hello world blog post

Signed-off-by: Danielle Lancashire <dani@builds.terrible.systems>

content/en/blog/news/first-post/index.md

@@ -1,17 +1,131 @@
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-date: 2024-02-28
+date: 2024-03-13
 title: Introducing SpinKube
 linkTitle: Introducing SpinKube
 description: >
-  A new open source project that streamlines the experience of developing, deploying, and operating Wasm workloads on Kubernetes.
-author: Fermyon Technologies ([@fermyontech](https://twitter.com/fermyontech))
+  Bringing the next wave of hyper-efficient serverless to Kubernetes.
+author: The SpinKube Team ([@SpinKube](https://mastodon.social/@SpinKube))
 resources:
   - src: "**.{png,jpg}"
     title: "Image #:counter"
 ---
+Today we're introducing SpinKube - an open source platform for efficiently 
+running Spin-based WebAssembly (Wasm) applications on Kubernetes. Built with love by 
+folks from Microsoft, SUSE, LiquidReply, and Fermyon.
 
-# Introducing SpinKube
+SpinKube combines the application lifecycle management of the [Spin Operator][spin-operator], the efficiency of the [containerd-shim-spin][containerd-shim-spin], and the node lifecycle management of the forthcoming [runtime-class-manager][runtime-class-manager] (formerly [KWasm][kwasm]) to provide an excellent developer and operator experience alongside excellent density and scaling characteristics regardless of processor architecture or deployment environment.
 
+>
+> TL;DR? Check out the [quickstart][quickstart] to learn how to deploy
+> Wasm to Kubernetes today.
+> 
 
+## Why Serverless?
 
+Containers and Kubernetes revolutionized the field of software development and operations. A unified packaging and dependency management system that was flexible enough to allow most applications to become portable - to run the same versions locally as you did in production - was a desperately needed iteration from the Virtual Machines that came before.
 
+But while containers give us a relatively light weight abstraction layer, they come with a variety of issues:
+
+- *Change management* - patching system dependencies (such as OpenSSL) need to happen on every image independently, which can become difficult to manage at large scale. 
+- *Rightsizing workloads* - managing access to shared resources like CPU and memory correctly is difficult, which often leads to over-provisioning resources (over introducing downtime) resulting in low utilization of host resources. 
+- *Size* - containers are often bigger than needed (impacting important metrics like startup time), and often include extraneous system dependencies by default which are not required to run the application.
+
+The desire to fix many of these issues is what led to the "first" generation of serverless.
+
+>
+> Borrowing ideas from CGI, serverless apps are not written as persistent servers. Instead serverless code responds to events (such as HTTP requests) - but does not run as a daemon process listening on a socket. The networking part of serverless architectures is delegated to the underlying infrastructure.
+> 
+
+First-generation serverless runtimes (such as AWS Lambda, Google Cloud Functions, Azure Functions, and their Kubernetes counterparts OpenWhisk and KNative) are all based on the principle of running a VM or container per function. While this afforded flexibility for application developers, complexity was introduced to platform engineers as neither compute type is designed to start quickly. Platform engineers operating these serverless platforms became responsible for an elaborate dance of pre-warming compute capacity and loading a workload just in time, making for a difficult tradeoff in cold start performance and cost. The result? A slow and inefficient first generation of Serverless.
+
+To illustrate this point, below are all the steps required to start a Kubernetes Pod:
+
+| Phase | Description |
+| --- | --- |
+| Kube init | Kubernetes schedules a Pod to a node, the node then prepares to run the Pod |
+| Image pull (cacheable) | The node pulls the container images from a remote repository to a local cache when needed |
+| Image mount | The node prepares the container images and mounts them |
+| Container start | The node configures and starts the Pod's containers and sidecars |
+| Application load | Each container loads application code and libraries, and initializes application memory. |
+| Application initialization | Each container performs application-specific init code (entry-point, setting up telemetry, validating connections to external dependencies, etc.) |
+| Pod ready | The Pod responds to the asynchronous readiness probe and signals that it is now ready |
+| Service ready | The Pod gets added to the list of active endpoints for a Service and can start receiving traffic |
+
+Circumventing this process, and the amount of duplication that occurs on every replica even if they are running on the same physical hardware, is where the [Spin][spin] WebAssembly Runtime and SpinKube shines.
+
+## Why SpinKube?
+
+SpinKube solves or mitigates many of the issues commonly associated with deploying, scaling, and operating serverless applications.
+
+The foundational work that underpins the majority of this comes from removing the Container. Spin Applications are primarily distributed as OCI Artifacts, rather than traditional container images, meaning that you only ship your compiled application and its assets - without system dependencies.
+
+To execute those applications we have a [runwasi](https://github.com/containerd/runwasi) based [containerd-shim-spin](https://github.com/spinkube/containerd-shim-spin/) that takes a Spin app, [pre-compiles it for the specific architecture](https://github.com/spinkube/containerd-shim-spin/pull/32) (caching the result in the containerd content store), and is then ready to service requests with [sub-millisecond startup times](https://fermyon.github.io/spin-benchmarks/criterion/reports/spin-executor_sleep-1ms/index.html) - no matter how long your application has been sitting idle.
+
+This also has the benefit of moving the core security patching of web servers, queue listeners, and other application boundaries to the host, rather than image artifacts themselves.
+
+But what if you mostly rely on images provided by your cloud provider? Or deploy to environments that are not easily reproducible?
+
+This is where [runtime-class-manager][runtime-class-manager] (coming soon, formerly [KWasm][kwasm]) comes into play - a Production Ready and Kubernetes-native way to manage WebAssembly runtimes on Kubernetes Hosts. With runtime-class-manager you can ensure that containerd-shim-spin is installed on your hosts, manage version lifecycles, and security patches - all from the Kubernetes API.
+
+By combining these technologies, scaling a Wasm application looks more like this:
+
+| Phase | Description |
+| --- | --- |
+| Kube init | Kubernetes schedules a Pod to a node, the node then prepares to run the Pod |
+| Image pull (cacheable) | The node pulls the needed container images from a remote repository to a local cache when needed |
+| Wasm Load (cachable) | The shim loads the Wasm and prepares it for execution on the node’s CPU architecture. |
+| Application start | The shim starts listening on the configured port and is ready to serve |
+| Pod ready | The Pod responds to the asynchronous readiness probe and signals that it is now ready |
+| Service ready | The Pod gets added to the list of active endpoints for a Service and can start receiving traffic |
+
+Or in practice:
+
+<video src="spinkube-scaling.mp4" width="80%" controls></video>
+
+
+## Deploying applications
+
+This leaves one major area that can be painful when deploying applications to Kubernetes today: the developer and deployment experience.
+
+This is where [Spin][spin], [the `spin kube` plugin](https://github.com/spinkube/spin-plugin-kube), and [`spin-operator`][spin-operator] start to shine.
+
+![Architecture diagram of the SpinKube project](spinkube-diagram.png)
+
+The spin-operator makes managing serverless applications easy - you provide the
+image and bindings to secrets, and the operator handles realizing that
+configuration as regular Kubernetes objects.
+
+Combining the operator with the `spin kube` plugin gives you superpowers, as it
+streamlines the process of generating Kubernetes YAML for your application and
+gives you a powerful starting point for applications.
+
+This is all it takes to get a HTTP application running on Kubernetes (after
+installing the containerd shim and the operator):
+
+```bash
+# Create a new Spin App
+spin new -t http-rust --accept-defaults spin-kube-app
+cd spin-kube-app
+
+# Build the Spin App
+spin build
+
+# Push the Spin App to an OCI registry
+export IMAGE_NAME=ttl.sh/spin-app-$(uuidgen):1h
+spin registry push $IMAGE_NAME
+
+# Scaffold Kubernetes manifests
+spin kube scaffold -f $IMAGE_NAME > app.yaml
+
+# Deploy to Kubernetes
+kubectl apply -f app.yaml
+```
+
+If you want to try things out yourself, the easiest way is to follow the [quickstart guide][quickstart] - we can’t wait to see what you build.
+
+[spin-operator]: https://github.com/spinkube/spin-operator
+[containerd-shim-spin]: https://github.com/spinkube/containerd-shim-spin
+[runtime-class-manager]: https://github.com/spinkube/runtime-class-manager
+[spin]: https://github.com/fermyon/spin
+[quickstart]: https://www.spinkube.dev/docs/spin-operator/quickstart/
+[kwasm]: https://kwasm.sh/