I think that the beauty of the human experience is that all you need to learn is to practice. You automatically improve at what you're doing. The kinds of skills that atrophy when you use AI are skills that AI can already automate. And nobody is going to pay you to do slowly what a machine can do quickly/cheaply.
When you deploy AI to build something, you wind up doing the work that the AI itself can't do. Holding large amounts of context, maintaining a vision, writing apis and defining interfaces. Alongside like, project management. How much time is spent on features vs refactoring vs testing.
The bikeshedding is coming from in the room. The point is that the feature didn't cause any regression in capability. And who tf wants a plugin system with only support for first party plugins?
That's a fair point. If you want to calculate the real total water usage of any person, you must first invent the universe. You have to cut it off somewhere.
See, I have no problem with searches that involve warrants and probably cause. They could already violate the shit out of your privacy with a warrant. That's kind of the point of a warrant.
The k8s api server is the thing that's configured to talk to your Thales or whatever. On managed kubernetes, these are usually preconfigured to talk to the vendor -- that's the difference between a secret and a config map. The secret is encrypted when it's stored in etcd.
You'd be forgiven for being mistaken however, because this encryption is handled in a way that's transparent to the application.
If you're talking about your application making a call to the k8s api server, then you shouldn't do that unless you're developing a plugin. The kubelet knows how to retrieve and mount secrets from the k8s api server and display them as environment variables to the application. You just declare it as a part of your deployment in the podspec.
If you're not developing k8s operators, you're calling the api server directly, then complaining about lock in, then that's a skill issue. If you're developing k8s operators, then you should use a tool like kind for integration tests and dependency injection for other stuff and the concept of lock in doesn't make sense. You can also deploy your helm chart directly to kind.
Yes you can? The container should be completely agnostic to the fact that it's running in kubernetes. You can do config the same way. Configmaps are mounted as regular files and environment variables. The application doesn't care if the configmap came from the cluster resource or a file your created on your dev machine with dev credentials. You can mount local files into the container yourself. It's docker run -v "source:destination" I think.
How is the kubernetes secret API lock in? Genuinely wondering - were you trying to use that deployment yaml for something other than a kubernetes deployment? For most applications, you should be mounting the secret on your application, then you can inject it as either an environment variable or a json file that your application reads in an environment agnostic way.
Then, on the backend, you can configure etcd to use whatever KMS provider you like for encryption.
But is every feature in a browser supposed to be standardized? Like, it's against the rules somehow to develop features without asking permission from Apple and Mozilla?
OTOH, are all of the browsers supposed to move in lock step? Is chrome supposed to wait for everyone else's approval before launching any kind of feature?
I think the hope is that you just start there. They might have migrated the meeting room devices. Why would you set out to replace *everything* at once? Do something, get some revenue/experience, then try to fan out.
When you deploy AI to build something, you wind up doing the work that the AI itself can't do. Holding large amounts of context, maintaining a vision, writing apis and defining interfaces. Alongside like, project management. How much time is spent on features vs refactoring vs testing.