Featured

Building a Fully Local, System-Wide AI Agent CLI on Linux

Cloud-based agentic CLI tools are undeniably convenient, but sending internal terminal workflows, system commands, and proprietary files to third-party endpoints is a persistent privacy risk. Running a fully local, autonomous agent directly on your workstation eliminates subscription costs, avoids rate limits, and grants you unconstrained access across all your attached drives. This guide details how to pair Ollama with Open Interpreter on modern Linux (Ubuntu), resolve tricky runtime quirks like Python 3.14 native builds and removed modules, map persistent storage, and create a one-click desktop launcher. System Architecture The setup consists of two layers: Inference Engine (Ollama): Serves local open-weight models with hardware GPU acceleration. Agent Harness (Open Interpreter): Interprets natural language queries, writes code, and executes Bash or Python commands directly on your system. For agentic coding and file management, qwen2.5-coder:14b provides an optimal balance of c...

Install Tokio runtime


  1. Ensure Rust is Installed
    If you haven't installed Rust yet, make sure to do so using rustup:

    winget install -e --id Rustlang.Rustup
    
  2. Create a New Rust Project
    If you're starting fresh, create a new Rust project:

    cargo new my_project
    cd my_project
    
  3. Add Tokio as a Dependency
    Open the Cargo.toml file in your project and add Tokio:

    [dependencies]
    tokio = { version = "1", features = ["full"] }
    

    Alternatively, you can run:

    cargo add tokio --features full
    
  4. Write a Basic Tokio Application
    Now, create a simple async function in main.rs:

    use tokio::time::{sleep, Duration};
    
    #[tokio::main]
    async fn main() {
        println!("Hello, Tokio!");
        sleep(Duration::from_secs(2)).await;
        println!("Done!");
    }
    
  5. Build and Run
    Compile and execute your program:

    cargo run

Comments