pbrt-v3 VS helm

Physically Based Rendering[0] was an excellent textbook when I read it ages ago and conveniently enough it looks to have been updated with a new edition last year.

[0]: https://pbrt.org/

Physically Based Raytracing

Both of these books are free, and both are written in C++, but they can be done in any language. The first book, a raytracer in a weekend, is part of a series, you can find it here: https://raytracing.github.io/ And, if you get to the third book in that series, or you need a reference book, the PBRT book covers the math in more depth and discusses the latest theory, you can get the last edition of the book (5 years out of date) for free though: https://pbrt.org/

Making a “complete” one is a never-ending rabbit hole you can spend a lifetime on and is a very active area of research covering more advanced geometry, probability, optics, machine learning etc etc. A great introduction to that is https://pbrt.org

This isn't the highest priority but if you haven't already, learn how computer graphics works. Get a working knowledge of the camera matrix, real time graphics (say, OpenGL but threeJS is an option), and photorealistic graphics. PBRT is the go-to for photorealistic graphics. The first two books of Foundations of Game Engine Development are way more useful than they have any right to be (and my favorite textbooks I've ever read, 10/10).

Org-mode strength though is in working with different languages in a same source file, which I am not sure if Knuths version does. Anyway, to see how the original idea looks like, check the Wikipedia article, or to see it in real-life see some of books that are written in the literate style, like Physically Based Rendering, which seems to be available for free nowadays or C Interfaces and Implementations.

I contributed a tiny bit to pbrt[1], and one of the things I loved was that if you just plugged in physical values you almost always got great results with minimal tweaking.

The Octane data seems most complete at first glance (with complex IOR etc), but for things like milk and blood I expected at the very least some absorption coefficient for the translucency or similar.

[1]: https://pbrt.org/

Applying Kubernetes manifests individually is problematic because files can get overlooked. Packaging your applications as Helm charts lets you version your manifests and easily repeat deployments into different environments. Helm tracks the state of each deployment as a "release" in your cluster.

helm

Explanation here: https://github.com/helm/helm/issues/12681#issuecomment-19593...

Looks like it's a bug in Helm, but actually isn't Helm's fault, the issue was introduced by Fedora Linux.

Helm (Get from here https://helm.sh/)

It’s also well understood that having a k8s cluster is not enough to make developers able to host their services - you need a devops team to work with them, using tools like delivery pipelines, Helm, kustomize, infra as code, service mesh, ingress, secrets management, key management - the list goes on! Developer Portals like Backstage, Port and Cortex have started to emerge to help manage some of this complexity.

Kubernetes orchestrates deployments and manages resources through yaml configuration files. While Kubernetes supports a wide array of resources and configurations, our aim in this tutorial is to maintain simplicity. For the sake of clarity and ease of understanding, we will use yaml configurations with hardcoded values. This method simplifies the learning process but isn’t ideal for production environments due to the need for manual updates with each new deployment. Although there are methods to streamline and automate this process, such as using Helm charts or bash scripts, we’ll not delve into those techniques to keep the tutorial manageable and avoid fatigue — you might be quite tired by that point!

Helm is a package manager that automates Kubernetes applications' creation, packaging, configuration, and deployment by combining your configuration files into a single reusable package. This eliminates the requirement to create the mentioned Kubernetes resources by ourselves since they have been implemented within the Helm chart. All we need to do is configure it as needed to match our requirements. From the public Helm chart repository, we can get the charts for common software packages like Consul, Jenkins SonarQube, etc. We can also create our own Helm charts for our custom applications so that we don’t need to repeat ourselves and simplify deployments.

We can search for charts https://helm.sh/ . Charts can be pulled(downloaded) and optionally unpacked(untar).

Generally I felt as if I was diving in the deepest of waters without the correct equipement and that was horrifying. Unfortunately to me, I had to dive even deeper before getting equiped with tools like ArgoCD, and k8slens. I had to start working with... HELM.

Within the architecture of Cyclops, a central component is the Helm engine. Helm is very popular within the Kubernetes community; chances are you have already run into it. The popularity of Helm plays to Cyclops's strength because of its straightforward integration.