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Definitely look into Livebook and Elixir, and the whole ecosystem around it, including:
- https://github.com/elixir-nx/axon Nx-powered Neural Networks
- https://github.com/elixir-nx/nx Multi-dimensional arrays (tensors) and numerical definitions for Elixir
- https://github.com/elixir-nx/scholar Traditional machine learning on top of Nx
- https://github.com/elixir-nx/bumblebee Pre-trained Neural Network models in Axon (+ Models integration)
- https://github.com/elixir-explorer/explorer Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
- https://fly.io/blog/rethinking-serverless-with-flame/ (for offloading large work to remote containers)
- https://www.youtube.com/watch?v=RABXu7zqnT0 InstructorEx
And of course Livebook (https://livebook.dev)
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InfluxDB
InfluxDB high-performance time series database. Collect, organize, and act on massive volumes of high-resolution data to power real-time intelligent systems.
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We've made an open source fork of Jupyter - kind of like Cursor but for Jupyter.
See GH: https://github.com/pretzelai/pretzelai/
You can install it with pip install pretzelai (in a new environment preferably) - then run it with pretzel lab. You can bring your own keys or use the default free (for now) AI server.
We also have a hosted version to make it easy to try it out: https://pretzelai.app
Would love to get your feedback!
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Imports is the thing that makes it useless. You cannot import stuff normally in the kernel..
Yes, my current project is https://github.com/srcbookdev/srcbook
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Definitely look into Livebook and Elixir, and the whole ecosystem around it, including:
- https://github.com/elixir-nx/axon Nx-powered Neural Networks
- https://github.com/elixir-nx/nx Multi-dimensional arrays (tensors) and numerical definitions for Elixir
- https://github.com/elixir-nx/scholar Traditional machine learning on top of Nx
- https://github.com/elixir-nx/bumblebee Pre-trained Neural Network models in Axon (+ Models integration)
- https://github.com/elixir-explorer/explorer Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
- https://fly.io/blog/rethinking-serverless-with-flame/ (for offloading large work to remote containers)
- https://www.youtube.com/watch?v=RABXu7zqnT0 InstructorEx
And of course Livebook (https://livebook.dev)
-
Definitely look into Livebook and Elixir, and the whole ecosystem around it, including:
- https://github.com/elixir-nx/axon Nx-powered Neural Networks
- https://github.com/elixir-nx/nx Multi-dimensional arrays (tensors) and numerical definitions for Elixir
- https://github.com/elixir-nx/scholar Traditional machine learning on top of Nx
- https://github.com/elixir-nx/bumblebee Pre-trained Neural Network models in Axon (+ Models integration)
- https://github.com/elixir-explorer/explorer Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
- https://fly.io/blog/rethinking-serverless-with-flame/ (for offloading large work to remote containers)
- https://www.youtube.com/watch?v=RABXu7zqnT0 InstructorEx
And of course Livebook (https://livebook.dev)
-
Definitely look into Livebook and Elixir, and the whole ecosystem around it, including:
- https://github.com/elixir-nx/axon Nx-powered Neural Networks
- https://github.com/elixir-nx/nx Multi-dimensional arrays (tensors) and numerical definitions for Elixir
- https://github.com/elixir-nx/scholar Traditional machine learning on top of Nx
- https://github.com/elixir-nx/bumblebee Pre-trained Neural Network models in Axon (+ Models integration)
- https://github.com/elixir-explorer/explorer Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
- https://fly.io/blog/rethinking-serverless-with-flame/ (for offloading large work to remote containers)
- https://www.youtube.com/watch?v=RABXu7zqnT0 InstructorEx
And of course Livebook (https://livebook.dev)
-
Definitely look into Livebook and Elixir, and the whole ecosystem around it, including:
- https://github.com/elixir-nx/axon Nx-powered Neural Networks
- https://github.com/elixir-nx/nx Multi-dimensional arrays (tensors) and numerical definitions for Elixir
- https://github.com/elixir-nx/scholar Traditional machine learning on top of Nx
- https://github.com/elixir-nx/bumblebee Pre-trained Neural Network models in Axon (+ Models integration)
- https://github.com/elixir-explorer/explorer Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
- https://fly.io/blog/rethinking-serverless-with-flame/ (for offloading large work to remote containers)
- https://www.youtube.com/watch?v=RABXu7zqnT0 InstructorEx
And of course Livebook (https://livebook.dev)
-
CodeRabbit
CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
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explorer
Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
Definitely look into Livebook and Elixir, and the whole ecosystem around it, including:
- https://github.com/elixir-nx/axon Nx-powered Neural Networks
- https://github.com/elixir-nx/nx Multi-dimensional arrays (tensors) and numerical definitions for Elixir
- https://github.com/elixir-nx/scholar Traditional machine learning on top of Nx
- https://github.com/elixir-nx/bumblebee Pre-trained Neural Network models in Axon (+ Models integration)
- https://github.com/elixir-explorer/explorer Series (one-dimensional) and dataframes (two-dimensional) for fast and elegant data exploration in Elixir
- https://fly.io/blog/rethinking-serverless-with-flame/ (for offloading large work to remote containers)
- https://www.youtube.com/watch?v=RABXu7zqnT0 InstructorEx
And of course Livebook (https://livebook.dev)
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Perhaps not quite what you're looking for but Ankane does (a lot) of great work, e.g. https://github.com/ankane/torch.rb.
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This ironically renders better for me straight from Github than it does on nbviewer.org:
https://github.com/SciRuby/sciruby-notebooks/blob/master/get...
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While the example-notebooks repo you've mentioned is not actively maintained it looks like the IRuby kernel used by Jupyter still is maintained: https://github.com/SciRuby/iruby
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There is also jupyter_on_rails [0] which integrates both. Using it feels so good, I love how an app can suddently become a playground or a sandbox.
[0]: https://github.com/Yuki-Inoue/jupyter_on_rails
