gabrielfalcao

# Getting a random free tcp port in python using sockets

def get_free_tcp_port():
    tcp = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
    tcp.bind(('', 0))
    addr, port = tcp.getsockname()
    tcp.close()
    return port

jahe

// Enable component-scanning and auto-configuration with @SpringBootApplication Annotation
// It combines @Configuration + @ComponentScan + @EnableAutoConfiguration
@SpringBootApplication
public class FooApplication {
  public static void main(String[] args) {
    // Bootstrap the application
    SpringApplication.run(FooApplication.class, args);
  }
}

Brainiarc7

Low-Latency Live Streaming for your Desktop using ffmpeg and netcat:

Preamble:

In this post I will explore how to stream a video and audio capture from one computer to another using ffmpeg and netcat, with a latency below 100ms, which is good enough for presentations and general purpose remote display tasks on a local network.

The problem:

Streaming low-latency live content is quite hard, because most software-based video codecs are designed to achieve the best compression and not best latency. This makes sense, because most movies are encoded once and decoded often, so it is a good trade-off to use more time for the encoding than the decoding.

Arinerron

#!/bin/bash

# Give the usual warning.
clear;
echo "[INFO] Automated Android root script started.\n\n[WARN] Exploit requires sdk module \"NDK\".\nFor more information, visit the installation guide @ https://goo.gl/E2nmLF\n[INFO] Press Ctrl+C to stop the script if you need to install the NDK module. Waiting 10 seconds...";
sleep 10;
clear;

# Download and extract exploit files.
echo "[INFO] Downloading exploit files from GitHub...";

jonico

#!groovy
import groovy.json.JsonOutput
import groovy.json.JsonSlurper

def label = "mypod-${UUID.randomUUID().toString()}"
podTemplate(label: label, yaml: """
spec:
      containers:
      - name: mvn
        image: maven:3.3.9-jdk-8

oifland

// Related to https://issues.jenkins-ci.org/browse/JENKINS-26481

abcs = ['a', 'b', 'c']

node('master') {
    stage('Test 1: loop of echo statements') {
        echo_all(abcs)
    }
    
    stage('Test 2: loop of sh commands') {

endolith

"""
Train a neural network to implement the discrete Fourier transform
"""
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import numpy as np
import matplotlib.pyplot as plt

N = 32
batch = 10000

socool

// Enable component-scanning and auto-configuration with @SpringBootApplication Annotation
// It combines @Configuration + @ComponentScan + @EnableAutoConfiguration
@SpringBootApplication
public class FooApplication {
  public static void main(String[] args) {
    // Bootstrap the application
    SpringApplication.run(FooApplication.class, args);
  }
}

michaelchughes

Goal

Install working tensorflow or pytorch via standard conda environment workflow.

Basic Setup : Install pytorch in a fresh conda environment

The recommended conda-based install process works smoothly:

$ # Create a fresh environment

PARC6502

Backend as a Service

Supabase - ~52K stars

• Designed explicitly as an open source firebase alternative
• Typescript based
• Docker support

Appwrite - ~32K stars

• Written in JavaScript and PHP
• Docker based
• Realtime support across all services