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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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explorer
Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
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Ray
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Right, so our goal was to allow configuring the bundle with app icons etc and importantly custom URL schemes and file types [1]. And then macOS- [2] and Windows- [3] specific launchers capture the "open file" and "open url" events and trigger the Elixir side of things. Another reason for native launchers is if for any reason we couldn't start the Elixir side, we want to let the user know that something went wrong and point to logs. (We have that on Mac and will have on Windows very soon.)
[1]: https://github.com/livebook-dev/livebook/blob/920f70817efbac...
[2]: https://github.com/livebook-dev/livebook/blob/920f70817efbac...
[3]: https://github.com/livebook-dev/livebook/blob/920f70817efbac...
I've wondered whether it's easier to add data analyst stuff to Elixir that Python seems to have, or add features to Python that Erlang (and by extension Elixir) provides out of the box.
By what I can see, if you want multiprocessing on Python in an easier way (let's say running async), you have to use something like ray core[0], then if you want multiple machines you need redis(?). Elixir/Erlang supports this out of the box.
Explorer[1] is an interesting approach, where it uses Rust via Rustler (Elixir library to call Rust code) and uses Polars as its dataframe library. I think Rustler needs to be reworked for this usecase, as it can be slow to return data. I made initial improvements which drastically improves encoding (https://github.com/elixir-nx/explorer/pull/282 and https://github.com/elixir-nx/explorer/pull/286, tldr 20+ seconds down to 3).
[0] https://github.com/ray-project/ray
I've wondered whether it's easier to add data analyst stuff to Elixir that Python seems to have, or add features to Python that Erlang (and by extension Elixir) provides out of the box.
By what I can see, if you want multiprocessing on Python in an easier way (let's say running async), you have to use something like ray core[0], then if you want multiple machines you need redis(?). Elixir/Erlang supports this out of the box.
Explorer[1] is an interesting approach, where it uses Rust via Rustler (Elixir library to call Rust code) and uses Polars as its dataframe library. I think Rustler needs to be reworked for this usecase, as it can be slow to return data. I made initial improvements which drastically improves encoding (https://github.com/elixir-nx/explorer/pull/282 and https://github.com/elixir-nx/explorer/pull/286, tldr 20+ seconds down to 3).
[0] https://github.com/ray-project/ray
From the discord blog posts it seems that elixir powers the chat system, with rust and python as the other two main languages in their stack.
As for whatsapp, they are mainly a erlang shop and yesterday they open sourced a type checker for erlang:
https://github.com/WhatsApp/eqwalizer
I think this is a fair analogy, but maybe I'd add that LiveBooks uses MarkDown markupfor the non-code part of the notebook, make it far more readable, and with more visualizations coming in.
For example, recently an addition to add automatically generated sequence diagrams to Livebook Kino (https://github.com/livebook-dev/kino/pull/165). So generate code which shows a supervision tree, and automatically create a visual diagram of the code in one place. Pretty great for learning and explanatory material.
Visidata† and Datasette†† are both excellent for rapid exploration. Both are available via Python pip.
† _ https://www.visidata.org/
†† _ https://datasette.io/