apollo-client VS kubernetes

Actually, it's worse than that. Next has started throwing errors if it statically detects you even _importing_ hooks inside of a React Server Component environment:

- https://github.com/apollographql/apollo-client/issues/10974

- https://github.com/apollographql/apollo-client/issues/11167

To the point that Lenz Weber( a maintainer of Apollo Client, and my co-maintainer on Redux Toolkit), is considering resorting to a package that wraps and re-exports all of React's public API just to avoid that static analysis:

- https://github.com/apollographql/apollo-client/pull/11175

Apollo Client (18k ⭐) -> A comprehensive state management library for JavaScript that enables you to manage both local and remote data with GraphQL. Use it to fetch, cache, and modify application data, all while automatically updating your UI.

This currently doesn't work as expected so I have logged this issue in apollo client repo.

I understand this has something to do with the fact, that SvelteKit expects the client and server code to be completely identical - this has already prompted changes to rxjs and apollo is still pending to be fixed. I understand the reason behind being able to run rxjs and apollo on the server but

There is a long-standing issue with error caching (its absence, that is), which leads to errors being rendered as a loading state on the server side. Therefore, if you're doing SSR, you might want to design your schema in such a way that there are no intentional errors in queries whatsoever. To do so, we can use null values instead of NOT_FOUND errors (see how we essentially treated errors as data here?). Note how nothing stops us from setting the response code to 404 in case of a null value, should we want so.

Lol apollo client too.. saw this issue opened like, immediately after the release https://github.com/apollographql/apollo-client/issues/10231

There were some caching issues, outlined here https://github.com/apollographql/apollo-client/issues/6916 to be aware of

If you look at how major backend projects structure their code, it's almost always object-oriented TypeScript.

I submit for the record:

- Apollo Client: https://github.com/apollographql/apollo-client/blob/main/src...

- Storybook: https://github.com/storybookjs/storybook/blob/next/lib/chann...

- Nest: https://github.com/nestjs/nest/blob/master/packages/core/nes...

- MongoDB Driver: https://github.com/mongodb/node-mongodb-native/blob/main/src...

- Prisma: https://github.com/prisma/prisma/blob/main/packages/engine-c...

[※Alert: The following is a code example using the Apollo Client notation, but the Apollo Client does not support Suspense at this time. Github issue about Suspense support: https://github.com/apollographql/apollo-client/issues/9627]

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.

Open Source and Cloud Computing: A Match Made in Heaven The cloud is accelerating OSS adoption. Cloud-native technologies like Kubernetes [https://kubernetes.io/] and Istio [https://istio.io/], both open-source projects, are revolutionizing how applications are built and deployed across cloud platforms.

If you do kubectl explain deployment than (surprise!) you'll get a description for extensions/v1beta1. Because kubectl explain works the same way, just like kubectl get:

Through the Fast Forward program, we give free services and support to open source projects and the nonprofits that support them. We support many of the world’s top programming languages (like Python, Rust, Ruby, and the wonderful Scratch), foundational technologies (cURL, the Linux kernel, Kubernetes, OpenStreetMap), and projects that make the internet better and more fun for everyone (Inkscape, Mastodon, Electronic Frontier Foundation, Terms of Service; Didn’t Read).

In this project, you will understand and get hands on experience around the entire concept around CI/CD from applications perspective. To fully gain real expertise around this idea, it is best to see it in action across different programming languages and from the platform perspective too. From the application perspective, we will be focusing on PHP here; there are more projects ahead that are based on Java, Node.js, .Net and Python. By the time you start working on Terraform, Docker and Kubernetes projects, you will get to see the platform perspective of CI/CD in action.