dhh

# THIS LINUX SETUP SCRIPT HAS MORPHED INTO A WHOLE PROJECT: HTTPS://OMAKUB.ORG
# PLEASE CHECKOUT THAT PROJECT INSTEAD OF THIS OUTDATED SETUP SCRIPT.
#
#

# Libraries and infrastructure
sudo apt update -y
sudo apt install -y \
	docker.io docker-buildx \
	build-essential pkg-config autoconf bison rustc cargo clang \

huytd

How to use:

./wordle.sh

Or try the unlimit mode:

skeeto

Moved to skeeto/scratch/aidrivers

riveSunder

"""
Simple MLP demo using [autograd](https://github.com/HIPS/autograd)
With l1 and l2 regularization. 

Depends on autograd and scikit-learn (the latter for the mini digits dataset)

pip install autograd scikit-learn
"""

from autograd import numpy as np

IanColdwater

Mute these words in your settings here: https://twitter.com/settings/muted_keywords

ActivityTweet
generic_activity_highlights
generic_activity_momentsbreaking
RankedOrganicTweet
suggest_activity
suggest_activity_feed
suggest_activity_highlights
suggest_activity_tweet

Harry-Harrison

// This prints a list of buttons that on tap will fire a different type of haptic vibration

import SwiftUI

struct ContentView: View {
    let generator = UINotificationFeedbackGenerator()
    
    var body: some View {
        VStack(alignment: .center, spacing: 30.0) {
            Button(action: {

bmjstau

function [rgb] = wesAnderson(N, which)
% wesAnderson returns N colors taken from Wes Anderson's films, which
% can be used as color maps for hipster line plots. Colors have been 
% adapted from https://github.com/karthik/wesanderson, who has them from 
% http://wesandersonpalettes.tumblr.com/.
% 
% Inputs:
% INT N         Number of colors. Is limited by color palette.
% CHAR which    Name of color palette. See 
%               https://github.com/karthik/wesanderson for previews.

konrad

#!/usr/bin/env bash

# Returns a BibTeX (www.bibtex.org) entry for one or more given DOIs
# (https://www.doi.org/).
#
# Call it like this:
#
# $ doi2bibtex.sh 10.1093/bioinformatics/btu533
#
# Can also be used for several DOIs at once:

giuseppebonaccorso

import matplotlib.pyplot as plt
import numpy as np

from scipy.integrate import odeint

# Set random seed (for reproducibility)
np.random.seed(1000)

# Start and end time (in milliseconds)
tmin = 0.0

nitely

# LICENSE: MIT

import collections


RIGHT, LEFT = range(2)

Op = collections.namedtuple('Op', [
    'precedence',
    'associativity'])