dhall-lang VS CPython

Fail to see how this is any different than Dhall (https://dhall-lang.org/) other than it produces plists too.

Kubernetes config is a decent example. I had ChatGPT generate a representative silly example -- the content doesn't matter so much as the structure:

https://gist.github.com/cstrahan/528b00cd5c3a22e3d8f057bb1a7...

Now consider 100s (if not 1000s) of such files.

I haven't given Pkl an in depth look yet, but I can say that the Industry Standard™ of "simple YAML" + string substitution (with delicate, error prone indentation -- since YAML is indentation sensitive) is easily beat by any of:

- https://jsonnet.org/

- https://nickel-lang.org/

- https://nixos.org/manual/nix/stable/language/index.html

- https://dhall-lang.org/

- (insert many more here, probably including Pkl)

There are underpowered languages / tools, that can only solve a problem for which they are intended poorly. But not all limited tools are like that.

Say, eBPF is prominently not Turing-complete, which allows to guarantee that a eBPF program terminates, and even how soon. Still eBPF is hugely useful in its area.

Or, say, regular expressions are limited to regular languages; in particular, they famously [1] cannot process recursive structures, like trees. Still tools like grep / ag / rg are mightily useful.

Yes, I agree that YAML is underpowered for proper k8s configuration! But it's also too powerful for its own good in other aspects [2]. I wish Google used Dhall [3] or their own purely functional config language (FCL? I already forgot the name) instead of YAML; sadly, they did not.

[1]: https://stackoverflow.com/a/1732454/223424

[2]: https://ruudvanasseldonk.com/2023/01/11/the-yaml-document-fr...

[3]: https://dhall-lang.org/

Dhall: Dhall is a programmable configuration language that combines features like JSON, functions, types, and import capabilities. Its style leans towards functional programming, so if you're familiar with functional-style languages such as Haskell, you might find Dhall to be quite intuitive.

I've been thinking along these lines but more 'strongly validated' than statically typed in the sense that you'd be better off being able to load the entire config and then produce a list of problems (and should be able to offer good editor support if done correctly).

Though https://dhall-lang.org/ demonstrates that you can statically type quite a lot of configuration to great advantage, which appears to be programmatically embeddable in multiple languages per https://docs.dhall-lang.org/howtos/How-to-integrate-Dhall.ht...

> Where practical is in the sense of an engineer (or in their terms, a CS practitioner),

Configuration processing. E.g. I'd like my yamls to be decidable, though I'd settle for guaranteed to halt[1].

[1] https://dhall-lang.org/

Maybe you'd like jsonnet: https://jsonnet.org/

I find it particularly useful for configurations that often have repeated boilerplate, like ansible playbooks or deploying a bunch of "similar-but" services to kubernetes (with https://tanka.dev).

Dhall is also quite interesting, with some tradeoffs: https://dhall-lang.org/

A few years ago I did a small comparison by re-implementing one of my simpler ansible playbooks: https://github.com/retzkek/ansible-dhall-jsonnet

And to checkout Dhall: https://dhall-lang.org/

Thankfully, Python, the go-to coding language for loads of developers, is here to save the day. It's got some awesome features for diving into text sentiment analysis. With cool libraries like Tweepy, we can sift through X(Twitter) data and snag those interesting tweets about Bitcoin. And then there's TextBlob, a clever tool for understanding the sentiment in text. When it's time to clean up and organize all that data, libraries like pandas and numpy are there to help out. And let's not forget about matplotlib, the master of visualisations that can help us see the trends in sentiment crystal clear. Armed with these tools, developers can really dig deep into social media data and figure out what the general public thinks about Bitcoin.

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> It provides a meaningful performance improvement for at least one popular platform (realistically, on the order of 5%).

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After that, with the basics of software engineering understood, I would move on to a wider use language, with a bigger ecosystem to employ, most likely Python. This would expose me to large system design / distributed systems and architectural challenges...

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