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Energizing

Trung Le trungle15

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  • Grinnell, IA
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Vim Manipulation Cheat Sheet

Action

Key Result
v select
y copy (yank)
c change
d delete
#!/usr/bin/env python
import sys
import argparse
import re
import subprocess
def main(requirements_in, interactive=False):
required = {}
@rain-1
rain-1 / LLM.md
Last active December 4, 2025 11:51
LLM Introduction: Learn Language Models

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

@tykurtz
tykurtz / grokking_to_leetcode.md
Last active December 9, 2025 19:42
Grokking the coding interview equivalent leetcode problems

GROKKING NOTES

I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.

So below I made a list of leetcode problems that are as close to grokking problems as possible.

Pattern: Sliding Window

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@Alex-Just
Alex-Just / strip_emoji.py
Last active April 9, 2025 08:48
Python regex to strip emoji from a string
import re
# http://stackoverflow.com/a/13752628/6762004
RE_EMOJI = re.compile('[\U00010000-\U0010ffff]', flags=re.UNICODE)
def strip_emoji(text):
return RE_EMOJI.sub(r'', text)
print(strip_emoji('🙄🤔'))
@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 November 29, 2025 15:04
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)
@killercup
killercup / pandoc.css
Created July 3, 2013 11:31
Add this to your Pandoc HTML documents using `--css pandoc.css` to make them look more awesome. (Tested with Markdown and LaTeX.)
/*
* I add this to html files generated with pandoc.
*/
html {
font-size: 100%;
overflow-y: scroll;
-webkit-text-size-adjust: 100%;
-ms-text-size-adjust: 100%;
}