hitchstory VS bumblebee

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* Docs are better if they come attached to a guarantee that they're valid, not out of date and not missing crucial details.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

Despite some limitations, you will probably find Bumblebee (https://github.com/elixir-nx/bumblebee) interesting.

"Bumblebee provides pre-trained Neural Network models on top of Axon. It includes integration with HuggingFace Models, allowing anyone to download and perform Machine Learning tasks with few lines of code"

That's not bad at all!

There is also Nx and Bumblebee in Elixir land - it really changes the how one approaches running models in production. The fact that one can put together a service (or local process) running any model published to hugging face in a couple of lines of code is amazing.

[0] https://github.com/elixir-nx/bumblebee/blob/main/examples/ph...

[1] https://gist.github.com/toranb/8be408eaa97d5a5b795aec7d7fbee...

Theses notes describes how this can be done with the Elixir language and Nx, Axon (Nx-powered Neural Network library) and Bumblebee which provides pre-trained Neural Network models.

Apart from running code inside a "markdown" file, livebook can do much more. You have Smart cells to show charts, run sql queries against a db, run Neural Network tasks such as Image-To-Text generation using Bumblebee[1], etc. It is collaborative as well.

[1] https://github.com/elixir-nx/bumblebee

Elixir is an increasingly interesting platform for ML (see Nx, Axon, and more recently BumbleBee https://github.com/elixir-nx/bumblebee). I'm pretty happy to see this book released in beta.

José from the Livebook team. I don't think I can make a pitch because I have limited Python/R experience to use as reference.

My suggestion is for you to give it a try for a day or two and see what you think. I am pretty sure you will find weak spots and I would be very happy to hear any feedback you may have. You can find my email on my GitHub profile (same username).

In general we have grown a lot since the Numerical Elixir effort started two years ago. Here are the main building blocks:

* Nx (https://github.com/elixir-nx/nx/tree/main/nx#readme): equivalent to Numpy, deeply inspired by JAX. Runs on both CPU and GPU via Google XLA (also used by JAX/Tensorflow) and supports tensor serving out of the box

* Axon (https://github.com/elixir-nx/axon): Nx-powered neural networks

* Bumblebee (https://github.com/elixir-nx/bumblebee): Equivalent to HuggingFace Transformers. We have implemented several models and that's what powers the Machine Learning integration in Livebook (see the announcement for more info: https://news.livebook.dev/announcing-bumblebee-gpt2-stable-d...)

* Explorer (https://github.com/elixir-nx/explorer): Series and DataFrames, as per this thread.

* Scholar (https://github.com/elixir-nx/scholar): Nx-based traditional Machine Learning. This one is the most recent effort of them all. We are treading the same path as scikit-learn but quite early on. However, because we are built on Nx, everything is derivable, GPU-ready, distributable, etc.

Regarding visualization, we have "smart cells" for VegaLite and MapLibre, similar to how we did "Data Transformations" in the video above. They help you get started with your visualizations and you can jump deep into the code if necessary.

I hope this helps!

The current pipeline expects PCM audio and, if data is coming from a microphone in the browser, you can do the initial processing and conversion in the browser (see the JS in this single file Phoenix app speech to text example [0]).

On the other hand, if you expect a variety of formats (mp3, wav, etc), then shelling out or embedding ffmpeg is probably the quickest path to achieve something. The Membrane Framework[1] is an option here too which includes streaming. I believe Lars is going to do a cool demo with Membrane at ElixirConf EU next week.

[0]: https://github.com/elixir-nx/bumblebee/blob/main/examples/ph...

[1]: https://membrane.stream/

Outside of that, Elixir can be used for data pipelines, audio-video processing, and it is making inroads on Machine Learning with projects like Livebook, Nx, and Bumblebee.