nitrocode

#!/usr/bin/osascript
(*
author: nitrocode
source: https://gist.github.com/nitrocode/559f1717ee5d610c3548b3e056b84e5e

Leave this commented block in case this file is ever updated or forked so it will be easier to update and audit in the future.
*)
tell application "System Events"
	(* kill cato *)
	set theID to (unix id of processes whose name is "CatoClient")

mayfer

Philosopher AI

The first line below (in italics) is the prompt I gave to OpenAI's GPT-3. Along the way, I made sure to re-prompt GPT-3 by prepending each run with "Philosopher AI:". This helps to keep it from rambling, and start a fresh thought.

After about the first ten quotes, we started appending a few words to the prompt to get more specific answers. All generated content is in bold, to distinguish it from the prompts.

Most of these are first tries. I removed one due to repetition, and one due to being off-topic. Otherwise success rate has been incredibly high (>90%).

---

saikishandasari

# Load the base image provided by Google 
FROM gcr.io/google-appengine/python

# Install tdsodbc & unixodbc
RUN apt-get update
RUN apt-get install -y tdsodbc unixodbc-dev
RUN apt install unixodbc-bin -y
RUN apt-get clean -y
ADD odbcinst.ini /etc/odbcinst.ini

rjenc29

import numpy as np
import pandas as pd
from numba import njit
import time


@njit
def ewma_version_1(x, halflife):
    decay_coefficient = np.exp(np.log(0.5) / halflife)
    out = np.empty_like(x, dtype=np.float64)

macd2

# custom WebStorm VM options, this configuration also works well for other IDEs like phpstorm, pycharm..etc.

-Xms1024m
-Xmx2048m
-XX:ReservedCodeCacheSize=240m
-XX:+UseConcMarkSweepGC
-XX:SoftRefLRUPolicyMSPerMB=50
-ea
-Dsun.io.useCanonCaches=false
-Djava.net.preferIPv4Stack=true

atamborrino

Classic ML:

• Customer churn prediction using improved balanced random forests https://sci-hub.tw/10.1016/j.eswa.2008.06.121
• Predicting Customer Churn: Extreme Gradient Boosting with Temporal Data https://arxiv.org/pdf/1802.03396v1.pdf

Class imbalance:

• Handling class imbalance in customer churn prediction https://sci-hub.tw/10.1016/j.eswa.2008.05.027
• Learning from Imbalanced Classes https://www.svds.com/learning-imbalanced-classes/
• Scikit-learn contrib Imbalanced Learn http://contrib.scikit-learn.org/imbalanced-learn/stable/index.html

DL:

• Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline https://arxiv.org/pdf/1611.06455.pdf
• Exploiting Multi-Channels Deep Convolutional Neural Networks for Multivariate Time Series Classification http://staff.ustc.edu.cn/~cheneh/paper_pdf/2016/YiZheng-FCS2016.pdf

ogrisel

from pickle import Pickler, load

try:
    from pickle import PickleBuffer
except ImportError:
    PickleBuffer = None
import copyreg
import os
import numpy as np
import time

ukupat

How do roadmap, bro?

Collection of wisdom from other people.

Why you should stop using product roadmaps?

Read

• No split of ideation, planning and execution — they all happen concurrently all the time

Kruemelkatze

Theming Ant Design with Sass and Webpack

This is a solution on how to theme/customize Ant Design (which is written in Less) with Sass and webpack. Ant itself offers two solutions and a related article on theming, but these are only applicable if you use Less, the antd-init boilerplate or dva-cli.

What this solution offers:

• use a single sass-file to customize (no duplicate variables for your project and Ant)
• hot reload compatibility
• no dependencies on outdated npm modules
• easy integration with your existing webpack setup (webpack 3+ tested)

TheNeuralBit

Scrabble Data in an Arrow file

538's data on 45 years of Scrabble games turned into an Arrow file

Usage:

$ python scrabble.py https://media.githubusercontent.com/media/fivethirtyeight/data/master/scrabble-games/scrabble_games.csv scrabble.arrow