hal9ai VS unofficial-observablehq-compiler

We are not using libfortran not gdpr, we are basically using whatever libraries are available for the web. Since most data scientists don't want to use JS per se, you can build the apps as blocks in the Hal9 site or using a soon-to-be-released Python/R package, see https://notebooks.hal9.com

Feel free to check out our repo as well, all the "primitives" / blocks code is in the scripts folder: https://github.com/hal9ai/hal9ai

https://hal9.com helps data scientists build faster web applications.

It uses WebGL and WebAssembly to process larger datasets, perform inference in the browser with TensorFlow.js, and enables running Python code with Pyodide.

If you want to build a web application on top of your ML project, give https://hal9.com a shot. We designed Hal9 with ease of use for deployment and maximum compatibility with web technologies that enable you to build ML apps with React, Vue, etc. We launched a couple months ago but could use some early feedback and users. Thank you!

BTW. If you are ever interested in helping us out, you can send a PR's to our GitHub repo. For instance, the summarize and convert blocks are here: https://github.com/hal9ai/hal9ai/blob/main/scripts/transforms/summarize.txt.js and https://github.com/hal9ai/hal9ai/blob/main/scripts/transforms/convert.txt.js

We are working in https://hal9.com which is language agnostic and allows you to compose different programming languages; however, we are focused at the moment at 1D-graphs but have plans to support 2D-graphs in the coming weeks.

If you want a demo or just time to chat, I'm available at javier at hal9.ai.

We would love to help out since this is a perfect use case for https://hal9.ai; we are about to release our beta version that makes this as easy as copy-pasting code. You can find me at javier at hal9.ai to find some time to chat and give you a walkthrough of our code-to-api functionality.

Hi there, would love to help you out. We are building a JavaScript + Python/R/AI service called https://hal9.ai to enable you to build React/Vue/Svelte/Angular apps against Python/R models. Want to send me an email to javier at hal9.ai to chat and help out?

Would love to hear your feedback for https://hal9.ai -- We are building an open source platform for data analysis based on reusable code blocks and a community to build and monetize their contributions. We are pretty early in our journey, launched our alpha and getting ready for our beta release, but would love to hear your thoughts. You can find me at javier at hal9.ai. Cheers!

As someone building an in-browser notebook I have a lot of opinions on notebook environments. Notebooks serve different purposes, sometimes the notebook itself is the end-goal because the author is creating an interactive tutorial or explaining a complex concept with a bunch of visualizations. Observable is a fantastic tool for that, and the kind-of-Javascript reactive programming system it is built on is a great fit for that.

Outside of that use-case, I think notebooks are great for the first 20% of the effort that gets 80% of the work done. If it turns out one also needs to do the other 80% of the effort to get the last 20%, it is time to "graduate" away from a notebook. For instance if I am participating in a Kaggle machine learning competition I may train my first models in a Jupyter notebook for quick iteration on ideas, but when I settle onto a more rigid pipeline and infra, I will move to plain Python files that I can test and collaborate on.

This "graduation" from notebook to the "production/serious" environment should be straightforward, which means there shouldn't be too much magic in the notebook without me opting into it. Documentation in my eyes is not so different, I should be able to copy the examples easily into my JS project without knowing specifics of Observable and adapt it to my problem. Saying "don't be lazy and just learn Observable", or "you must learn D3 itself properly to be able to use it anyway" is not helpful. Observable being a closed, walled garden doesn't help: not being able to author notebooks without using their closed source editor is a liability that I can totally understand makes it a non-starter for some companies and individuals.

I think it's ok to plug my own project: It's called Starboard [1] and is truly open source [2]. It's built on different principles: it's hackable, extendable, embeddable, shareable, and easy to check into git (i.e. I try to take what makes the web so great and put that in a notebook environment). You write vanilla JS/ES/Python/HTML/CSS, but you can also import your own more advanced cell types. Here's an example which actually introduces an Observable cell type [3] which is built upon the Observable runtime (which is open source) and an unofficial compiler package [4]. I would be happy for the D3 examples to be expressed in these really-close-to-vanilla JS notebooks, but I can convince the maintainers to do so.

[1]: https://starboard.gg

[2]: https://github.com/gzuidhof/starboard-notebook

[3]: https://starboard.gg/gz/open-source-observablehq-nfwK2VA

[4]: https://github.com/asg017/unofficial-observablehq-compiler