's the scope of this project? Is it meant to be closer to developers or do you see this being used in production?
Dagger is meant for both development and production. Note that Dagger doesn't run your application itself: only the pipelines to build, test and deploy it. So, although the project is still young and pre-1.0, we expect it will be production-ready more quickly because of the nature of the workloads (running a pipeline is easier than running an app).
> How is progress of builds, observability, etc being tackled?
All Dagger SDKs target the same Dagger Engine. End-users and administrators can target the engine API directly, for logging, instrumentation, etc. The API is not yet publicly documented, but will be soon.
We're also building an optional cloud service, Dagger Cloud, that will provide a lot of these features as a "turnkey" software supply chain management platform.
The problem is that mid, upper management and execs don't much care for how we feel about it.
They are literally measuring who is using AI and how much and will eventually make it into an excuse for poor performance.