pack VS language

I've recently found this quote by Kelsey Hightower:

"I'm convinced the majority of people managing infrastructure just want a PaaS. The only requirement: it has to be built by them."

Source: https://twitter.com/kelseyhightower/status/85193508753294540...

In the last few weeks, I've experimented a bit with Flux (https://fluxcd.io/), Tekton (https://tekton.dev/) and Cloud Native Buildpacks (https://buildpacks.io/) on how to provide K8s powered git push deployments without using a dedicated CI/CD server.

My project is still in early alpha stage and just a proof of concept :-) My vision is to expand it into an Open Source PaaS in the future.

Do you think the above quote is true? What does an open source PaaS need to be like in order to be accepted by software developers?

Some other projects have been discontinued in the past (like Flynn or Deis) or were created before the Kubernetes era.

Is it the right direction to provide a Heroku like solution based on K8s or is it better to provide an Open Source Infrastructure as Code library with building blocks to avoid everything from scratch?

```bash (curl -sSL "https://github.com/buildpacks/pack/releases/download/v0.28.0/pack-v0.28.0-linux-arm64.tgz" | sudo tar -C /usr/local/bin/ --no-same-owner -xzv pack)

Although Dockerfiles have the benefit of migrating existing workloads to containers without having to update your toolchain, I definitely prefer the container-first workflow. Cloud Native [Buildpacks](https://buildpacks.io/) are a CNCF incubating project but were proven at Heroku. Buildpacks support common languages, but working on a Go project I've also had a great experience with [ko](https://ko.build/). Free yourself from Dockerfile!

Cloud Native Buildpacks

It's big and overwhelming and sometimes scary. But you know what? It's also fun, engaging, and very refreshing. Because I'm a DevRel, I don't have many chances to focus on something particular. Still, I'm having a lot of fun exploring different CMSs (like Statamic, Craft, or Sanity), new approaches (at last, I understood why the headless approach is so important), and diving into tech I never used before (hello Buildpacks).

I noticed that I can enable inline-class as an experiment to play with Extension Types. You need to also add sdk: ^3.3.0-0 to your pubspec.yaml.

Current syntax is not all that bad if you are going to do OO and add various helper methods on `Message` and its subclasses, but if you just want to define your data and no behavior / helpers - then it is exceedingly verbose.

[1]: https://github.com/dart-lang/language/issues/3021

Reference

A future version of Dart will probably support macros which should make this all a bit easier to use, similar to how Swift 5.9 works which makes already fantastic use of its new macro capabilities by integrating mobx (or solidjs) like reactivity into SwiftUI by a harmlessly looking @Obervable annotation.

Coming from a Dart context here where that team is also looking at adding Macros to the language. It was really interesting to compare and contrast some of the approaches https://github.com/dart-lang/language/blob/main/working/macr...

Yes, that needs a bit of boilerplate for the constructor declaration and the extra build method, but I personally don't mind and with implicit constructors this will become much easier. Also, you get a performant UI as Flutter knows to not redraw widgets that didn't change.

I know that it would be nice not to use the generator at all, but we have to wait until static metaprogramming is implemented in dart. https://github.com/dart-lang/language/issues/1482

I implemented a prototype version of the algorithm in that paper when exploring exhaustiveness checking for pattern matching in Dart.

I found it pretty easy to understand, but also really easy to get it to generate huge combinatorially large spaces. Some careful memoization and deduplication helped, but even so I never got the performance to a state I considered acceptable.

Instead, I went with Luc Maranget's classic approach and figured out a way to adapt it to a language with subtyping (with a ton of work from Johnni Winther to figure out all of the hard complex cases around generics):

https://github.com/dart-lang/language/blob/main/accepted/fut...

The performance (in the prototype!) was dramatically better. You can always make pattern matching go combinatorial, but I haven't seen any real-world switches get particularly slow with our approach yet, and we have some fairly large tests of matching on tuples of enums.