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@sxiii
sxiii / readme.md
Created March 4, 2021 19:40
How to launch games via Proton from CLI (useful for debug)

How to launch games via Proton from CLI (useful for debug)

In just 2 steps

First. Run this:

export STEAM_COMPAT_DATA_PATH=~/.local/share/Steam/steamapps/compatdata

Second. Now in the same terminal, run your game (this example: Raft)

~/.local/share/Steam/steamapps/common/Proton\ 5.13/proton run ~/.local/share/Steam/steamapps/common/Raft/Raft.exe

@Thiez
Thiez / main.rs
Created October 26, 2017 10:40 — forked from steveklabnik/main.rs
The Results of the Expressive C++17 Coding Challenge in Rust
use std::io::{Read, Write};
use std::fs::File;
fn main() {
let args = ::std::env::args().collect::<Vec<_>>();
// skip the program name
if args.len() != 5 {
println!("Usage: {} <filename> <columnname> <replacement> <output_filename>", args[0]);
}
@pesterhazy
pesterhazy / ripgrep-in-emacs.md
Last active July 19, 2024 16:05
Using ripgrep in Emacs using helm-ag (Spacemacs)

Why

Ripgrep is a fast search tool like grep. It's mostly a drop-in replacement for ag, also know as the Silver Searcher.

helm-ag is a fantastic package for Emacs that allows you to display search results in a buffer. You can also jump to locations of matches. Despite the name, helm-ag works with ripgrep (rg) as well as with ag.

How

@KodrAus
KodrAus / Profile Rust on Linux.md
Last active August 12, 2024 12:37
Profiling Rust Applications

Profiling performance

Using perf:

$ perf record -g binary
$ perf script | stackcollapse-perf.pl | rust-unmangle | flamegraph.pl > flame.svg

NOTE: See @GabrielMajeri's comments below about the -g option.

@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
@karpathy
karpathy / min-char-rnn.py
Last active May 8, 2025 09:24
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np
# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)