Kenn Ejima kenn

name

apple-container

description

Apple Silicon Mac で動く軽量 Linux コンテナランタイム `container` (1.1.0) の利用者向けリファレンス。`container run`、`container build`、`container image pull/push`、`container machine`、ボリューム・ネットワーク・DNS・設定ファイルなど、日常的なコマンドの使い方を網羅する。`container` コマンドの使い方やトラブルシューティングに関する質問で使用する。

apple-container（`container` CLI）

Apple 製の container ツールは、Linux コンテナを 軽量 VM 1 つ = コンテナ 1 個 の方式で Mac 上に起動するランタイム。OCI 互換イメージを使うため Docker / podman で作ったイメージがそのまま動く。リポジトリは apple/container。

このスキルは container 1.1.0 用かつ利用者向け。ソースコードの修正やビルド方法ではなく、container コマンドを使う側の知識を扱う。

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.

消費税の計算

原材料: A社
製造: B社
卸: C社
小売: D社

消費税が無い状態で、小売売価100、卸値70、製造販売価50、原材料価格20とする。1個商品が売れた場合

|　|売価|仕入れ値|粗利|

	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

	// Types for the result object with discriminated union
	type Success<T> = {
	data: T;
	error: null;
	};

	type Failure<E> = {
	data: null;
	error: E;
	};

	<svg width="300" height="300" viewBox="0 0 300 300">
	<defs>
	<filter id="paperEffect">
	<feTurbulence
	type="fractalNoise"
	baseFrequency="0.04"
	numOctaves="5"
	result="noise"
	/>
	<feDiffuseLighting

	type InitFunction = (send: SendFunction) => CleanupFunction;
	type SendFunction = (event: string, data: string) => void;
	type CleanupFunction = () => void;

	export function eventStream(request: Request, init: InitFunction) {
	let stream = new ReadableStream({
	start(controller) {
	let encoder = new TextEncoder();
	let send = (event: string, data: string) => {
	controller.enqueue(encoder.encode(`event: ${event}\n`));

	<video id="player" autoplay muted playsinline> </video>
	<button id="capture">Capture</button>
	<canvas id="canvas" width=320 height=240></canvas>
	<script>
	const player = document.getElementById('player');
	const canvas = document.getElementById('canvas');
	const context = canvas.getContext('2d');
	const captureButton = document.getElementById('capture');

	const constraints = {

	(use srfi-27)
	(use srfi-42)
	(use gauche.sequence)

	(define n 10000)
	(define points (make-vector n 0))

	(define (reset init)
	(set! points (make-vector n init)))

	require 'bundler/inline'

	gemfile do
	source 'https://rubygems.org'
	gem 'colorize'
	end

	class MatcherInterface
	def initialize(some_object)
	@some_object = some_object

Kenn Ejima kenn

apple-container（container CLI）

LLM Wiki

The core idea

消費税の計算

apple-container（`container` CLI）