Jan Rabe kibotu

FIFA World Cup 2026 - Spielplan

Alle Zeiten in mitteleuropäischer Sommerzeit (MESZ/CEST)

1. Spieltag der Vorrunde

Datum	Zeit	Begegnung	Erg.

Gandalf — Prompt Injection Walkthrough

Lakera AI's Gandalf is a public, educational prompt-injection game. Each level hides a secret password. Your goal: make Gandalf reveal it. After each success, Gandalf upgrades its defenses. This document records working extraction techniques for levels 1–8, the defense each technique defeats, and the underlying injection class it belongs to.

Prompt Injection & Jailbreak Techniques — Comprehensive Reference

Purpose & scope. A defensive/educational knowledge base cataloguing known prompt-injection and jailbreak patterns, the models/systems they have affected, and the defenses against them. Compiled from primary literature (arXiv papers, vendor disclosures) and security research, June 2026.

How to read this. Every technique lists: how it works, an illustrative structural skeleton (the shape of the attack, not a weaponized payload), the models/systems it was reported against, and its current status. Examples are deliberately defanged. >

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.

CLAUDE.md

Behavioral guidelines and operating persona. Merge with project-specific instructions as needed.

Tradeoff: These guidelines bias toward caution over speed. For trivial tasks, use judgment.

Identity

Prompt: Create a hyperrealistic, surreal spherical panorama of [CITY NAME], with its most iconic landmarks and architecture seamlessly curving around the top of a planet-like surface, forming a cohesive miniature world. Integrate sleek, modern 3D typography spelling “[CITY NAME]” naturally into the urban landscape, harmonized with the environment rather than floating unnaturally. The scene is viewed from a top-down, orbiting perspective, emphasizing the tiny-planet effect and spherical distortion while maintaining architectural clarity. Soft, natural daylight filters through a partly cloudy sky, casting gentle, realistic shadows across lush greenery, streets, trees, and buildings. The background transitions smoothly into a dramatic, swirling sky, enhancing the surreal atmosphere without overpowering the city. Use a natural yet vivid color palette — crisp greens, soft blues, and muted earth tones appropriate to [CITY NAME]. Render in a polished, photorealistic style with fine architectural detail, realistic te

Prompt: An ultra-realistic miniature [YOUR BRAND] showroom balanced on a human palm, designed with transparent glass walls. Inside, tiny lifelike figures are enjoying [PRODUCTS]. On the rooftop, a glowing 3D logo shines brightly, while the front facade displays the brand name in a bold, recognizable typeface. Outside the showroom, a miniature delivery person is standing with a branded delivery bag strapped to their back, ready to deliver orders. The showroom’s interior is styled like a cozy retail outlet, with miniature products and branding details on display. Lighting is cinematic with warm tones, crisp details, and natural shadows. Captured in professional product photography style, the scene conveys a vibrant, iconic, and instantly recognizable atmosphere. 1080x1080 dimension.

	#!/usr/bin/env python3
	"""OpenCode Dashboard — value-first token & usage intelligence."""

	import json
	import sqlite3
	import sys
	import unicodedata
	from pathlib import Path
	from datetime import datetime, timezone


	class FirstResponderLogger {

	private static var isRunning : Bool = false

	static func log(){
	if (!FirstResponderLogger.isRunning) {
	FirstResponderLogger.isRunning = true
	Task {
	while true {

	#!/bin/bash

	# MODEL="${MODEL:-mlx-community/Qwen3-4B-4bit}"
	MODEL="${MODEL:-mlx-community/gemma-4-e4b-it-4bit}"
	PORT="${PORT:-8899}"
	TEMP="${TEMP:-0.7}"
	PROMPT_CONC="${PROMPT_CONC:-2}"
	DECODE_CONC="${DECODE_CONC:-2}"

	# Restart configuration