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@rom1504
rom1504 / distributed_dalle2_laion.md
Last active September 4, 2025 21:14
distributed dalle2 laion
@pixelsnafu
pixelsnafu / CloudsResources.md
Last active March 1, 2026 16:35
Useful Resources for Rendering Volumetric Clouds

Volumetric Clouds Resources List

  1. A. Schneider, "Real-Time Volumetric Cloudscapes," in GPU Pro 7: Advanced Rendering Techniques, 2016, pp. 97-127. (Follow up presentations here, and here.)

  2. S. Hillaire, "Physically Based Sky, Atmosphere and Cloud Rendering in Frostbite" in Physically Based Shading in Theory and Practice course, SIGGRAPH 2016. [video] [course notes] [scatter integral shadertoy]

  3. [R. Högfeldt, "Convincing Cloud Rendering – An Implementation of Real-Time Dynamic Volumetric Clouds in Frostbite"](https://odr.chalmers.se/hand

@schicks
schicks / README.md
Last active September 8, 2025 13:44
Pydantic Generation Strategy

Pydantic Generation Strategy

We frequently use pydantic at Pluralsight to validate data at the edge of a well typed domain. I've been trying to sell my coworkers on using hypothesis for testing, and thought it might go easier if they could generate test data from existing pydantic schemas. I found that it was almost trivial for data classes, but BaseModel subclasses (which are unfortunately much more common in our code) don't play as nicely, and deeply nested schemas can get you into trouble. If anyone has any advice on how to get around the errors that come from the last test, I'd be super greatful.

Requirements for usage

  • pydantic
  • hypothesis
  • pytest
@raulqf
raulqf / Install_OpenCV4_CUDA12.6_CUDNN8.9.md
Last active February 25, 2026 16:48
How to install OpenCV 4.10 with CUDA 12 in Ubuntu 24.04

Install OpenCV 4.10 with CUDA 12.6 and CUDNN 8.9 in Ubuntu 24.04

First of all install update and upgrade your system:

    $ sudo apt update
    $ sudo apt upgrade

Then, install required libraries:

@ovaris
ovaris / checkFiles.ts
Last active March 17, 2020 18:09
Google cloud storage problem
import {chunk} from 'lodash';
import * as admin from "firebase-admin";
import * as storage from '@google-cloud/storage';
const gcs = storage();
//https://github.com/GoogleCloudPlatform/google-cloud-node/issues/2254
gcs.interceptors.push({
request: function(reqOpts) {
reqOpts.forever = false;
return reqOpts
# QEmu
brew install qemu
# Home for out tests
mkdir ~/arm-emu
cd ~/arm-emu
# Download initrd and kernel
wget http://ftp.de.debian.org/debian/dists/jessie/main/installer-armel/current/images/versatile/netboot/initrd.gz
@karpathy
karpathy / pg-pong.py
Created May 30, 2016 22:50
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym
# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward
{-# LANGUAGE OverloadedStrings #-}
module Main where
import Text.Parsec
import Text.Parsec.String
import Text.Parsec.Char
import Control.Monad
import Data.List
import Data.Either
@jasonrudolph
jasonrudolph / about.md
Last active November 13, 2025 16:55
Programming Achievements: How to Level Up as a Developer
#!/usr/bin/env python
"""
Usage:
./rtail.py user@host:path/foo.log bar.log host2:/path/baz.log
"""
import optparse
import os