I’m looking for any tips or tricks for making chrome headless mode less detectable. Here is what I’ve done so far:
Set my args as follows:
const run = (async () => {
const args = [
'--no-sandbox',
'--disable-setuid-sandbox',
'--disable-infobars',
The following are examples of the four types rate limiters discussed in the accompanying blog post. In the examples below I've used pseudocode-like Ruby, so if you're unfamiliar with Ruby you should be able to easily translate this approach to other languages. Complete examples in Ruby are also provided later in this gist.
In most cases you'll want all these examples to be classes, but I've used simple functions here to keep the code samples brief.
This uses a basic token bucket algorithm and relies on the fact that Redis scripts execute atomically. No other operations can run between fetching the count and writing the new count.
The following commands will start a container with Kafka and Zookeeper running on mapped ports 2181 (Zookeeper) and 9092 (Kafka).
docker pull spotify/kafka
docker run -d -p 2181:2181 -p 9092:9092 --env ADVERTISED_HOST=kafka --env ADVERTISED_PORT=9092 --name kafka spotify/kafka
ADVERTISTED_HOST was set to kafka, which will allow other containers to be able to run Producers and Consumers.
| // example.h | |
| DECLARE_DYNAMIC_DELEGATE_TwoParams(FBindAllKeysDelegate, const FKey&, Key, bool, bKeyPressed); | |
| // example.cpp | |
| void UExampleStatics::BindAllKeyInputActions(AActor *Actor, const FBindAllKeysDelegate& Delegate) | |
| { | |
| // Get input component from actor | |
| UInputComponent *InputComponent = Actor->InputComponent; | |
| if (InputComponent == nullptr) return; |
| #!/bin/sh | |
| ### BEGIN INIT INFO | |
| # Provides: TeamCity Build Agent | |
| # Required-Start: $remote_fs $syslog | |
| # Required-Stop: $remote_fs $syslog | |
| # Default-Start: 2 3 4 5 | |
| # Default-Stop: 0 1 6 | |
| # Short-Description: Start build agent daemon at boot time | |
| # Description: Enable service provided by daemon. | |
| ### END INIT INFO |
| /* Code snippets from Unite 2015 - A coder's guide to spline-based procedural geometry */ | |
| /* https://www.youtube.com/watch?v=o9RK6O2kOKo */ | |
| // Optimized GetPoint | |
| Vector3 GetPoint( Vector3[] pts, float t ) { | |
| float omt = 1f-t; | |
| float omt2 = omt * omt; | |
| float t2 = t * t; | |
| return pts[0] * ( omt2 * omt ) + | |
| pts[1] * ( 3f * omt2 * t ) + |
##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
| """ | |
| 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) |
| Spring STOMP chat |
