RandyMcMillan

use sha2::{Digest, Sha256};
use std::collections::HashSet;
use rand_0_8_5/*_0_9_2*/::{RngCore, thread_rng};

/// For a 256-bit security level, we need 256 pairs of preimages (512 total).
const BITS: usize = 256;

/// The Private Key: Two lists (Secret-0 and Secret-1).
struct PrivateKey {
    pairs: [[[u8; 32]; BITS]; 2],

RandyMcMillan

var spawn = require('child_process').spawn;

// spawn_detached(file, [args = []], [options = {}], [callback]);
function spawn_detached(file, args, options, callback) {
  if (arguments.length == 2 &&
      typeof args == 'function') {
    callback = arguments[1];
    args = undefined;
  }

RandyMcMillan

"""
The most atomic way to train and inference a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

RandyMcMillan

use std::env;
use std::fs::File;
use std::io::{BufRead, BufReader, Read, Write, Result};

/* ------------------------------------------------------------------ */
/* Model hyper-parameters                                            */
/* ------------------------------------------------------------------ */
const N_EMBD: usize = 32;
const N_HEAD: usize = 4;
const N_LAYER: usize = 1;

RandyMcMillan

// Dependencies used: tokio, hyper, hyper-util, http-body-util, serde, serde_json, url
use http_body_util::Full;
use hyper::body::Bytes;
use hyper::server::conn::http1;
use hyper::service::service_fn;
use hyper::{Request, Response, StatusCode};
use hyper_util::rt::TokioIo;
use std::net::SocketAddr;
use std::sync::atomic::{AtomicBool, Ordering};
use std::sync::Arc;

RandyMcMillan

use ndarray::Array1;
use rand::Rng; 
// The playground often requires RngExt for sampling distributions
use rand::distr::{Distribution, StandardUniform}; 

#[derive(Debug)]
enum Activation {
    Logistic,
    ReLU,
    Linear,

RandyMcMillan

use nalgebra::{DMatrix, DVector};
use ndarray::{Array1, Array2, Axis};

struct Simplex;

impl Simplex {
    pub fn volume_lagrange(vertices: &Array2<f64>) -> f64 {
        let (num_pts, dim) = vertices.dim();
        assert_eq!(num_pts, dim + 1);
        // Explicitly annotate Vec<f64> to satisfy the compiler

RandyMcMillan

struct Probability {
    value: f64,
}

impl Probability {
    fn new(p: f64) -> Self {
        assert!(
            (0.0..=1.0).contains(&p),
            "Probability must be between 0 and 1"
        );

RandyMcMillan

/// Exploring Landauer's Principle with Joule and BTU heat dissipation.
use std::f64::consts::LN_2;

const BOLTZMANN_CONSTANT: f64 = 1.380649e_23;
const JOULES_TO_BTU: f64 = 9.4781712e_4;

// --- 1. Logic Gate Definitions ---
#[allow(unused)]
#[derive(Debug, Clone, Copy)]
enum Gate {

RandyMcMillan

use std::rc::Rc;
use std::cell::RefCell;
use std::collections::HashMap;
use anyhow::{anyhow, Result};

/// A node in our Morse Virtual Circuit binary tree.
#[derive(Debug, Default)]
struct Node {
    value: Option<char>,
    dot: Option<Rc<RefCell<Node>>>,