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Cvar1984

"""theHunter: Call of the Wild - animal ESP over DMA (MemProcFS).

Run on the DMA host. Reads the game PC's memory through the FPGA card and draws every spawned
animal as a box with a snapline from the bottom of the screen and a species + distance label
(orange = wounded, gray = dead), plus a center dot crosshair, on a black window. Make the window the
same aspect ratio as the game (or --fullscreen on a same-resolution monitor / a fuser keyed on black).

    pip install memprocfs        # 64-bit Python; FPGA also needs FTD3XX.dll next to the memprocfs package
    python hunter_esp.py         # --device fpga by default, Esc to quit
    python hunter_esp.py --selftest

MangaD

The Comprehensive Guide to pre-commit

Table of Contents

1. Introduction: What is pre-commit?
2. Why Use pre-commit?
3. How pre-commit Works
4. Installing and Setting Up pre-commit
5. Configuring .pre-commit-configyaml
6. Built-in and Community Hooks

karpathy

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.

colinhacks

git config --global url."git@github.com:".insteadof "https://github.com/"

gentrexha

{
  "name": "Job Application Automation (Resume + Cover Letter + Outreach)",
  "nodes": [
    {
      "id": "05f93c2c-6956-42ed-a785-d2c2600e06bc",
      "name": "Information Extractor",
      "type": "@n8n/n8n-nodes-langchain.informationExtractor",
      "maxTries": 2,
      "position": [
        -880,

babhishek21

#!/usr/bin/env bash
# ==============================================================================
# OCI Always Free Tier Compliance Auditor
# ==============================================================================

set -eo pipefail

# ------------------------------------------------------------------------------
# Configurable Free Tier Thresholds & Shapes
# (Can be edited here or overridden via environment variables)

aamiaa

Caution

As of August 26th 2026, Discord has started suspending quest access of people caught automating quest completion.

Some users have received the following system message:

There isn't much I can do to make the script undetected, so use it at your own risk, as you WILL get flagged by doing so.

Complete Recent Discord Quest

TheGreatSageEqualToHeaven

Write-up

A warning to Roblox developers about a powerful exploit primitive. In this, I will detail the research I’ve conducted into this attack vector and walk you through how you as a developer, can protect against exploits with primitives like this.

DataStoreService lets you store data that needs to persist between sessions, such as items in a player’s inventory or skill points. Data stores are consistent per experience, so any place in an experience can access and change the same data, including places on different servers.

By default, experiences tested in Studio cannot access data stores, so you must first enable API services. You will need to do this to test the vulnerabilities.

The idea I wanted to explore when pondering the above question was; can we exploit remotes to prevent data from saving? It is easy to blame the developer for not protecting themselves against such a simple exploit but it ends up being more complicated than that. I found plenty of examples of these vulnerabilities occurring

ChristopherA

Mermaid on Github Examples

All of these diagrams are dynamically rendered during html display by Github, the images generated from text inside the Github-Flavored Markdown. None are static images. Mermaid support was released for Github on 2022-02-14

Pros & Cons:

• Pro: You don't need to care about the layout.
• Con: You cannot control the layout.

Notes:

• Not all the features of Mermaid (in particular symbols B-->C[fa:fa-ban forbidden], hyperlink and tooltips) are supported by Github.