hitchstory VS cue

I built something like this too:

https://github.com/hitchdev/hitchstory/blob/master/examples%...

I took the same approach to "docs are tests and tests are docs" with integration testing when I created this library: https://github.com/hitchdev/hitchstory

I realized at some point that a test and a how-to guide can and should actually be the same thing - not just for doctests, but for every kind of test.

It's not only 2x quicker to combine writing a test with writing docs, the test part and the docs part reinforce each other:

* Tests are more easily understandable when you attach written context intended for human consumption.

* Docs are better if they come attached to a guarantee that they're valid, not out of date and not missing crucial details.

* TDD is better if how-to docs are created as a side effect.

I have created a project for easily writing this type of test with YAML:

https://github.com/hitchdev/hitchstory

I dont think that this type of task is really appropriate for an LLM though. It is better to use hard abstractions for the truly deterministic stuff and for other stuff where you may need to do subtle trade offs (e.g. choosing a selector for the search bar) an LLM will generally do a bad job.

For those interested in the concept of having permanently up-to-date documentation with screenshots I built this testing framework based upon the idea that good documentation can be a autogenerated artefact of good tests:

https://github.com/hitchdev/hitchstory

I don't like gherkin. It's it has very awkward syntax, it's not type safe, it's very verbose, it has no ability to abstract scenarios and rather than being a source for generating the documentation it tries to be the documentation.

Nonetheless, there is a small number of projects where they either work around this or it doesn't matter as much. I find that most people that apply gherkin to their projects find it doesn't work - usually for one of the above reasons.

I built https://github.com/hitchdev/hitchstory as an alternative that has straightforward syntax (YAML), very strict type safety (StrictYAML), low verbosity, and is explicitly designed as a source for generating documentation rather than trying to be the documentation.

I built this because I had the same idea: https://github.com/hitchdev/hitchstory

If the specification can be tested and used to generate docs and can be rewritten based upon program output then the maintenance cost for producing docs like these plunges.

-c fsync=off -c synchronous_commit=off -c full_page_writes=off

I got the answer from Karen Jex at Djangocon 2023.

I used it to build some integration tests which exhibit best practices: https://github.com/hitchdev/hitchstory/tree/master/examples/...

I considered using tmpfs but I wanted to cache the entire database volume and couldnt figure out how to do that with podman.

This is incredible work.

To anyone curious, I highly recommend:

- https://hitchdev.com/hitchstory/approach/

- https://hitchdev.com/hitchstory/why-not/

From the overall RDD/BDD type home page:

- https://hitchdev.com/hitchstory/

The entire product site is a thing of richly informative beauty.

---

My only question was whether the generated 'docs' snippets would add value over just reading the story in your DASL. Any markdown site generator (such as the chosen Material for MKDocs) can just embed the ```yaml anyway. But then I realized what was generating e.g. …

- https://hitchdev.com/hitchstory/using/engine/rewrite-story/

… and how superior that is to typical docs, especially typical docstring or swagger factories.

If you are in a situation where you have a backend and you want to expose an API and then you would eventually want a client, you would need format specs as the starting point where server and clients are generated from that one source.

At the moment, OpenAPI with YAML is the only way to go but you can't easily split the spec into separate files as you would do any program with packages, modules and what not.

There are third party tools[0] which are archived and the libraries they depend upon are up for adoption.

In that space, either you can use something like cue language 1] or something like TypeSpec which is purpose built for this so yet, this seems like a great tool although I have not tried it yet myself.

[0]. https://github.com/APIDevTools/swagger-cli

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

EDIT: formating

Where `kube.cue` sets reasonable defaults (e.g. image is /). The "cluster" runs on a mini PC in my basement, and I have a small Digital Ocean VM with a static IP acting as an ingress (networking via Tailscale). Backups to cloud storage with restic, alerting/monitoring with Prometheus/Grafana, Caddy/Tailscale for local ingress.

[1] https://www.talos.dev/

[2] https://cuelang.org/

I've been somewhat surprised that CUE bills itself as "tooling friendly" and doesn't yet have a language server- the number one bit of tooling most devs use for a particular language.

I'm assuming it's becaus CUE is still unstable?

Anyway, if others are interested in CUE's LSP work, I think https://github.com/cue-lang/cue/issues/142 is the issue to subscribe to

This is where I usually pitch in with "Have your heard of CUELang, our lord and savior?": https://cuelang.org/

- Not turing complete

CUE: The core problem CUE solves is "type checking", which is mainly used in configuration constraint verification scenarios and simple cloud native configuration scenarios.

If you really want executable configurations please consider a newer language like https://dascript.org or https://cuelang.org which provide better type safety.

1- https://news.ycombinator.com/item?id=38030778

Markdown and XML are nice, but what about more advanced documentation formats like OpenAPI? For one recent project, I set up automatic generation of the OpenAPI docs from (much more compact and flexible) CUE definitions (https://cuelang.org/) - which has the bonus of also being able to test the API against the definitions. JetBrains has a CUE plugin, but it's really barebones (doesn't even support jumping from the usage of a schema to its definition). Of course the possibilities when generating docs are endless (just think of the various syntaxes for doc comments, embedding examples/tests in source code etc.)...

It doesn't include validators for TOML and INI, but if you're doing JSON and YAML, I would take a look at using or building upon CUE (https://cuelang.org/). It is a different take on schema definition (plus more), and is surprising terse and powerful model.