wasi-libc VS k3s

Now there are projects like WASI that allows for interfacing with system resources for WASM code this allows for devs to target WASM runtime for their apps sliding the apps to run locally on any OS without any porting required. This could be a game changer in the future like Docker and containers was in the past decade.

So it doesn’t seem like there has been progress on a pseudo-random number generator function for typst, but there are multiple other ways to solve this: 1. Just don’t. Typst has this functional philosophy, there one input always produces the same output. (not an answer to your question tho) 2. Interface with a webassembly module which has a random number generator. So you could e.g. compile c to wasm and statically link a libc version. You would then just have to export the rand() function. (You could use any lang for this, which has a stdlib with a pseudo random number generator) 3. Implement your own. Random number generators are actually not that hard something like an LCG isn’t to complex. (Id provide an example but im on my phone rn)

Actually WASI[0] will be a better alternative, IIRC extism serialize and deserialize the data that you want to pass every time, adding a lot of overhead.

[0] https://wasi.dev

Actually, it was in wasi-libc: https://github.com/WebAssembly/wasi-libc/blob/main/libc-bott...

Proper isolation in C# is only now becoming a thing, with .Net support for WASI, which is essentially a WebAssembly sandbox which can be given extremely granular privileges (such as access to spefic file system directories, or an effective virtual file system). As an upside, the idea is that it should be possible to write the WASI packages in more or less anything.

Wasm Labs dev here :)

In mod_wasm, there are some differences with a pure CGI implementation. When Apache boots, it loads the configuration and initializes the WasmVM. When a new HTTP request arrives, the VM is ready so you don't need to initialize a different process to manage it.

You still need to process the request and pass the data to the Wasm module. This step is done via STDIN through the WebAssembly System Interface (WASI) implementation [0]. The same happens in the opposite direction, as the module returns the data via STDOUT.

So, the CGI pattern is still there, but it doesn't require new processes and all the code runs in a sandbox.

However this is not the only way you can run a Wasm module. In this specific case, we use CGI via WASI. In other cases, you may compile a module to fulfill a specific API, like ProxyWasm [1] to create HTTP filters for proxies like Envoy.

- [0] https://wasi.dev/

- [1] https://github.com/proxy-wasm/spec

Never done that, but I think you need this: https://wasi.dev/

The Python builds from the WebAssembly language runtimes [0] project target the WebAssembly System Interfaces (WASI) [1]. It allows the Python interpreter to interact with resources like the filesystem.

Many server-side Wasm runtimes supports WASI out of the box. For the browser, you need to provide a polyfill to emulate these resources like the one provided by the WASI team [2].

Regarding SQLite, these builds include libsqlite so you should be able to use it :)

- [0] https://github.com/vmware-labs/webassembly-language-runtimes

- [1] https://wasi.dev/

- [2] https://wasi.dev/polyfill/

Effective debugging results in effective programming; itk-wasm makes effective debugging of WebAssembly possible. In this tutorial, adapted from the itk-wasm documentation, we walk through how to debug a C++ data processing pipeline with the mature, native binary debugging tools that are comfortable for developers. This is a fully featured way to ensure the base correctness of a processing pipeline. Next, we will walk through an interactive debugging experience for WASI WebAssembly. With itk-wasm, we can debug the same source code in either context with an interactive debugger. We also have a convenvient way to pass data from our local filesystem into a WebAssembly (Wasm) processing pipeline.

Communication between the WASM module and the rest of the application needs to be done in very simple types (bytes, ints and floats). No complex types are supported yet. This is why most WASM compilers also provide some glue-code to map between complex types like strings or arrays. The Web Assembly System Interface (WAS) is an on-progress standard aimed to solve this last limitation; once it's mature it will allow easy interoperation with almost every environment. WASI is already available in some WSAM compilers and runtimes.

Opinionated meaning it picks, install, patches your CNI/Ingress/Load Balancer/DNS Server/Metrics Server/Monitoring Setup.

k3s is probably most well known as it ships with bunch of preinstall software: https://github.com/k3s-io/k3s so you can just start throwing yaml files at cluster and handling workloads. It's what I use for my homelab.

Paid things I've heard of include OpenStack and SideroLabs. Haven't used personally by SRE coworkers say good things about them.

For self-hosting I've found https://k3s.io to be really good from the SUSE people. Works on basically any Linux distro and makes self-hosting k8s not miserable.

Yes it’s going to depend on which k8s distribution you’re using. We have work in-progress for k3s to natively support nix-snapshotter: https://github.com/k3s-io/k3s/pull/9319

For other distributions, nix-snapshotter works with official containerd releases so it’s just a matter of toml configuration and a systemd unit for nix-snapshotter.

We run Kubernetes outside of NixOS, but yes the NixOS modules provided by the nix-snapshotter certainly make it simple.

K3S: is a lightweight distribution of Kubernetes that is designed for resource-constrained environments. It is an excellent option for running Kubernetes on a virtual machine or cloud server.

I recently purchased a used Lenovo M900 Think Centre (i7 with 32GB RAM) from eBay to expand my mini-homelab, which was just a single Synology DS218+ plugged into my ISP's router (yuck!). Since I've been spending a big chunk of time at work playing around with Kubernetes, I figured that I'd put my skills to the test and run a k3s node on the new server. While I was familiar with k3s before starting this project, I'd never actually run it before, opting for tools like kind (and minikube before that) to run small test clusters for my local development work.

Kubernetes - k3s distribution

I’ve created a local cluster with K3S and installing Windmill could not be simpler with just one chart to configure, which already has sane defaults to get started. For this demo we will also configure workers to passthrough environment variables to our scripts so that they have access to the Kubernetes API server for later.

You should be familiar with Kubernetes and have set up a Kubernetes cluster. I recommend k3s.