duyng2512

/*
    Java implementation of Event Loop
    
    Event Loop contain:
        - List of event
        - List of handler corresponding to each event data
*/

public final class EventLoop {

byt3bl33d3r

#! /usr/bin/env python3

'''
    Needs Requests (pip3 install requests)

    Author: Marcello Salvati, Twitter: @byt3bl33d3r
    License: DWTFUWANTWTL (Do What Ever the Fuck You Want With This License)

    
    This should allow you to detect if something is potentially exploitable to the log4j 0day dropped on December 9th 2021.

eamirgh

rm -rf ~/.config/JetBrains/GoLand2021.3/eval
rm -rf ~/.config/JetBrains/GoLand2021.3/options/other.xml
touch ~/.config/JetBrains/GoLand2021.3/options/other.xml
sed -i -E 's/<property name=\"evl.*\".*\/>//' ~/.config/JetBrains/GoLand2021.3/options/other.xml
rm -rf ~/.java/.userPrefs/jetbrains/goland 

onlurking

Ruby and Rails Study Roadmap

Motivation

• Is Rails still relevant in 2018 ?

The Philosophy of

• Ruby
• Ruby on Rails

prod3v3loper

Apache Benchmark AB

Test your website and find out how it reacts if Surf 100 users at the same time and load your webpage with 10 requests each.

Information

-n requests

Number of requests to perform for the benchmarking session. The default is to just perform a single request which usually leads to non-representative benchmarking results.

rozza

        MongoClientSettings expected = MongoClientSettings.builder()
            .applyToClusterSettings(new Block<ClusterSettings.Builder>() {
            @Override
            void apply(final ClusterSettings.Builder builder) {
                builder.hosts([new ServerAddress('host1', 1), new ServerAddress('host2', 2)])
                        .mode(ClusterConnectionMode.MULTIPLE)
                        .requiredReplicaSetName('test')
                        .serverSelectionTimeout(25000, TimeUnit.MILLISECONDS)
                        .maxWaitQueueSize(10 * 7) // maxPoolSize * waitQueueMultiple
                        .localThreshold(30, TimeUnit.MILLISECONDS)

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

yoavram

import matplotlib.pyplot as plt
import numpy as np

import sklearn.datasets
import sklearn.cross_validation as cv
from sklearn import linear_model

dataset = sklearn.datasets.fetch_california_housing()
X = dataset['data']
y = dataset['target']

kastnerkyle

# Author: Kyle Kastner
# License: BSD 3-Clause
# Implementing http://mnemstudio.org/path-finding-q-learning-tutorial.htm
# Q-learning formula from http://sarvagyavaish.github.io/FlappyBirdRL/
# Visualization based on code from Gael Varoquaux gael.varoquaux@normalesup.org
# http://scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection

thomasdarimont

package demo;

import java.util.concurrent.TimeUnit;

import org.springframework.aop.TargetSource;
import org.springframework.aop.framework.ProxyFactory;
import org.springframework.aop.support.DelegatingIntroductionInterceptor;
import org.springframework.beans.factory.BeanFactory;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;