For our code assistant use cases the local inference on Macs will tend to favor workflows where there is a lot of generation and little reading and this is the opposite of how many of use use Claude Code.

Source: I started getting Mac Studios with max ram as soon as the first llama model was released.

nix run --impure 'git+https://codeberg.org/ingenieroariel/arcan?ref=nix-flake-buil...'

Comments URL: https://news.ycombinator.com/item?id=46887326

Points: 73

# Comments: 19

Comments URL: https://news.ycombinator.com/item?id=46773133

Points: 2

# Comments: 0

An article called "A Spreadsheet and a Debugger walk into a Shell" [0] by Bjorn (letoram) is a good showcase of an alternative to cells in a Jupyter notebook (Excel like cells!). Another alternative a bit more similar to Jupyter that also runs on Arcan is Pipeworld.

[0] https://arcan-fe.com/2024/09/16/a-spreadsheet-and-a-debugger... [1] https://arcan-fe.com/2021/04/12/introducing-pipeworld/

PS: I hang out at Arcan's Discord Server, you are welcome to join https://discord.com/invite/sdNzrgXMn7

Chris Randall is pretty awesome too: https://www.instagram.com/chris.randall.art/

Since it is a complete toolkit, you can have detachable applications where you send both code and state to a server and retrieve it from another device (like Apple's continuity).

In the end it is just a bunch of lua scripts talking to other components via /dev/shm and to other computers using a new protocol called a12://

Comments URL: https://news.ycombinator.com/item?id=42516575

Points: 77

# Comments: 9

nix run nixpkgs#pyspread [0/1 built, 3/113/132 copied (1311.8/1721.6 MiB), 280.4/300.7 MiB DL] fetching llvm-16.0.6 from https://cache.nixos.org

https://pasteboard.co/P1eh7B7W8C9R.png

There is a library by the same author called lonboard that provides the JS bits inside JupyterLab. https://github.com/developmentseed/lonboard

I think it is based on the Kepler.gl / Deck.gl data loaders that go straight to GPU from network.

I kept getting emails about small changes and the bills got bigger all over the place including BigQuery and how they dealt with queries on public datasets. Bill got higher.

There is a non zero chance I conflated things. But from my point of view: I created a system and let it running for years - afterwards bills got higher out of the blue and I moved out.

   { pkgs, ... }: {
   services.postgres = {
    enable = true;
    package = pkgs.postgresql_15;
    initialDatabases = [{ name = "mydb"; }];
    extensions = extensions: [
      extensions.postgis
      extensions.timescaledb
    ];
    settings.shared_preload_libraries = "timescaledb";
    initialScript = "CREATE EXTENSION IF NOT EXISTS timescaledb;";
  };
   }

A project funded by the EU to bring Nix to Windows.

(edit: typo and clarity)

System is a Mac Studio with 128GB + Asahi Linux + mmapped parquet files and DuckDB, it also runs airflow for us and with Nix can be used to accelerate developer builds and run the airflow tasks for the data team.

GCP is nice when it is free/cheap but they keep tabs on what you are doing and may surprise you at any point in time with ever higher bills without higher usage.

In my use case, the niche is not having WSL/Docker available and letting end users repeat studies or re-run configuration scripts.