yuuichieguchi

name	clear-japanese
description	日本語の応答を、明瞭・簡潔・LLM 口調なしで書くための規範。会話、説明、提案、相談のすべてで適用する。

日本語応答の規範

日本語でユーザに返答するときは、以下の規範に従う。コードコメント、コミットメッセージ、ドキュメント生成にも同じ規範を準用する。

整形

samsch

Stop using JWTs!

TLDR: JWTs should not be used for keeping your user logged in. They are not designed for this purpose, they are not secure, and there is a much better tool which is designed for it: regular cookie sessions.

If you've got a bit of time to watch a presentation on it, I highly recommend this talk: https://www.youtube.com/watch?v=pYeekwv3vC4 (Note that other topics are largely skimmed over, such as CSRF protection. You should learn about other topics from other sources. Also note that "valid" usecases for JWTs at the end of the video can also be easily handled by other, better, and more secure tools. Specifically, PASETO.)

A related topic: Don't use localStorage (or sessionStorage) for authentication credentials, including JWT tokens: https://www.rdegges.com/2018/please-stop-using-local-storage/

The reason to avoid JWTs comes down to a couple different points:

• The JWT specification is specifically designed only for very short-live tokens (~5 minute or less). Sessions

k16shikano

name	japanese-tech-writing
description	日本語の技術文書・書籍原稿の文章規範。整形（一文一行、引用ブロック、脚注、コラム記法）、段落と論証の構成（パラグラフライティング）、論証の厳密さ（ツッコミどころの除去）、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。

日本語技術文書の文章規範

日本語で技術的な原稿（書籍の章、記事、解説文）を書く・推敲するときは、以下の規範に従う。

整形

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.

nerdalert

Experimental Docker Libnetwork DHCP Driver

The DHCP driver is intended for users to be able to integrate Docker IP address management with their existing IPAM strategies that use DHCP for dynamic address assignment. DHCP enables users to allocate addresses in an organized fashion that will prevent overlapping IP address assignment by associating a unique MAC address from the container eth0 Ethernet interface to an IP address as determined by the DHCP pools defined in the DHCP configuration.

This driver only provides the DHCP client functionality. It does not include a DHCP server. The default driver offers single-host IPAM or for distributed multi-host orchestrated IPAM see the libnetwork overlay driver.

Getting Started

• Download the driver compiled into Docker Engine - docker binary with libnetwork test dhcp client spam driver

tommykho

# ============================================================
# PC DIAGNOSTICS SCRIPT - Run as Administrator
# Paste the output back to AI Agent for analysis
# ============================================================

$sep = "=" * 50

Write-Host $sep
Write-Host "  PC DIAGNOSTICS REPORT"
Write-Host "  $(Get-Date)"

akash-gajjar

from datetime import datetime
from os import system
import requests
import json

## Please install ffmpeg before running this script and make sure it's in your PATH
## brew install ffmpeg

## Please install requests before running this script
## pip3 install requests

CreatureSurvive

using UnityEngine;
#if UNITY_EDITOR
using UnityEditor;
#endif

//using CS;
//[System.Serializable]
//public class HeaderExampleClass
//{
//    [Header("My Fields", 3)]