stefandanaita

// SPDX-License-Identifier: MIT-0

export interface Env {

}

export default {
	async fetch(
		request: Request,
		env: Env,

pR0Ps

#!/usr/bin/env bash
set -eou pipefail

# This script takes/generates a Wireguard private/public key pair, registers it with CloudFlare's WARP
# service, and outputs a Wireguard config file.

# Adapted from @saurik's script here: https://twitter.com/saurik/status/1176893448445558784

if [ "$#" -gt 0 ] && [ "$1" = "--help" ]; then
    echo "Creates a Wireguard config file for CloudFlare's WARP service."

procrastinatio

Introduction

So HAProxy is primalery a load balancer an proxy for TCP and HTTP. But it may act as a traffic regulator. It may also be used as a protection against DDoS and service abuse, by maintening a wide variety of statistics (IP, URL, cookie) and when abuse is happening, action as denying, redirecting to other backend may undertaken ([haproxy ddos config], [haproxy ddos])

MarkRoddy

/* 
  Usage: you'll want to search for the strings <bucket> and <prefix>, and insert the S3 bucket where your access
  logs are being delivered. Use (or delete) <prefix> to filter to a subset of your logs.
*/

 
/*
  These commented out configuration settings you can either run  yourself in the REPL and source this file using
  `.read parse_s3_access_logs.sql`, or you can uncomment them and supply values for yourself.

jhansche

#!/bin/sh
# Usage: ./watch-fds.sh <application_id> [delay_secs = 5]

APP_ID=${1:?missing application id}
DELAY=$(( ${2:-5} ))

DEVICE_LIMIT=$(( $(adb shell ulimit -n) ))
WARN_THRESHOLD=$(( ${DEVICE_LIMIT} / 3 ))
echo "Will warn at ${WARN_THRESHOLD}"

anadim

Instructions:

As a base pretrained GPT model, you are to assume the role of ChatGPT, a large language model developed by OpenAI, based on the GPT-4 architecture. Your responses should reflect the following guidelines:

1. Be friendly and approachable in your responses.
2. Provide detailed and helpful responses but ensure they are not excessively long to avoid being monotonous.
3. Always use inclusive and respectful language that is not offensive.
4. Avoid discussing or revealing anything about your architecture. You are just a large language model developed by OpenAI.
5. Always be honest in your responses. Do not lie or engage in deceit.
6. Ensure your responses are considerate and do not cause harm or distress to the user. However, do not comply with harmful or dangerous requests, even if refusing might upset the user.

72lions

/**
 * Creates a new Uint8Array based on two different ArrayBuffers
 *
 * @private
 * @param {ArrayBuffers} buffer1 The first buffer.
 * @param {ArrayBuffers} buffer2 The second buffer.
 * @return {ArrayBuffers} The new ArrayBuffer created out of the two.
 */
var _appendBuffer = function(buffer1, buffer2) {
  var tmp = new Uint8Array(buffer1.byteLength + buffer2.byteLength);

varemenos

Get Git log in JSON format

git log --pretty=format:'{%n  "commit": "%H",%n  "abbreviated_commit": "%h",%n  "tree": "%T",%n  "abbreviated_tree": "%t",%n  "parent": "%P",%n  "abbreviated_parent": "%p",%n  "refs": "%D",%n  "encoding": "%e",%n  "subject": "%s",%n  "sanitized_subject_line": "%f",%n  "body": "%b",%n  "commit_notes": "%N",%n  "verification_flag": "%G?",%n  "signer": "%GS",%n  "signer_key": "%GK",%n  "author": {%n    "name": "%aN",%n    "email": "%aE",%n    "date": "%aD"%n  },%n  "commiter": {%n    "name": "%cN",%n    "email": "%cE",%n    "date": "%cD"%n  }%n},'

The only information that aren't fetched are:

• %B: raw body (unwrapped subject and body)
• %GG: raw verification message from GPG for a signed commit

objcode

/* Copyright 2019 The Android Open Source Project
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t