I'm excited about the social songwriting aspect here. It honestly hadn't occurred to me. It makes a ton of sense and it's something I wish I had when I was younger and searching for bandmates or songwriting accomplices. The idea that you can put an unfinished piece of your music out there with the intention that someone else helps turn it into something greater just feels like a direct hit on what the internet is for. Good luck with this!
Yup, that's right. It was over the course of about three years (2020-2023). Our JS team was made redundant when we switched to LiveView, which accounts for about half of that. Several of the others are actually still doing Elixir at other places now :).
I recently sat down with Brian Cardarella at DockYard to discuss some of the benefits we saw at Amplified after going all in on Elixir. This followed my talk at Code BEAM EU where I discussed our experience of a year of doing machine learning in production with Elixir [1].
I agree with some comments I've seen recently that too much content about Elixir is telling and not showing how great it is. So here's a case study in how consolidating around Elixir was a win.
Hey! I gave a talk at Code BEAM Europe this past year about our experience with a year of machine learning on the BEAM [1]. That is, the impact on our business of going 'all in' on Elixir from a more fragmented stack (Python for ML and ETL). We actually did this twice, once in 2020 from a JS front end/Elixir back end to LiveView, then again in 2022 for the ML/ETL stuff. We benefitted massively on both occasions. I recently sat down with Brian Cardarella at DockYard to talk about this and they wrote up some posts about it [2].
I overall agree with your sentiment (and this is not some bashing on Tyler's blog post by the way) - Elixir is good enough that we could focus on "Showing" instead of "Telling", at least that's what I'm trying to do more and more.
And the OP presentation about ML is spot on in that regard :-)
We use it in production at Amplified! It’s been a joy. We train our own large language models and have fine tuned and deployed using Elixir. I talked about it at ElixirConf this year (https://m.youtube.com/watch?v=Y2Nr4dNu6hI).
We were able to completely eliminate a few python services and consolidate to all Elixir for ETL and ML.
So is jupytext, rmd and qmd. But what do you do about the output?
The nice thing about markdown-like notebooks is that they play well with git. The nice thing about jupyter style notebooks is that they contain all the content needed to actually _read_ the notebook.
I've been working on a dataframe library for Elixir that's built on top of Polars and that's heavily influenced by dplyr if you're interested in checking it out: https://github.com/elixir-nx/explorer
