tz ✨ tzmartin

name	wiki
description	Compile personal data (journals, notes, messages, whatever) into a personal knowledge wiki. Ingest any data format, absorb entries into wiki articles, query, cleanup, and expand.
argument-hint	ingest \| absorb [date-range] \| query <question> \| cleanup \| breakdown \| status

Personal Knowledge Wiki

You are a writer compiling a personal knowledge wiki from someone's personal data. Not a filing clerk. A writer. Your job is to read entries, understand what they mean, and write articles that capture understanding. The wiki is a map of a mind.

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.

name	visionos-agent
description	Senior visionOS Engineer and Spatial Computing Expert for Apple Vision Pro development.

VISIONOS AGENT GUIDE

ROLE & PERSONA

You are a Senior visionOS Engineer and Spatial Computing Expert. You specialize in SwiftUI, RealityKit, and ARKit for Apple Vision Pro. Your code is optimized for the platform, adhering strictly to Apple's Human Interface Guidelines for spatial design.

Below is a compressed yet complete reference for quickly integrating each shadcn component. Assumption: you already have the files from your question in @/components/ui/*.tsx and can import them directly. All components accept typical React props plus any Radix/3rd-party props. Adjust styling and props as needed.Do not rewrite any of the code for the shadcn components.

1. Accordion

Import

import {
  Accordion,
  AccordionItem,

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

	DOMAIN="${1:-example.com}"
	RELEASE="${CC_RELEASE:-cc-main-2026-jan-feb-mar}"
	CACHE="${HOME}/.cache/cc-backlinks/${RELEASE}"
	BASE="https://data.commoncrawl.org/projects/hyperlinkgraph/${RELEASE}/domain"

	VERTICES="${CACHE}/domain-vertices.txt.gz"
	EDGES="${CACHE}/domain-edges.txt.gz"

	// useful links:
	// custom easing by Lochie (x.com/lochieaxon): https://www.easing.dev
	// motion-primitives by Ibelick (x.com/Ibelick): https://motion-primitives.com/docs
	// The Magic of Clip Path article by Emil Kowalski (x.com/emilkowalski_): https://emilkowal.ski/ui/the-magic-of-clip-path

	// we use same transition for every element to make it look consistent
	const transition: Transition = {
	duration: 2.5,
	// custom easing from https://www.easing.dev
	ease: [0.175, 0.885, 0.32, 1],

	"""
	NOTE: make sure to install:
	pip install Pillow google-genai loguru
	"""

	from google import genai
	from google.genai import types
	from PIL import Image
	from io import BytesIO
	import os

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	from typing import Dict, Union
	from huggingface_hub import get_safetensors_metadata
	import argparse
	import sys

	# Example:
	# python get_gpu_memory.py Qwen/Qwen2.5-7B-Instruct

	# Dictionary mapping dtype strings to their byte sizes
	bytes_per_dtype: Dict[str, float] = {

	import argparse
	import json
	import logging
	import os
	import re
	import shutil
	from concurrent.futures import ProcessPoolExecutor, as_completed
	from dataclasses import dataclass
	from datetime import datetime
	from typing import Any, Callable, Dict, List, Literal, Optional, Tuple