alphastrata

#include <cuda_runtime.h>
#include <stdio.h>
#include <stdlib.h>

#define NUM_ELEMENTS 150000000

// Function to generate 150 million 0.0 floats
void generateData(float **data, size_t num_elements) {
    *data = (float *)malloc(num_elements * sizeof(float));

alphastrata

1. Unstage the file:

git reset HEAD <file>

2. Remove the file from the commit history:

git filter-branch --tree-filter 'rm -f <file>' HEAD

alphastrata

1. # create new .py file with code found below
2. # install ollama
3. # install model you want “ollama run mistral”
4. conda create -n autogen python=3.11
5. conda activate autogen
6. which python
7. python -m pip install pyautogen
7. ollama run mistral
8. ollama run codellama
9. # open new terminal

alphastrata

pub mod termite {

    use std::env;

    /// Termite is a simple logging implementation, powered by the fern crate.
    #[allow(unused_imports)]
    use log::{debug, error, info, trace, warn};

    /// Initialise termite.log, after initilisation you'll find it works EXACTLY as most of the logging
    /// crates you're used to.

alphastrata

import argparse
import yfinance as yf
from tabulate import tabulate
from typing import Optional, List


def red_green(number: float) -> str:
    return "green" if number >= 0 else "red"

alphastrata

import yfinance as yf
import plotly.graph_objects as go


class Stonk:
    def __init__(self, symbol):
        self.symbol = symbol
        self.buy_price = None
        self.today_price = None
        self.historical_data = None

alphastrata

def number_to_words(number):
    # Define dictionaries for text representation
    ones = {
        0: "zero",
        1: "one",
        2: "two",
        3: "three",
        4: "four",
        5: "five",
        6: "six",

alphastrata

WGSL 3D SDF Primitives

How to use this gist:

1. Build a sphere tracer with WebGPU (paper, paper2, youtube)
2. Create model with sdf functions from here
3. Add light and shadows
4. ???
5. PROFIT

This code tested in Chrome and Firefox, should work on PC too.

alphastrata

import cv2
import pytesseract
import os
import csv
import logging
from tqdm import tqdm

# Set path to input video file
video_path = "myfile.mp4"

alphastrata

use std::marker::PhantomData;

struct Raw;
struct Batter;
struct Baking;
struct Done;

struct Cake<State> {
    state: PhantomData<State>,
}