gitflow VS kubernetes

1. GitFlow

Pull Requests (or Merge Requests) are merged only when (1) all of the automated tests pass; and (2) enough necessary reviewers have indicated approval.

Git doesn't tell you when it's necessary to have full test coverage and manual infosec review in development cycles that produce releases, and neither do Pull Requests.

https://westurner.github.io/hnlog/#comment-19552164 ctrl-f hubflow

It looks like datasift's gitflow/hubflow docs are 404'ing, but the original nvie blog post [1] has the Git branching workflow diagrams; which the wpsharks/hubflow fork [3] of datasift/gitflow fork [2] of gitflow [1]has a copy of in the README:

[1] https://github.com/nvie/gitflow

[2] https://github.com/datasift/gitflow

[3] https://github.com/wpsharks/hubflow?tab=readme-ov-file

https://learngitbranching.js.org/ is still a great resource, and it could work on mobile devices.

The math of VCS deltas and mutable and immutable content-addressed DAG nodes identified by 2^n bits describing repo/$((2*inf)) bits ;

>> "ugit – Learn Git Internals by Building Git in Python" https://www.leshenko.net/p/ugit/

SLSA.dev is a social construct atop e.g. git, which is really a low-level purpose-built tool and Perl and now Python porcelain.

jj (jujutsu) is a git-compatible VCS CLI: https://github.com/martinvonz/jj

"Ask HN: Best Git workflow for small teams" (2016)

A successful Git branching model - известная модель ветвления от Vincent Driessen.

Before embarking on the project, we needed to decide on our Git workflow. Having experienced the unnecessary indirection and bureaucracy of Gitflow, I immediately proposed and implemented a trunk-based strategy instead.

As a best practice, consider creating a dev branch where you can stage your code and collaborate with other developers on your team. Alternatively, you can follow the Trunk-based development branching strategy. For larger teams and complex deployment cycles, the Gitflow branching model might be more suitable.

Note - this IS NOT Git Flow, which is way too complicated for what we're doing. Even the person who created Git Flow has some warnings and thoughts about it, 10 years later (https://nvie.com/posts/a-successful-git-branching-model/).

just use Gitflow

Git and this: https://nvie.com/posts/a-successful-git-branching-model/

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.