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Daniel Salvadori danaugrs

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@lavalamp
lavalamp / The Three Go Landmines.markdown
Last active December 27, 2025 05:41
Golang landmines

There are three easy to make mistakes in go. I present them here in the way they are often found in the wild, not in the way that is easiest to understand.

All three of these mistakes have been made in Kubernetes code, getting past code review at least once each that I know of.

  1. Loop variables are scoped outside the loop.

What do these lines do? Make predictions and then scroll down.

func print(pi *int) { fmt.Println(*pi) }
@karpathy
karpathy / min-char-rnn.py
Last active April 4, 2026 16:39
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)
@jnordwick
jnordwick / avg.rs
Created July 30, 2015 22:13
count, sum, and avg macros in rust
macro_rules! avg {
($($t:expr),*) => (sum!($($t),*)/count!($($t),*));
}
macro_rules! count {
($h:expr) => (1);
($h:expr, $($t:expr),*) =>
(1 + count!($($t),*));
}
@baraldilorenzo
baraldilorenzo / readme.md
Last active September 13, 2025 12:17
VGG-16 pre-trained model for Keras

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

@Avaq
Avaq / combinators.js
Last active November 25, 2025 09:37
Common combinators in JavaScript
const I = x => x
const K = x => y => x
const A = f => x => f (x)
const T = x => f => f (x)
const W = f => x => f (x) (x)
const C = f => y => x => f (x) (y)
const B = f => g => x => f (g (x))
const S = f => g => x => f (x) (g (x))
const S_ = f => g => x => f (g (x)) (x)
const S2 = f => g => h => x => f (g (x)) (h (x))
@alirobe
alirobe / reclaimWindows10.ps1
Last active March 26, 2026 20:53
This Windows 10 Setup Script turns off a bunch of unnecessary Windows 10 telemetery, bloatware, & privacy things. Not guaranteed to catch everything. Review and tweak before running. Reboot after running. Scripts for reversing are included and commented. Fork of https://github.com/Disassembler0/Win10-Initial-Setup-Script (different defaults). N.…
###
###
### UPDATE: For Win 11, I recommend using this tool in place of this script:
### https://christitus.com/windows-tool/
### https://github.com/ChrisTitusTech/winutil
### https://www.youtube.com/watch?v=6UQZ5oQg8XA
### iwr -useb https://christitus.com/win | iex
###
### OR take a look at
### https://github.com/HotCakeX/Harden-Windows-Security
@longtimeago
longtimeago / squash-commits.md
Last active April 1, 2024 20:44
How to squash commits in a GitHub pull request

How to squash commits in a GitHub pull request

o you've contributed some code to an open source project, say, Rails. And they'd like you to squash all of the commits in your pull request. But you're not a git wizard; how do you make this happen?

Normally, you'd do something like this. I'm assuming upstream is a git remote that is pointing at the official project repository, and that your changes are in your 'omgpull' branch:

@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
@yurivish
yurivish / venn-diagrams.js
Last active December 26, 2020 03:20
Area-proportional Venn Diagrams
// Since `overlapArea` function is monotonic increasing, we can perform a
// simple bisection search to find the distance that leads to an overlap
// area within epsilon of the desired overlap.
function distanceForOverlapArea(r1, r2, desiredOverlap) {
// Ensure r1 <= r2
if (r1 > r2) {
var temp = r2;
r2 = r1;
r1 = temp;
}
@postpostscript
postpostscript / replify
Last active November 7, 2024 02:47
replify - Create a REPL for any command
#!/bin/sh
command="${*}"
printf "Initialized REPL for `%s`\n" "$command"
printf "%s> " "$command"
read -r input
while [ "$input" != "" ];
do
eval "$command $input"
printf "%s> " "$command"