Cactus erbanku

import sys
import os

NOP = 0x90
offsets_and_values = {
    0x00030170: 0x00,
    0x000A94D0: NOP, 0x000A94D1: NOP, 0x000A94D2: NOP, 0x000A94D3: NOP, 0x000A94D4: NOP, 0x000A94D5: NOP, 0x000A94D6: NOP, 0x000A94D7: NOP, 0x000A94D8: NOP, 0x000A94D9: NOP, 0x000A94DA: NOP, 0x000A94DB: NOP, 0x000A94DC: NOP, 0x000A94DD: NOP, 0x000A94DE: NOP, 0x000A94DF: NOP, 0x000A94E0: NOP, 0x000A94E1: NOP, 0x000A94E2: NOP, 0x000A94E3: NOP, 0x000A94E4: NOP, 0x000A94E5: NOP, 0x000A94E6: NOP, 0x000A94E7: NOP, 0x000A94E8: NOP, 0x000A94E9: NOP, 0x000A94EA: NOP, 0x000A94EB: NOP, 0x000A94EC: NOP, 0x000A94ED: NOP, 0x000A94EE: NOP, 0x000A94EF: NOP, 0x000A94F0: NOP, 0x000A94F1: NOP, 0x000A94F2: NOP, 0x000A94F3: NOP, 0x000A94F4: NOP, 0x000A94F5: NOP, 0x000A94F6: NOP, 0x000A94F7: NOP, 0x000A94F8: NOP, 0x000A94F9: NOP, 0x000A94FA: NOP, 0x000A94FB: NOP, 0x000A94FC: NOP, 0x000A94FD: NOP, 0x000A94FE: NOP, 0x000A94FF: NOP, 0x000A9500: NOP, 0x000A9501: NOP, 0x000A9502: NOP, 0x000A9503: NOP, 0x000A9504: NOP, 0x000A9505: NOP, 0x000A9506: NO

Codex /goal Implementation

How OpenAI's Codex CLI implements the /goal slash command for persisted long-running task objectives.

The /goal command sets a persisted objective for a long-running task. It's a five-layer system: a SQLite table stores one goal per thread with status (active/paused/budget_limited/complete), optional token budget, and running usage counters. The app-server exposes thread/goal/set/get/clear JSON-RPC methods. The model sees three tools — create_goal, update_goal(complete), and get_goal — but can't pause/resume; those are system-controlled. A runtime event bus hooks into turn lifecycle to track token + wall-clock deltas for accounting, auto-pauses on interrupt, auto-reactivates paused goals on thread resume, and injects budget-limit steering into the model's response stream. The TUI handles the /goal slash command and displays goal state in the status bar.

Author: etraut-openai | 5 PRs, ~15K additions, landed in ~10 days (Apr 16–25, 2026)

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.

export-dify.ai-workflows.js

Export all Dify workflows/apps as YAML and download them as a ZIP.

Run

Open Dify Console (dify.ai) → DevTools → Console and run full script export-dify.ai-workflows.js

Output

Saved Tab Groups Cleanup Tool

This repository includes a local cleanup tool for removing Chrome saved tab groups directly from the profile sync database.

Use it only when Chrome is fully closed.

What It Does

The tool deletes LevelDB keys with the prefix:

	/**
	* Cloudflare Worker Telegram Bot
	* Logic: Extracts metadata, handles /setup for menu, and provides individually
	* copiable monospace fields with a full JSON dump.
	*/

	export default {
	async fetch(request, env) {
	const url = new URL(request.url);

	(() => {
	const LOG = '[Dify Batch Delete]';

	const log = (...args) => console.log(LOG, ...args);
	const err = (...args) => console.error(LOG, ...args);

	const escapeRegExp = (s) => s.replace(/[.*+?^${}()\|[\]\\]/g, '\\$&');

	const getCookie = (name) => {
	const match = document.cookie.match(

	#Requires AutoHotkey v2.0
	#SingleInstance Force

	; Variable to store the last clipboard content
	lastClipboard := ""

	; Set up a timer to monitor the clipboard every 500ms
	SetTimer(CheckClipboardAndWindow, 500)

	CheckClipboardAndWindow() {

	<#
	.SYNOPSIS
	Resolves many Windows GUIDs to human friendly values.

	.DESCRIPTION
	Resolve-WindowsGUID.ps1 takes a GUID from a Windows system and attempts
	to return a human friendly value from either a static list or from a
	dynamically generated list of LogProvider GUIDs. There are undoubtedly
	other GUIDs in use throughout Windows that will not fall into either of
	these sets. If you encounter a GUID that you can't resolve via this

	import json
	import os
	import re
	import argparse
	from datetime import datetime


	def parse_time(ts: str):
	try:
	return datetime.fromisoformat(ts.replace("Z", "+00:00"))