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jppittma

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Claude Design 3 months ago

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.

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.

Still a ways to go. I found out part of the reason for my linux bluetooth woes is that a settings in bluetooth called “JustWorks” and “FastConnect” default to off. Like. Why?

As if most linux users who enable bluetooth want it to connect slowly and not work.

Being a bit hyperbolic intentionally but the point still stands

Not sure where I fall on that one. In business, it is said that one ought to focus on core competencies and let other companies handle the rest. Why shouldn’t a country do the same? Should low level manufacturing be a core competency of the US? Why? Turning steel into screws is certainly lower margin than turning screws and engines into airplanes.

Here's the hope that the demand for software continues to increase as developer productivity rises, and that increases in developer productivity are partially captured in higher salaries.

I don’t really agree with this. The vast majority of the time, if you encounter an error at runtime, there’s not much you can do about it, but log it and try again. From there, it becomes about bubbling the error up until you have the context to do that. Having to handle bespoke error type from different libraries is actually infuriating, and people thinking this is a good idea makes anyhow mandatory for development in the language.

It's really gonna depend on the project. When my hobby project was greenfield, the AI was way better than I am. It was (still is) more knowledgable about the standards that govern the field and about low level interface details. It can shit out a bunch of code that relies on knowing these details in seconds/minutes, rather than hours/days.

Now that the project has grown and all that stuff is hammered out, it can't seem to consistently write code that compiles. It's very tunnel visioned on the specific file its generating, rather than where that fits in the context of what/how we're building what we're building.

Having worked on a graphical application in rust for a (albeit not a complex one) computers today are fast.. latencies top out at 3 ms with cpu based rendering in an application with just a few rendering optimizations.

The fact that you just draw on the screen instead of doing whatever html parsing / DOM/IR is probably doing it? And doing rendering on the gpu means extra delay in the processing moving from cpu to gpu and being a frame behind because of vsync.